Use SPR data with adjusted scores

folding_studio_demo/app.py

@@ -39,8 +39,8 @@ MOLECULE_REPS = [
 
 
 MODEL_CHOICES = [
-    # ("AlphaFold2", FoldingModel.AF2),
-    # ("OpenFold", FoldingModel.OPENFOLD),
+    ("AlphaFold2", FoldingModel.AF2),
+    ("OpenFold", FoldingModel.OPENFOLD),
     # ("SoloSeq", FoldingModel.SOLOSEQ),
     ("Boltz-1", FoldingModel.BOLTZ),
     ("Chai-1", FoldingModel.CHAI),
@@ -49,6 +49,15 @@ MODEL_CHOICES = [
 
 DEFAULT_SEQ = "MALWMRLLPLLALLALWGPDPAAA"
 MODEL_EXAMPLES = {
+    FoldingModel.AF2: [
+        ["Monomer", f">A\n{DEFAULT_SEQ}"],
+        ["Multimer", f">A\n{DEFAULT_SEQ}\n>B\n{DEFAULT_SEQ}"],
+    ],
+    FoldingModel.OPENFOLD: [
+        ["Monomer", f">A\n{DEFAULT_SEQ}"],
+        ["Multimer", f">A\n{DEFAULT_SEQ}\n>B\n{DEFAULT_SEQ}"],
+    ],
+    FoldingModel.SOLOSEQ: [["Monomer", f">A\n{DEFAULT_SEQ}"]],
     FoldingModel.BOLTZ: [
         ["Monomer", f">A|protein\n{DEFAULT_SEQ}"],
         ["Multimer", f">A|protein\n{DEFAULT_SEQ}\n>B|protein\n{DEFAULT_SEQ}"],
@@ -70,27 +79,31 @@ def sequence_input(dropdown: gr.Dropdown | None = None) -> gr.Textbox:
     Returns:
         gr.Textbox: Sequence input component
     """
-    sequence = gr.Textbox(
-        label="Protein Sequence",
-        lines=2,
-        placeholder="Enter a protein sequence or upload a FASTA file",
-    )
-    dummy = gr.Textbox(label="Complex type", visible=False)
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            sequence = gr.Textbox(
+                label="Protein Sequence",
+                lines=2,
+                placeholder="Enter a protein sequence or upload a FASTA file",
+            )
+            dummy = gr.Textbox(label="Complex type", visible=False)
+
+            examples = gr.Examples(
+                examples=MODEL_EXAMPLES[FoldingModel.BOLTZ],
+                inputs=[dummy, sequence],
+            )
+        file_input = gr.File(
+            label="Upload a FASTA file",
+            file_types=[".fasta", ".fa"],
+            scale=0,
+        )
 
-    examples = gr.Examples(
-        examples=MODEL_EXAMPLES[FoldingModel.BOLTZ],
-        inputs=[dummy, sequence],
-    )
     if dropdown is not None:
         dropdown.change(
             fn=lambda x: gr.Dataset(samples=MODEL_EXAMPLES[x]),
             inputs=[dropdown],
             outputs=[examples.dataset],
         )
-    file_input = gr.File(
-        label="Upload a FASTA file",
-        file_types=[".fasta", ".fa"],
-    )
 
     def _process_file(file: gr.File | None) -> gr.Textbox:
         if file is None:
@@ -115,7 +128,7 @@ def simple_prediction(api_key: str) -> None:
     """
     gr.Markdown(
         """
-        ### Predict a Protein Structure
+        ## Predict a Protein Structure
 
         It will be run in the background and the results will be displayed in the output section.
         The output will contain the protein structure and the pLDDT plot.
@@ -157,7 +170,19 @@ def model_comparison(api_key: str) -> None:
     Args:
         api_key (str): Folding Studio API key
     """
+    gr.Markdown(
+        """
+        ## Compare Folding Models
 
+        Select multiple models to compare their predictions on your protein sequence.
+        You can either enter the sequence directly or upload a FASTA file.
+
+        The selected models will run in parallel and generate:
+        - 3D structures of your protein that you can visualize and compare
+        - pLDDT confidence scores plotted for each residue
+        
+        """
+    )
     with gr.Row():
         models = gr.CheckboxGroup(
             label="Model",
@@ -176,6 +201,9 @@ def model_comparison(api_key: str) -> None:
         variant="primary",
     )
     with gr.Row():
+        af2_predictions = gr.CheckboxGroup(label="AlphaFold2", visible=False)
+        openfold_predictions = gr.CheckboxGroup(label="OpenFold", visible=False)
+        solo_predictions = gr.CheckboxGroup(label="SoloSeq", visible=False)
         chai_predictions = gr.CheckboxGroup(label="Chai", visible=False)
         protenix_predictions = gr.CheckboxGroup(label="Protenix", visible=False)
         boltz_predictions = gr.CheckboxGroup(label="Boltz", visible=False)
@@ -186,28 +214,50 @@ def model_comparison(api_key: str) -> None:
         metrics_plot = gr.Plot(label="pLDDT")
 
     # Store the initial predictions
-    aligned_paths = gr.State()
-    plddt_fig = gr.State()
+    prediction_outputs = gr.State()
 
     predict_btn.click(
         fn=predict_comparison,
         inputs=[sequence, api_key, models],
         outputs=[
+            prediction_outputs,
+            af2_predictions,
+            openfold_predictions,
+            solo_predictions,
             chai_predictions,
             boltz_predictions,
             protenix_predictions,
-            aligned_paths,
-            plddt_fig,
         ],
+    ).then(
+        fn=filter_predictions,
+        inputs=[
+            prediction_outputs,
+            af2_predictions,
+            openfold_predictions,
+            solo_predictions,
+            chai_predictions,
+            boltz_predictions,
+            protenix_predictions,
+        ],
+        outputs=[mol_outputs, metrics_plot],
     )
 
     # Handle checkbox changes
-    for checkbox in [chai_predictions, boltz_predictions, protenix_predictions]:
+    for checkbox in [
+        af2_predictions,
+        openfold_predictions,
+        solo_predictions,
+        chai_predictions,
+        boltz_predictions,
+        protenix_predictions,
+    ]:
         checkbox.change(
             fn=filter_predictions,
             inputs=[
-                aligned_paths,
-                plddt_fig,
+                prediction_outputs,
+                af2_predictions,
+                openfold_predictions,
+                solo_predictions,
                 chai_predictions,
                 boltz_predictions,
                 protenix_predictions,
@@ -242,99 +292,114 @@ def create_correlation_tab():
         "antigen_sequence": "Antigen Sequence",
     }
     spr_data_with_scores = spr_data_with_scores.rename(columns=prettified_columns)
-    with gr.Row():
-        columns = [
-            "Antibody Name",
-            "KD (nM)",
-            "Antibody VH Sequence",
-            "Antibody VL Sequence",
-            "Antigen Sequence",
-        ]
-        # Display dataframe with floating point values rounded to 2 decimal places
-        spr_data = gr.DataFrame(
-            value=spr_data_with_scores[columns].round(2),
-            label="Experimental Antibody-Antigen Binding Affinity Data",
-        )
+    columns = [
+        "Antibody Name",
+        "KD (nM)",
+        "Antibody VH Sequence",
+        "Antibody VL Sequence",
+        "Antigen Sequence",
+    ]
+    # Display dataframe with floating point values rounded to 2 decimal places
+    spr_data = gr.DataFrame(
+        value=spr_data_with_scores[columns].round(2),
+        label="Experimental Antibody-Antigen Binding Affinity Data",
+    )
 
     gr.Markdown("# Prediction and correlation")
-    with gr.Row():
-        fake_predict_btn = gr.Button(
-            "Predict structures of all complexes",
-            elem_classes="gradient-button",
-            variant="primary",
-        )
-    with gr.Row():
-        prediction_dataframe = gr.Dataframe(label="Predicted Structures Data")
-    with gr.Row():
-        with gr.Row():
-            correlation_type = gr.Radio(
-                choices=["Spearman", "Pearson", "R²"],
-                value="Spearman",
-                label="Correlation Type",
-                interactive=True,
-            )
-        with gr.Row():
-            correlation_ranking_plot = gr.Plot(label="Correlation ranking")
-    with gr.Row():
-        with gr.Column():
+
+    fake_predict_btn = gr.Button(
+        "Predict structures of all complexes",
+        elem_classes="gradient-button",
+        variant="primary",
+    )
+    prediction_dataframe = gr.Dataframe(
+        label="Predicted Structures Data", visible=False
+    )
+    prediction_dataframe.change(
+        fn=lambda x: gr.Dataframe(x, visible=True),
+        inputs=[prediction_dataframe],
+        outputs=[prediction_dataframe],
+    )
+    with gr.Row(visible=False) as correlation_row:
+        with gr.Column(scale=0):
             with gr.Row():
-                # User can select the columns to display in the correlation plot
-                correlation_column = gr.Dropdown(
-                    label="Score data to display",
-                    choices=SCORE_COLUMNS,
-                    multiselect=False,
-                    value=SCORE_COLUMNS[0],
+                correlation_type = gr.Radio(
+                    choices=["Spearman", "Pearson"],
+                    value="Spearman",
+                    label="Correlation Type",
+                    interactive=True,
+                    min_width=150,
                 )
-                # Add checkbox for log scale and update plot when either input changes
             with gr.Row():
                 log_scale = gr.Checkbox(
-                    label="Display x-axis on logarithmic scale", value=False
-                )
-            with gr.Row():
-                score_description = gr.Markdown(
-                    get_score_description(correlation_column.value)
-                )
-                correlation_column.change(
-                    fn=lambda x: get_score_description(x),
-                    inputs=correlation_column,
-                    outputs=score_description,
+                    label="Use log scale for KD",
+                    value=False,
+                    min_width=150,
                 )
+        with gr.Column():
+            correlation_ranking_plot = gr.Plot(label="Correlation ranking")
+    with gr.Row(visible=False) as regression_row:
+        with gr.Column(scale=0):
+            
+            # User can select the columns to display in the correlation plot
+            correlation_column = gr.Dropdown(
+                label="Score data to display",
+                choices=SCORE_COLUMNS,
+                multiselect=False,
+                value=SCORE_COLUMNS[0],
+            )
+            score_description = gr.Markdown(
+                get_score_description(correlation_column.value)
+            )
+            correlation_column.change(
+                fn=lambda x: get_score_description(x),
+                inputs=correlation_column,
+                outputs=score_description,
+            )
         with gr.Column():
             regression_plot = gr.Plot(label="Correlation with binding affinity")
 
     fake_predict_btn.click(
-        fn=lambda x: fake_predict_and_correlate(
-            spr_data_with_scores, SCORE_COLUMNS, ["Antibody Name", "KD (nM)"]
+        fn=lambda x: (
+            *fake_predict_and_correlate(
+                spr_data_with_scores, SCORE_COLUMNS, ["Antibody Name", "KD (nM)"]
+            ),
+            gr.Row(visible=True),
+            gr.Row(visible=True)
         ),
         inputs=[correlation_type],
-        outputs=[prediction_dataframe, correlation_ranking_plot, regression_plot],
+        outputs=[
+            prediction_dataframe,
+            correlation_ranking_plot,
+            regression_plot,
+            correlation_row,
+            regression_row,
+        ],
     )
 
-    def update_regression_plot(score, use_log):
-        return make_regression_plot(spr_data_with_scores, score, use_log)
-
-    def update_correlation_plot(correlation_type):
+    def update_plots_with_log(correlation_type, score, use_log):
         logger.info(f"Updating correlation plot for {correlation_type}")
         corr_data = compute_correlation_data(spr_data_with_scores, SCORE_COLUMNS)
         logger.info(f"Correlation data: {corr_data}")
-        return plot_correlation_ranking(corr_data, correlation_type)
+        corr_ranking_plot = plot_correlation_ranking(corr_data, correlation_type, kd_col="KD (nM)" if not use_log else "log_kd")
+        regression_plot = make_regression_plot(spr_data_with_scores, score, use_log)
+        return regression_plot, corr_ranking_plot
 
     correlation_column.change(
-        fn=update_regression_plot,
-        inputs=[correlation_column, log_scale],
-        outputs=regression_plot,
+        fn=update_plots_with_log,
+        inputs=[correlation_type, correlation_column, log_scale],
+        outputs=[regression_plot, correlation_ranking_plot],
     )
 
     correlation_type.change(
-        fn=update_correlation_plot,
-        inputs=[correlation_type],
-        outputs=correlation_ranking_plot,
+        fn=update_plots_with_log,
+        inputs=[correlation_type, correlation_column, log_scale],
+        outputs=[regression_plot, correlation_ranking_plot],
     )
-
     log_scale.change(
-        fn=update_regression_plot,
-        inputs=[correlation_column, log_scale], 
-        outputs=regression_plot,
+        fn=update_plots_with_log,
+        inputs=[correlation_type, correlation_column, log_scale],
+        outputs=[regression_plot, correlation_ranking_plot],
     )
 
 
@@ -360,7 +425,7 @@ def __main__():
         )
         api_key = gr.Textbox(label="Folding Studio API Key", type="password")
         gr.Markdown("## Demo Usage")
-        with gr.Tab("🚀 Simple Prediction"):
+        with gr.Tab("🚀 Basic Folding"):
             simple_prediction(api_key)
         with gr.Tab("📊 Model Comparison"):
             model_comparison(api_key)

folding_studio_demo/correlate.py

@@ -1,9 +1,10 @@
 import logging
-import pandas as pd
 from pathlib import Path
+
 import numpy as np
+import pandas as pd
 import plotly.graph_objects as go
-from scipy.stats import spearmanr, pearsonr, linregress
+from scipy.stats import linregress, pearsonr, spearmanr
 
 logger = logging.getLogger(__name__)
 
@@ -16,7 +17,7 @@ SCORE_COLUMN_NAMES = {
     "complex_pde_boltz": "Boltz Complex pDE",
     "complex_ipde_boltz": "Boltz Complex ipDE",
     "interchain_pae_monomer": "AlphaFold2 GapTrick Interchain PAE",
-    "interface_pae_monomer": "AlphaFold2 GapTrick Interface PAE", 
+    "interface_pae_monomer": "AlphaFold2 GapTrick Interface PAE",
     "overall_pae_monomer": "AlphaFold2 GapTrick Overall PAE",
     "interface_plddt_monomer": "AlphaFold2 GapTrick Interface pLDDT",
     "average_plddt_monomer": "AlphaFold2 GapTrick Average pLDDT",
@@ -24,15 +25,16 @@ SCORE_COLUMN_NAMES = {
     "interface_ptm_monomer": "AlphaFold2 GapTrick Interface pTM",
     "interchain_pae_multimer": "AlphaFold2 Multimer Interchain PAE",
     "interface_pae_multimer": "AlphaFold2 Multimer Interface PAE",
-    "overall_pae_multimer": "AlphaFold2 Multimer Overall PAE", 
+    "overall_pae_multimer": "AlphaFold2 Multimer Overall PAE",
     "interface_plddt_multimer": "AlphaFold2 Multimer Interface pLDDT",
     "average_plddt_multimer": "AlphaFold2 Multimer Average pLDDT",
     "ptm_multimer": "AlphaFold2 Multimer pTM Score",
-    "interface_ptm_multimer": "AlphaFold2 Multimer Interface pTM"
+    "interface_ptm_multimer": "AlphaFold2 Multimer Interface pTM",
 }
 
 SCORE_COLUMNS = list(SCORE_COLUMN_NAMES.values())
 
+
 def get_score_description(score: str) -> str:
     descriptions = {
         "Boltz Confidence Score": "The Boltz model confidence score provides an overall assessment of prediction quality (0-1, higher is better).",
@@ -49,80 +51,99 @@ def get_score_description(score: str) -> str:
         "AlphaFold2 GapTrick Average pLDDT": "The AlphaFold2 GapTrick model average pLDDT provides the mean confidence across all residues in monomeric predictions (0-100, higher is better).",
         "AlphaFold2 GapTrick pTM Score": "The AlphaFold2 GapTrick model pTM score assesses overall fold accuracy in monomeric predictions (0-1, higher is better).",
         "AlphaFold2 GapTrick Interface pTM": "The AlphaFold2 GapTrick model interface pTM specifically evaluates accuracy of interface regions in monomeric predictions (0-1, higher is better).",
-        "AlphaFold2 GapTrick Interchain PAE": "The AlphaFold2 GapTrick model interchain PAE estimates position errors between chains in multimeric predictions (lower is better).",
-        "AlphaFold2 Multimer Interface PAE": "The AlphaFold2 Multimer model interface PAE estimates position errors specifically at interfaces in multimeric predictions (lower is better).", 
+        "AlphaFold2 Multimer Interface PAE": "The AlphaFold2 Multimer model interface PAE estimates position errors specifically at interfaces in multimeric predictions (lower is better).",
         "AlphaFold2 Multimer Overall PAE": "The AlphaFold2 Multimer model overall PAE estimates position errors across the entire structure in multimeric predictions (lower is better).",
         "AlphaFold2 Multimer Interface pLDDT": "The AlphaFold2 Multimer model interface pLDDT measures confidence in interface region predictions for multimeric models (0-100, higher is better).",
         "AlphaFold2 Multimer Average pLDDT": "The AlphaFold2 Multimer model average pLDDT provides the mean confidence across all residues in multimeric predictions (0-100, higher is better).",
         "AlphaFold2 Multimer pTM Score": "The AlphaFold2 Multimer model pTM score assesses overall fold accuracy in multimeric predictions (0-1, higher is better).",
-        "AlphaFold2 Multimer Interface pTM": "The AlphaFold2 Multimer model interface pTM specifically evaluates accuracy of interface regions in multimeric predictions (0-1, higher is better)."
+        "AlphaFold2 Multimer Interface pTM": "The AlphaFold2 Multimer model interface pTM specifically evaluates accuracy of interface regions in multimeric predictions (0-1, higher is better).",
     }
     return descriptions.get(score, "No description available for this score.")
 
-def compute_correlation_data(spr_data_with_scores: pd.DataFrame, score_cols: list[str]) -> pd.DataFrame:
+
+def compute_correlation_data(
+    spr_data_with_scores: pd.DataFrame, score_cols: list[str]
+) -> pd.DataFrame:
     corr_data_file = Path("corr_data.csv")
     if corr_data_file.exists():
         logger.info(f"Loading correlation data from {corr_data_file}")
         return pd.read_csv(corr_data_file)
-    
+
     corr_data = []
     spr_data_with_scores["log_kd"] = np.log10(spr_data_with_scores["KD (nM)"])
     kd_col = "KD (nM)"
     corr_funcs = {}
     corr_funcs["Spearman"] = spearmanr
     corr_funcs["Pearson"] = pearsonr
-    corr_funcs["R²"] = linregress
-    for correlation_type, corr_func in corr_funcs.items():
-        for score_col in score_cols:
-            logger.info(f"Computing {correlation_type} correlation between {score_col} and KD (nM)")
-            res = corr_func(spr_data_with_scores[kd_col], spr_data_with_scores[score_col])
-            logger.info(f"Correlation function: {corr_func}")
-            correlation_value = res.rvalue**2 if correlation_type == "R²" else res.statistic
-            corr_data.append({
-                "correlation_type": correlation_type,
-                "score": score_col,
-                "correlation": correlation_value,
-                "p-value": res.pvalue
-            })
-            logger.info(f"Correlation {correlation_type} between {score_col} and KD (nM): {correlation_value}")
+    for kd_col in ["KD (nM)", "log_kd"]:
+        for correlation_type, corr_func in corr_funcs.items():
+            for score_col in score_cols:
+                logger.info(
+                    f"Computing {correlation_type} correlation between {score_col} and {kd_col}"
+                )
+                res = corr_func(
+                    spr_data_with_scores[kd_col], spr_data_with_scores[score_col]
+                )
+                logger.info(f"Correlation function: {corr_func}")
+                correlation_value = res.statistic
+                corr_data.append(
+                    {
+                        "correlation_type": correlation_type,
+                        "kd_col": kd_col,
+                        "score": score_col,
+                        "correlation": correlation_value,
+                        "p-value": res.pvalue,
+                    }
+                )
 
     corr_data = pd.DataFrame(corr_data)
     # Find the lines in corr_data with NaN values and remove them
     corr_data = corr_data[corr_data["correlation"].notna()]
     # Sort correlation data by correlation value
-    corr_data = corr_data.sort_values('correlation', ascending=True)
-    
+    corr_data = corr_data.sort_values("correlation", ascending=True)
+
     corr_data.to_csv("corr_data.csv", index=False)
-    
+
     return corr_data
 
-def plot_correlation_ranking(corr_data: pd.DataFrame, correlation_type: str) -> go.Figure:
+
+def plot_correlation_ranking(
+    corr_data: pd.DataFrame, correlation_type: str, kd_col: str
+) -> go.Figure:
     # Create bar plot of correlations
-    data = corr_data[corr_data["correlation_type"] == correlation_type]
-    corr_ranking_plot = go.Figure(data=[
-        go.Bar(
-            x=data["correlation"],
-            y=data["score"], 
-            name=correlation_type,
-            text=data["correlation"],
-            orientation='h',
-            hovertemplate="<i>Score:</i> %{y}<br><i>Correlation:</i> %{x:.3f}<br>"
-        )
-    ])
+    data = corr_data[
+        (corr_data["correlation_type"] == correlation_type)
+        & (corr_data["kd_col"] == kd_col)
+    ]
+    corr_ranking_plot = go.Figure(
+        data=[
+            go.Bar(
+                x=data["correlation"],
+                y=data["score"],
+                name=correlation_type,
+                text=data["correlation"],
+                orientation="h",
+                hovertemplate="<i>Score:</i> %{y}<br><i>Correlation:</i> %{x:.3f}<br>",
+            )
+        ]
+    )
     corr_ranking_plot.update_layout(
         title="Correlation with Binding Affinity",
         yaxis_title="Score",
         xaxis_title=correlation_type,
         template="simple_white",
-        showlegend=False
+        showlegend=False,
     )
     return corr_ranking_plot
 
-def fake_predict_and_correlate(spr_data_with_scores: pd.DataFrame, score_cols: list[str], main_cols: list[str]) -> tuple[pd.DataFrame, go.Figure]:
+
+def fake_predict_and_correlate(
+    spr_data_with_scores: pd.DataFrame, score_cols: list[str], main_cols: list[str]
+) -> tuple[pd.DataFrame, go.Figure]:
     """Fake predict structures of all complexes and correlate the results."""
-    
+
     corr_data = compute_correlation_data(spr_data_with_scores, score_cols)
-    corr_ranking_plot = plot_correlation_ranking(corr_data, "Spearman")
+    corr_ranking_plot = plot_correlation_ranking(corr_data, "Spearman", kd_col="KD (nM)")
 
     cols_to_show = main_cols[:]
     cols_to_show.extend(score_cols)
@@ -131,17 +152,20 @@ def fake_predict_and_correlate(spr_data_with_scores: pd.DataFrame, score_cols: l
 
     return spr_data_with_scores[cols_to_show].round(2), corr_ranking_plot, corr_plot
 
-def make_regression_plot(spr_data_with_scores: pd.DataFrame, score: str, use_log: bool) -> go.Figure:
+
+def make_regression_plot(
+    spr_data_with_scores: pd.DataFrame, score: str, use_log: bool
+) -> go.Figure:
     """Select the regression plot to display."""
     # corr_plot is a scatter plot of the regression between the binding affinity and each of the scores
-    scatter =  go.Scatter(
-            x=spr_data_with_scores["KD (nM)"],
-            y=spr_data_with_scores[score],
-            name=f"Samples",
-            mode='markers',  # Only show markers/dots, no lines
-            hovertemplate="<i>Score:</i> %{y}<br><i>KD:</i> %{x:.2f}<br>",
-            marker=dict(color='#1f77b4')  # Set color to match default first color
-        )   
+    scatter = go.Scatter(
+        x=spr_data_with_scores["KD (nM)"],
+        y=spr_data_with_scores[score],
+        name=f"Samples",
+        mode="markers",  # Only show markers/dots, no lines
+        hovertemplate="<i>Score:</i> %{y}<br><i>KD:</i> %{x:.2f}<br>",
+        marker=dict(color="#1f77b4"),  # Set color to match default first color
+    )
     corr_plot = go.Figure(data=scatter)
     corr_plot.update_layout(
         xaxis_title="KD (nM)",
@@ -154,7 +178,7 @@ def make_regression_plot(spr_data_with_scores: pd.DataFrame, score: str, use_log
             xanchor="right",
             x=1,
         ),
-        xaxis_type="log" if use_log else "linear"  # Set x-axis to logarithmic scale
+        xaxis_type="log" if use_log else "linear",  # Set x-axis to logarithmic scale
     )
     # compute the regression line
     if use_log:
@@ -162,23 +186,25 @@ def make_regression_plot(spr_data_with_scores: pd.DataFrame, score: str, use_log
         x_vals = np.log10(spr_data_with_scores["KD (nM)"])
     else:
         x_vals = spr_data_with_scores["KD (nM)"]
-    
+
     # Fit line to data
     corr_line = np.polyfit(x_vals, spr_data_with_scores[score], 1)
-    
+
     # Generate x points for line
     corr_line_x = np.linspace(min(x_vals), max(x_vals), 100)
     corr_line_y = corr_line[0] * corr_line_x + corr_line[1]
-    
+
     # Convert back from log space if needed
     if use_log:
         corr_line_x = 10**corr_line_x
     # add the regression line to the plot
-    corr_plot.add_trace(go.Scatter(
-        x=corr_line_x,
-        y=corr_line_y,
-        mode='lines',
-        name=f"Regression line",
-        line=dict(color='#1f77b4')  # Set same color as scatter points
-    ))
-    return corr_plot
+    corr_plot.add_trace(
+        go.Scatter(
+            x=corr_line_x,
+            y=corr_line_y,
+            mode="lines",
+            name=f"Regression line",
+            line=dict(color="#1f77b4"),  # Set same color as scatter points
+        )
+    )
+    return corr_plot

folding_studio_demo/model_fasta_validators.py

@@ -248,15 +248,15 @@ class ChaiFastaValidator(BaseFastaValidator):
                     )
                 seen_names.add(name)
                 # validate sequence format
-                # sequence = str(record.seq).strip()
-                # if (
-                #     entity_type in {EntityType.PEPTIDE, EntityType.PROTEIN}
-                #     and not get_entity_type(sequence) == entity_type
-                # ):
-                #     return (
-                #         False,
-                #         f"CHAI Validation Error: Sequence type mismatch. Expected '{entity_type}' but found '{get_entity_type(sequence)}'",
-                #     )
+                sequence = str(record.seq).strip()
+                if (
+                    entity_type in {EntityType.PEPTIDE, EntityType.PROTEIN}
+                    and not get_entity_type(sequence) == entity_type
+                ):
+                    return (
+                        False,
+                        f"CHAI Validation Error: Sequence type mismatch. Expected '{entity_type}' but found '{get_entity_type(sequence)}'",
+                    )
 
         return True, None
 

folding_studio_demo/models.py

@@ -1,17 +1,26 @@
 """Models for the Folding Studio API."""
 
+import json
 import logging
 import os
+import sys
+import time
+from io import StringIO
 from pathlib import Path
 from typing import Any
 
 import gradio as gr
 import numpy as np
+from folding_studio import single_job_prediction
 from folding_studio.client import Client
+from folding_studio.commands.experiment import results as get_results
+from folding_studio.commands.experiment import status as get_status
 from folding_studio.query import Query
 from folding_studio.query.boltz import BoltzQuery
 from folding_studio.query.chai import ChaiQuery
 from folding_studio.query.protenix import ProtenixQuery
+from folding_studio_data_models import AF2Parameters, OpenFoldParameters
+from folding_studio_data_models.parameters.base import BaseFoldingParameters
 
 from folding_studio_demo.model_fasta_validators import (
     BaseFastaValidator,
@@ -20,15 +29,29 @@ from folding_studio_demo.model_fasta_validators import (
     ProtenixFastaValidator,
 )
 
+
+class Capturing(list):
+    """Capture stdout output."""
+
+    def __enter__(self):
+        self._stdout = sys.stdout
+        sys.stdout = self._stringio = StringIO()
+        return self
+
+    def __exit__(self, *args):
+        self.extend(self._stringio.getvalue().splitlines())
+        del self._stringio  # free up some memory
+        sys.stdout = self._stdout
+
+
 logger = logging.getLogger(__name__)
 
 
 class AF3Model:
-    def __init__(
-        self, api_key: str, model_name: str, query: Query, validator: BaseFastaValidator
-    ):
+    model_name = None
+
+    def __init__(self, api_key: str, query: Query, validator: BaseFastaValidator):
         self.api_key = api_key
-        self.model_name = model_name
         self.query = query
         self.validator = validator
 
@@ -116,8 +139,10 @@ class AF3Model:
 
 
 class ChaiModel(AF3Model):
+    model_name = "Chai"
+
     def __init__(self, api_key: str):
-        super().__init__(api_key, "Chai", ChaiQuery, ChaiFastaValidator())
+        super().__init__(api_key, ChaiQuery, ChaiFastaValidator())
 
     def call(
         self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
@@ -158,8 +183,10 @@ class ChaiModel(AF3Model):
 
 
 class ProtenixModel(AF3Model):
+    model_name = "Protenix"
+
     def __init__(self, api_key: str):
-        super().__init__(api_key, "Protenix", ProtenixQuery, ProtenixFastaValidator())
+        super().__init__(api_key, ProtenixQuery, ProtenixFastaValidator())
 
     def call(
         self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
@@ -179,8 +206,10 @@ class ProtenixModel(AF3Model):
 
 
 class BoltzModel(AF3Model):
+    model_name = "Boltz"
+
     def __init__(self, api_key: str):
-        super().__init__(api_key, "Boltz", BoltzQuery, BoltzFastaValidator())
+        super().__init__(api_key, BoltzQuery, BoltzFastaValidator())
 
     def call(
         self, seq_file: Path | str, output_dir: Path, format_fasta: bool = False
@@ -205,3 +234,113 @@ class BoltzModel(AF3Model):
             }
             for cif_path in prediction_paths
         }
+
+
+class OldModel:
+    model_name = None
+
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+
+    def call(
+        self,
+        seq_file: Path | str,
+        output_dir: Path,
+        parameters: BaseFoldingParameters,
+        *args,
+        **kwargs,
+    ) -> None:
+        """Predict protein structure from amino acid sequence using AF2 model.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file containing amino acid sequence
+            output_dir (Path): Path to output directory
+        """
+        output = single_job_prediction(
+            fasta_file=seq_file,
+            parameters=parameters,
+        )
+        experiment_id = output["message"]["experiment_id"]
+        done = False
+        while not done:
+            with Capturing() as output:
+                get_status(experiment_id)
+            status = output[0]
+            logger.info(f"Experiment {experiment_id} status: {status}")
+            if status == "Done":
+                done = True
+                logger.info("Downloading results")
+                get_results(
+                    experiment_id,
+                    force=True,
+                    unzip=True,
+                    output=output_dir / "results.zip",
+                )
+                logger.info("Results downloaded to %s", output_dir)
+            else:
+                logger.info("Sleeping for 10 seconds")
+                time.sleep(10)
+
+    def format_fasta(self, seq_file: Path | str) -> None:
+        """Format sequence to FASTA format.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file
+        """
+        return
+
+    def predictions(self, output_dir: Path) -> dict[int, dict[str, Any]]:
+        """Get the path to the prediction.
+
+        Args:
+            output_dir (Path): Path to output directory
+
+        Returns:
+            dict[int, dict[str, Any]]: Dictionary mapping model indices to their prediction paths and metrics
+        """
+        prediction_paths = list(
+            (output_dir / "results").rglob("relaxed_model_[0-9]_ptm_pred_0.pdb")
+        )
+        metrics_path = output_dir / "results" / "metrics_per_model.json"
+        if not metrics_path.exists():
+            return {}
+        with open(metrics_path, "r") as f:
+            metrics = json.load(f)
+        output = {
+            int(pred_path.stem.split("_")[2]): {
+                "prediction_path": pred_path,
+                "metrics": metrics[f"model_{int(pred_path.stem.split('_')[2])}_ptm"],
+            }
+            for pred_path in prediction_paths
+        }
+        return output
+
+    def has_prediction(self, output_dir: Path) -> bool:
+        """Check if prediction exists in output directory."""
+        return len(self.predictions(output_dir)) > 0
+
+    def check_file_description(self, seq_file: Path | str) -> tuple[bool, str | None]:
+        """Check if the file description is correct.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file
+
+        Returns:
+            tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
+        """
+
+        return True, None
+
+
+class AF2Model(OldModel):
+    model_name = "AlphaFold2"
+
+    def call(self, seq_file: Path | str, output_dir: Path, *args, **kwargs) -> None:
+        super().call(seq_file, output_dir, AF2Parameters(), *args, **kwargs)
+
+
+class OpenFoldModel(OldModel):
+    model_name = "OpenFold"
+
+    def call(self, seq_file: Path | str, output_dir: Path, *args, **kwargs) -> None:
+        super().call(seq_file, output_dir, OpenFoldParameters(), *args, **kwargs)

folding_studio_demo/predict.py

@@ -1,9 +1,11 @@
 """Predict protein structure using Folding Studio."""
 
+import concurrent.futures
 import hashlib
 import logging
 from io import StringIO
 from pathlib import Path
+from typing import Any
 
 import gradio as gr
 import numpy as np
@@ -12,7 +14,13 @@ from Bio import SeqIO
 from Bio.PDB import PDBIO, MMCIFParser, PDBParser, Superimposer
 from folding_studio_data_models import FoldingModel
 
-from folding_studio_demo.models import BoltzModel, ChaiModel, ProtenixModel
+from folding_studio_demo.models import (
+    AF2Model,
+    BoltzModel,
+    ChaiModel,
+    OpenFoldModel,
+    ProtenixModel,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -85,20 +93,22 @@ def convert_cif_to_pdb(cif_path: str, pdb_path: str) -> None:
 def create_plddt_figure(
     plddt_vals: list[list[float]],
     model_name: str,
+    indexes: list[int],
     residue_codes: list[list[str]] = None,
 ) -> go.Figure:
     """Create a plot of metrics."""
     plddt_traces = []
-    for i, plddt_val in enumerate(plddt_vals):
+
+    for i, (plddt_val, index) in enumerate(zip(plddt_vals, indexes)):
         # Create hover text with residue codes if available
         if residue_codes and i < len(residue_codes):
             hover_text = [
-                f"<i>pLDDT</i>: {plddt:.2f}<br><i>Residue:</i> {code} {idx}"
+                f"<i>{model_name} {index}</i><br><i>pLDDT</i>: {plddt:.2f}<br><i>Residue:</i> {code} {idx}"
                 for idx, (plddt, code) in enumerate(zip(plddt_val, residue_codes[i]))
             ]
         else:
             hover_text = [
-                f"<i>pLDDT</i>: {plddt:.2f}<br><i>Residue index:</i> {idx}"
+                f"<i>{model_name} {index}</i><br><i>pLDDT</i>: {plddt:.2f}<br><i>Residue index:</i> {idx}"
                 for idx, plddt in enumerate(plddt_val)
             ]
 
@@ -108,7 +118,7 @@ def create_plddt_figure(
                 y=plddt_val,
                 hovertemplate="%{text}<extra></extra>",
                 text=hover_text,
-                name=f"{model_name} {i}",
+                name=f"{model_name} {index}",
                 visible=True,
             )
         )
@@ -150,8 +160,19 @@ def _write_fasta_file(
     return seq_id, seq_file
 
 
-def extract_plddt_from_cif(cif_path):
-    structure = MMCIFParser().get_structure("structure", cif_path)
+def extract_plddt_from_structure(structure_path: str) -> tuple[list[float], list[str]]:
+    """Extract pLDDT values and residue codes from a structure file.
+
+    Args:
+        structure_path (Path): Path to structure file
+
+    Returns:
+        tuple[list[float], list[str]]: Tuple containing lists of pLDDT values and residue codes
+    """
+    if Path(structure_path).suffix == ".cif":
+        structure = MMCIFParser().get_structure("structure", structure_path)
+    else:
+        structure = PDBParser().get_structure("structure", structure_path)
 
     # Lists to store pLDDT values and residue codes
     plddt_values = []
@@ -206,6 +227,10 @@ def predict(
         model = ChaiModel(api_key)
     elif model_type == FoldingModel.PROTENIX:
         model = ProtenixModel(api_key)
+    elif model_type == FoldingModel.AF2:
+        model = AF2Model(api_key)
+    elif model_type == FoldingModel.OPENFOLD:
+        model = OpenFoldModel(api_key)
     else:
         raise ValueError(f"Model {model_type} not supported")
 
@@ -235,22 +260,36 @@ def predict(
         progress(
             0.4 + (0.4 * i / total_predictions), desc=f"Converting model {model_idx}..."
         )
-        cif_path = prediction["prediction_path"]
-        logger.info(f"CIF file: {cif_path}")
-
-        converted_pdb_path = str(
-            output_dir / f"{model.model_name}_prediction_{model_idx}.pdb"
-        )
-        convert_cif_to_pdb(str(cif_path), str(converted_pdb_path))
-        plddt_vals, residue_codes = extract_plddt_from_cif(cif_path)
-        pdb_paths.append(converted_pdb_path)
+        prediction_path = prediction["prediction_path"]
+        logger.info(f"Prediction file: {prediction_path}")
+        if Path(prediction_path).suffix == ".cif":
+            converted_pdb_path = str(
+                output_dir / f"{model.model_name}_prediction_{model_idx}.pdb"
+            )
+            convert_cif_to_pdb(str(prediction_path), str(converted_pdb_path))
+            pdb_paths.append(converted_pdb_path)
+        else:
+            pdb_paths.append(str(prediction_path))
+        plddt_vals, residue_codes = extract_plddt_from_structure(prediction_path)
         model_plddt_vals.append(plddt_vals)
         model_residue_codes.append(residue_codes)
 
     progress(0.8, desc="Generating plots...")
+    indexes = []
+    for pdb_path in pdb_paths:
+        if model_type in [
+            FoldingModel.AF2,
+            FoldingModel.OPENFOLD,
+            FoldingModel.SOLOSEQ,
+        ]:
+            indexes.append(int(Path(pdb_path).stem.split("_")[2]))
+        else:
+            indexes.append(int(Path(pdb_path).stem[-1]))
+
     plddt_fig = create_plddt_figure(
         plddt_vals=model_plddt_vals,
         model_name=model.model_name,
+        indexes=indexes,
         residue_codes=model_residue_codes,
     )
 
@@ -258,11 +297,13 @@ def predict(
     return pdb_paths, plddt_fig
 
 
-def align_structures(pdb_paths: list[str]) -> list[str]:
+def align_structures(
+    model_predictions: dict[FoldingModel, dict[int, dict[str, Any]]],
+) -> list[str]:
     """Align multiple PDB structures to the first structure.
 
     Args:
-        pdb_paths (list[str]): List of paths to PDB files to align
+        model_predictions (dict[FoldingModel, dict[int, dict[str, Any]]]): Dictionary mapping models to their prediction indices
 
     Returns:
         list[str]: List of paths to aligned PDB files
@@ -271,39 +312,47 @@ def align_structures(pdb_paths: list[str]) -> list[str]:
     parser = PDBParser()
     io = PDBIO()
 
-    # Parse the reference structure (first one)
-    ref_structure = parser.get_structure("reference", pdb_paths[0])
+    # Get the first structure as reference
+    first_model = next(iter(model_predictions.keys()))
+    first_pred = next(iter(model_predictions[first_model].values()))
+    ref_pdb_path = first_pred["pdb_path"]
+
+    # Parse reference structure and get CA atoms
+    ref_structure = parser.get_structure("reference", ref_pdb_path)
     ref_atoms = [atom for atom in ref_structure.get_atoms() if atom.get_name() == "CA"]
 
-    aligned_paths = [pdb_paths[0]]  # First structure is already aligned
+    for model_type in model_predictions.keys():
+        for index, prediction in model_predictions[model_type].items():
+            pdb_path = prediction["pdb_path"]
+
+            # Parse the structure to align
+            structure = parser.get_structure(f"{model_type}_{index}", pdb_path)
+            atoms = [atom for atom in structure.get_atoms() if atom.get_name() == "CA"]
 
-    # Align each subsequent structure to the reference
-    for i, pdb_path in enumerate(pdb_paths[1:], start=1):
-        # Parse the structure to align
-        structure = parser.get_structure(f"model_{i}", pdb_path)
-        atoms = [atom for atom in structure.get_atoms() if atom.get_name() == "CA"]
+            # Create superimposer
+            sup = Superimposer()
 
-        # Create superimposer
-        sup = Superimposer()
+            # Set the reference and moving atoms
+            sup.set_atoms(ref_atoms, atoms)
 
-        # Set the reference and moving atoms
-        sup.set_atoms(ref_atoms, atoms)
+            # Apply the transformation to all atoms in the structure
+            sup.apply(structure.get_atoms())
 
-        # Apply the transformation to all atoms in the structure
-        sup.apply(structure.get_atoms())
+            # Save the aligned structure
+            aligned_path = str(Path(pdb_path).parent / f"aligned_{Path(pdb_path).name}")
+            io.set_structure(structure)
+            io.save(aligned_path)
 
-        # Save the aligned structure
-        aligned_path = str(Path(pdb_path).parent / f"aligned_{Path(pdb_path).name}")
-        io.set_structure(structure)
-        io.save(aligned_path)
-        aligned_paths.append(aligned_path)
+            model_predictions[model_type][index]["pdb_path"] = aligned_path
 
-    return aligned_paths
+    return model_predictions
 
 
 def filter_predictions(
-    aligned_paths: list[str],
-    plddt_fig: go.Figure,
+    model_predictions: dict[FoldingModel, dict[int, dict[str, Any]]],
+    af2_selected: list[int],
+    openfold_selected: list[int],
+    solo_selected: list[int],
     chai_selected: list[int],
     boltz_selected: list[int],
     protenix_selected: list[int],
@@ -316,7 +365,7 @@ def filter_predictions(
         chai_selected (list[int]): Selected Chai model indices
         boltz_selected (list[int]): Selected Boltz model indices
         protenix_selected (list[int]): Selected Protenix model indices
-        model_predictions (dict[FoldingModel, list[int]]): Dictionary mapping models to their prediction indices
+        model_predictions (dict[FoldingModel, dict[int, dict[str, Any]]]): Dictionary mapping models to their prediction indices
 
     Returns:
         tuple[list[str], go.Figure]: Filtered PDB paths and updated pLDDT plot
@@ -325,26 +374,30 @@ def filter_predictions(
     filtered_fig = go.Figure()
 
     # Keep track of which traces to show
-    visible_paths = []
+    filtered_paths = []
 
     # Helper function to check if a trace should be visible
-    def should_show_trace(trace_name: str) -> bool:
-        model_name = trace_name.split()[0]
-        model_idx = int(trace_name.split()[1])
-
-        if model_name == "Chai" and model_idx in chai_selected:
+    def should_show_trace(model_name, pred_index: int) -> bool:
+        if model_name == FoldingModel.CHAI and pred_index in chai_selected:
+            return True
+        if model_name == FoldingModel.BOLTZ and pred_index in boltz_selected:
+            return True
+        if model_name == FoldingModel.PROTENIX and pred_index in protenix_selected:
+            return True
+        if model_name == FoldingModel.AF2 and pred_index in af2_selected:
             return True
-        if model_name == "Boltz" and model_idx in boltz_selected:
+        if model_name == FoldingModel.OPENFOLD and pred_index in openfold_selected:
             return True
-        if model_name == "Protenix" and model_idx in protenix_selected:
+        if model_name == FoldingModel.SOLOSEQ and pred_index in solo_selected:
             return True
         return False
 
     # Filter traces and paths
-    for i, trace in enumerate(plddt_fig.data):
-        if should_show_trace(trace.name):
-            filtered_fig.add_trace(trace)
-            visible_paths.append(aligned_paths[i])
+    for model_type in model_predictions.keys():
+        for index, prediction in model_predictions[model_type].items():
+            if should_show_trace(model_type, index):
+                filtered_fig.add_trace(prediction["plddt_trace"])
+                filtered_paths.append(prediction["pdb_path"])
 
     # Update layout
     filtered_fig.update_layout(
@@ -355,21 +408,58 @@ def filter_predictions(
         template="simple_white",
         legend=dict(yanchor="bottom", y=0.01, xanchor="left", x=0.99),
     )
+    return filtered_paths, filtered_fig
 
-    return visible_paths, filtered_fig
+
+def run_prediction(
+    sequence: str,
+    api_key: str,
+    model_type: FoldingModel,
+    format_fasta: bool = False,
+) -> dict[FoldingModel, dict[int, dict[str, Any]]]:
+    """Run a single prediction.
+
+    Args:
+        sequence (str): Amino acid sequence to predict structure for
+        api_key (str): Folding API key
+        model_type (FoldingModel): Folding model to use
+        format_fasta (bool): Whether to format the FASTA file
+
+    Returns:
+        Tuple containing:
+            - List of PDB paths
+            - pLDDT plot
+            - Dictionary mapping model to prediction indices
+    """
+    model_pdb_paths, model_plddt_traces = predict(
+        sequence, api_key, model_type, format_fasta=format_fasta
+    )
+    model_pdb_paths = sorted(model_pdb_paths)
+    model_predictions = {}
+    for pdb_path, plddt_trace in zip(model_pdb_paths, model_plddt_traces.data):
+        if model_type in [
+            FoldingModel.AF2,
+            FoldingModel.OPENFOLD,
+            FoldingModel.SOLOSEQ,
+        ]:
+            index = int(Path(pdb_path).stem.split("_")[2])
+        else:
+            index = int(Path(pdb_path).stem[-1])
+
+        model_predictions[index] = {"pdb_path": pdb_path, "plddt_trace": plddt_trace}
+    return model_predictions
 
 
 def predict_comparison(
     sequence: str, api_key: str, model_types: list[FoldingModel], progress=gr.Progress()
 ) -> tuple[
-    list[str],
-    go.Figure,
+    dict[FoldingModel, dict[int, dict[str, Any]]],
+    gr.CheckboxGroup,
+    gr.CheckboxGroup,
+    gr.CheckboxGroup,
     gr.CheckboxGroup,
     gr.CheckboxGroup,
     gr.CheckboxGroup,
-    list[str],
-    go.Figure,
-    dict,
 ]:
     """Predict protein structure from amino acid sequence using multiple models.
 
@@ -381,68 +471,94 @@ def predict_comparison(
 
     Returns:
         tuple containing:
-            - list[str]: Aligned PDB paths
-            - go.Figure: pLDDT plot
+            - dict[FoldingModel, dict[int, dict[str, Any]]]: Model predictions mapping
+            - gr.CheckboxGroup: AF2 predictions checkbox group
+            - gr.CheckboxGroup: OpenFold predictions checkbox group
+            - gr.CheckboxGroup: SoloSeq predictions checkbox group
             - gr.CheckboxGroup: Chai predictions checkbox group
             - gr.CheckboxGroup: Boltz predictions checkbox group
             - gr.CheckboxGroup: Protenix predictions checkbox group
-            - list[str]: Original PDB paths
-            - go.Figure: Original pLDDT plot
-            - dict: Model predictions mapping
     """
     if not api_key:
         raise gr.Error("Missing API key, please enter a valid API key")
 
-    # Set up unique output directory based on sequence hash
-    pdb_paths = []
-    plddt_traces = []
-    total_models = len(model_types)
+    progress(0, desc="Starting parallel predictions...")
+
+    # Run predictions in parallel
     model_predictions = {}
 
-    for i, model_type in enumerate(model_types):
-        progress(i / total_models, desc=f"Running {model_type} prediction...")
-        model_pdb_paths, model_plddt_traces = predict(
-            sequence, api_key, model_type, format_fasta=True
-        )
-        pdb_paths += model_pdb_paths
-        plddt_traces += model_plddt_traces.data
-        model_predictions[model_type] = [int(Path(p).stem[-1]) for p in model_pdb_paths]
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        # Create a future for each model prediction
+        future_to_model = {
+            executor.submit(
+                run_prediction, sequence, api_key, model_type, True
+            ): model_type
+            for model_type in model_types
+        }
+
+        # Process results as they complete
+        total_models = len(model_types)
+        completed = 0
+
+        for future in concurrent.futures.as_completed(future_to_model):
+            model_type = future_to_model[future]
+            try:
+                model_preds = future.result()
+                model_predictions[model_type] = model_preds
+
+                completed += 1
+                progress(
+                    completed / total_models,
+                    desc=f"Completed {model_type} prediction...",
+                )
+            except Exception as e:
+                logger.error(f"Prediction failed for {model_type}: {str(e)}")
+                raise gr.Error(f"Prediction failed for {model_type}: {str(e)}")
 
     progress(0.9, desc="Aligning structures...")
-    aligned_paths = align_structures(pdb_paths)
-    plddt_fig = go.Figure(data=plddt_traces)
-    plddt_fig.update_layout(
-        title="pLDDT",
-        xaxis_title="Residue index",
-        yaxis_title="pLDDT",
-        height=500,
-        template="simple_white",
-        legend=dict(yanchor="bottom", y=0.01, xanchor="left", x=0.99),
-    )
+
+    model_predictions = align_structures(model_predictions)
 
     progress(1.0, desc="Done!")
 
     # Create checkbox groups for each model type
+    af2_predictions = gr.CheckboxGroup(
+        visible=model_predictions.get(FoldingModel.AF2) is not None,
+        choices=list(model_predictions.get(FoldingModel.AF2, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.AF2, {}).keys()),
+    )
+    openfold_predictions = gr.CheckboxGroup(
+        visible=model_predictions.get(FoldingModel.OPENFOLD) is not None,
+        choices=list(model_predictions.get(FoldingModel.OPENFOLD, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.OPENFOLD, {}).keys()),
+    )
+    solo_predictions = gr.CheckboxGroup(
+        visible=model_predictions.get(FoldingModel.SOLOSEQ) is not None,
+        choices=list(model_predictions.get(FoldingModel.SOLOSEQ, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.SOLOSEQ, {}).keys()),
+    )
     chai_predictions = gr.CheckboxGroup(
         visible=model_predictions.get(FoldingModel.CHAI) is not None,
-        choices=model_predictions.get(FoldingModel.CHAI, []),
-        value=model_predictions.get(FoldingModel.CHAI, []),
+        choices=list(model_predictions.get(FoldingModel.CHAI, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.CHAI, {}).keys()),
     )
     boltz_predictions = gr.CheckboxGroup(
         visible=model_predictions.get(FoldingModel.BOLTZ) is not None,
-        choices=model_predictions.get(FoldingModel.BOLTZ, []),
-        value=model_predictions.get(FoldingModel.BOLTZ, []),
+        choices=list(model_predictions.get(FoldingModel.BOLTZ, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.BOLTZ, {}).keys()),
     )
     protenix_predictions = gr.CheckboxGroup(
         visible=model_predictions.get(FoldingModel.PROTENIX) is not None,
-        choices=model_predictions.get(FoldingModel.PROTENIX, []),
-        value=model_predictions.get(FoldingModel.PROTENIX, []),
+        choices=list(model_predictions.get(FoldingModel.PROTENIX, {}).keys()),
+        value=list(model_predictions.get(FoldingModel.PROTENIX, {}).keys()),
     )
 
     return (
+        model_predictions,
+        af2_predictions,
+        openfold_predictions,
+        solo_predictions,
         chai_predictions,
         boltz_predictions,
         protenix_predictions,
-        aligned_paths,
-        plddt_fig,
     )

spr_af_scores_mapped.csv

@@ -1,16 +1,54 @@
-antibody_name,antigen_lineage,kon (1/µM 1/s),koff (1/s),KD (nM),source_name,source_doi,pdb_id,antigen_chain_ids,antibody_heavy_chain_id,antibody_light_chain_id,structure_release_date,structure_resolution,gene_id,antigen_host,antigen_residue_indices,antigen_residue_indices_trimmed,antigen_domain,antibody_light_chain_host,epitope_residues,epitope_mutations,mutations,lineage_mutations_in_epitope,epitope_domain,epitope_is_multichain,epitope_isin_wt,paratope_residues,antibody_vh_sequence,antibody_vl_sequence,spike_sequence,antigen_sequence,antigen_sequence_without_indels,antigen_sequence_trimmed,antigen_sequence_trimmed_without_indels,antigen_pdb_sequence,antigen_pdb_sequence_trimmed,confidence_score_boltz,ptm_boltz,iptm_boltz,ligand_iptm_boltz,protein_iptm_boltz,complex_plddt_boltz,complex_iplddt_boltz,complex_pde_boltz,complex_ipde_boltz,interchain_pae_monomer,interface_pae_monomer,overall_pae_monomer,interface_plddt_monomer,average_plddt_monomer,ptm_monomer,interface_ptm_monomer,interchain_pae_multimer,interface_pae_multimer,overall_pae_multimer,interface_plddt_multimer,average_plddt_multimer,ptm_multimer,interface_ptm_multimer
-bd45_43;covox_269,B,0.293,0.00151,5.162393162,huhn_2025_spr,10.1038/s41467-024-54916-5,7neh,E,H,L,02/04/21,1.77,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(332, 527)",,RBD,homo sapiens (9606),R403 D405 E406 R408 T415 G416 K417 D420 Y421 Y453 L455 F456 R457 K458 S459 N460 Y473 Q474 A475 G476 F486 N487 Y489 Q493 S494 Y495 G496 F497 Q498 N501 Y505,,,,RBD,False,True,H:N30 R31 N32 Y33 Y52 S53 G54 G55 S56 F58 R94 D95 F96 Y97 E98 D101;L:S30 S31 Y32 Q90 L91 N92 S93 Y94,QVQLVESGGGLIQPGGSLRLSCAASGLTVNRNYMSWIRQAPGKGLEWVSVIYSGGSTFYADSVKGRFTISRDNSKNTLSLQMNSLRAEDTAIYYCARDFYEGSFDIWGQGTMVTVSS,AIQLTQSPSFLSASIGDRVTITCRASQGISSYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTEFTLTISSLQPEDFASYYCQQLNSYPAPVFGPGTKVDIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,ITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,ITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,,,-TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGK,,0.815568387508392,0.631265461444855,0.499751955270767,0.0,0.499751955270767,0.894522488117218,0.808752834796906,0.574461817741394,4.69568395614624,3.723450183868408,2.065487838091536,3.046133041381836,97.62024275169595,97.58975656824344,0.9224043719056731,0.8713607670503896,29.375048637390137,27.4414244016436,28.373729705810547,29.83158417666077,31.749196704388066,0.1932366931512888,0.1605053034841711
-bd45_41;covox_384,B,0.7466666666666667,0.0043133333333333,6.0200266319999995,huhn_2025_spr,10.1038/s41467-024-54916-5,7bep,C,D,F,12/24/20,2.61,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(332, 528)",,RBD,homo sapiens (9606),L452 L455 F456 I472 G482 V483 E484 G485 F486 Y489 F490 L492,,,,RBD,False,True,F:S30 Y32 N92 N93 A94 L95;D:Y50 R52 H55 T56 Y58 L98 F100 L100A E100B W100C,EVQLVESGGGLVKPGESLRLSCAASGFTFSDYYMTWIRQAPGKGLEWVSYIRSSGHTIYYADSVKGRFTISRDNAKNSLYLQMNSLRVEDTAVYYCARGGVLRFLEWPLNAFDIWGQGTMVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQGISNYLAWYQQKPGKVPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQKYNNALGTFGQGTKVEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,ITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK,ITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK,,,-TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGKK,,0.783776104450226,0.566782534122467,0.427239209413528,0.0,0.427239209413528,0.87291032075882,0.786508798599243,0.867737531661987,9.8238639831543,4.809793472290039,2.045966428021883,3.723745822906494,97.230994247972,97.19396149825464,0.8891827579215439,0.8724955180116689,29.54940414428711,27.382281917096904,28.28030586242676,31.741909817932445,35.71164433102995,0.2412751374627494,0.1611393982227129
-covox_58,B,0.479,0.00415,10.472440945,huhn_2025_spr,10.1038/s41467-024-54916-5,7qny,E,A,B,12/23/21,2.84,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(334, 516)",,RBD,homo sapiens (9606),E340 V341 A344 T345 R346 F347 A348 S349 A352 N354 K356 S399 K444 Y449 N450 R466,,,,RBD,False,True,A:S52 W52A N53 S54 G55 T56 I57 G58 T99 F100 V100B L100C;B:S95 D95A,QVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQPPGKGLEWVSGVSWNSGTIGYADSVKGRFIISRDNAKNSLYLQMNSLKAEDTALYYCAREVGGTFGVLISREGGLDYWGQGTLVTVSS,SYELTQPPSVSVAPGQTARITCGGNTIGSKSVHWYQQRPGQAPVLVVYDDSDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDSSSDRVVFGGGTKLTVL,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,0.813834190368652,0.623498022556305,0.497233539819717,0.0,0.497233539819717,0.892984330654144,0.831231832504273,0.777176558971405,6.74987268447876,3.416905641555786,2.73430979206958,3.271117687225342,95.47922926078462,96.64388937350576,0.9293244224193686,0.8333618649239609,29.62367248535156,27.908499213470808,28.87879753112793,27.93079605430594,28.99558687866513,0.1632285223405918,0.1555844490249545
-covox_278,B,0.4905,0.006315,12.865473560000002,huhn_2025_spr,10.1038/s41467-024-54916-5,7or9,E,H,L,06/04/21,2.34,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 527)",,RBD,homo sapiens (9606),T345 R346 Y351 N440 L441 D442 S443 K444 V445 G446 G447 N448 Y449 N450 Y451 L452 T470 E484 F490 L492 P499,,,,RBD,False,True,H:I30 R31 W50 N53 Y56 N58 I98 L99 T100 G100A Y100B L100C D100D;L:A30 S31 Y32 L33 A50 S52 S91 Y92 S93 T94 L95,QVQLVQSGAEVKKPGASVKVSCKASGYIFIRYGISWVRQAPGQGLEWMGWISANNGYTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDGGILTGYLDYFDHWGQGTLVTVSS,DIQMTQSPSSLSASVGDRLTITCRASQSIASYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYHCQQSYSTLGITFGPGTKVDIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGK,,0.780656039714813,0.592200815677643,0.456630498170853,0.0,0.456630498170853,0.861662447452545,0.78568172454834,0.749391078948975,6.61570310592651,3.967527985572815,1.887516614885252,3.1754387617111206,97.71261536639231,97.2269496569076,0.922128938711486,0.8817608181456824,29.532506942749023,26.75908168640427,27.047605514526367,42.820148956954306,52.943542943851064,0.3415059963152309,0.1679752252684513
-bd45_39;covox_45,B,0.2276666666666666,0.0065266666666666,28.70041021666667,huhn_2025_spr,10.1038/s41467-024-54916-5,7bel,X,C,D,12/23/20,2.53,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(334, 527)",,RBD,homo sapiens (9606),W353 N354 R355 K356 R357 I358 S359 N360 N394 Y396 P426 D428 K462 P463 F464 E465 R466 I468 E516 L518,,,,RBD,False,True,C:T31 Y32 S52 Y52A D53 S55 N56 Y58 K94 S97 Y98 A99 Y100 Y100A Y100B Y100C D101;D:N31 Y32 Y49 D50 N53 L54 E55 D92 N93,QVQLVESGGGVVQPGRSLRLSCAASGFTFSTYAMHWVRQAPGKGLEWVAVLSYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKGGSYAYYYYMDVWGKGTTVTVSS,DIQLTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGGGSGTDFTFTITSLQPEDIATYYCQQYDNLPLTFGGGTKVDIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,,,NLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGK,,0.864258110523224,0.750974237918854,0.670291066169739,0.0,0.670291066169739,0.912749826908112,0.863511741161346,0.518989622592926,4.14870548248291,3.968512177467346,2.542677193895171,3.364487648010254,96.57266771591796,97.10952461348532,0.914509605012032,0.8440767834045971,29.85724639892578,28.17639105213889,29.10641288757324,28.077718924878248,29.73296412083579,0.1647350462688318,0.1528304609817936
-covox_253,B,0.69975,0.0343125,68.8637716275,huhn_2025_spr,10.1038/s41467-024-54916-5,7ben,C,D,F,12/24/20,2.5,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(336, 516)",,RBD,homo sapiens (9606),K417 Y421 Y453 L455 F456 R457 K458 Y473 Q474 A475 G476 S477 T478 P479 G485 F486 N487 C488 Y489 Q493,,,,RBD,False,True,D:T30 T31 S32 A33 W50 V52 G53 S54 N56 P95 S99 T100 S100A C100B Y100C D100D A100E F100F;F:S31 Y32 Y91 G92 Y96,QVQLVQSGPEVKKPGTSVKVSCKASGFTFTTSAVQWVRQARGQRLEWIGWIVVGSGNTNYAQKFQERVTITRDMSTTTAYMELSSLRSEDTAVYFCAAPHCNSTSCYDAFDIWGQGTMVTVSS,DIQMTQSPGTLSLSPGEGATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSGATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPYTFGQGTKVEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,0.914573848247528,0.883981227874756,0.856384038925171,0.0,0.856384038925171,0.929121315479279,0.915573418140411,0.315263271331787,0.520528972148895,4.548707008361816,2.2264484833746336,3.614177465438843,97.53705417062714,96.7483133214709,0.9031158034443396,0.8620604364444605,26.547210693359375,25.422632414234357,26.56904697418213,37.23036184257237,49.15953242545456,0.4282457681214566,0.1836282104429391
-bd45_42;covox_316,B,0.4048,0.04032,102.189625528,huhn_2025_spr,10.1038/s41467-024-54916-5,7beh,E,H,L,12/23/20,2.3,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 527)",,RBD,homo sapiens (9606),Y449 Y453 L455 F456 V483 E484 G485 F486 N487 C488 Y489 F490 L492 Q493 S494,,,,RBD,False,True,H:T30 G31 Y33 W50 N52 N53 S54 G56 N58 F98 S99 M100 V100A R100B;L:Y30C Y32 Y91 N95 W96,QVQLVQSGAEVKKPGASVKVSCKASGYTFTGYYMHWVRQAPGQGLEWMGWINPNSGGTNYTQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYSCARDMAFSMVRGSFDYWGQGTLVTVSS,QAVLTQPPSASGSPGQSVTISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYEVSKRPSGVPDRFSGSKSGNTASLTVSGLQAEDEADYYCSSYAGSNHWVFGGGTKLTVL,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGK,,0.836899876594544,0.708972632884979,0.600401759147644,0.0,0.600401759147644,0.896024405956268,0.814204096794128,0.500937461853027,3.40170907974243,3.9755221605300903,2.4679562958916876,3.3112473487854004,97.23949349040056,97.2444966855708,0.920971468523108,0.8482919517430124,29.79031562805176,17.87317807569991,25.04682159423828,59.24685751554468,57.925111098887655,0.5151529747182887,0.3037515428354363
-bd45_38;covox_75,B,0.0959999999999999,0.0313285714285714,343.82737152857146,huhn_2025_spr,10.1038/s41467-024-54916-5,7ben,C,G,I,12/24/20,2.5,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(336, 516)",,RBD,homo sapiens (9606),N440 L441 D442 S443 K444 V445 G446 G447 N448 Y449 N450 L452 E484 F490 L492 Q493 S494 Y495 G496,,,,RBD,False,True,I:S30 S31 W32 A50 A91 K92 S93 F94 F96;G:V98 V100 A100A A100B R100C N100D H100E Y100F Y100G N100H,QVQLVESGGGVVQPGRSLRLSCAASGFTFNNYPLHWVRQAPGKGPEWVAVISQDGGNKYYVDSVKGRFTISRDNSKNTLYLQMNNLRAEDTALYYCARDVVVVVAARNHYYNGMDVWGQGTTVTVSS,DIQLTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYAVSSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQAKSFPFTFGPGTKVEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,,CPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFE,,0.801655769348145,0.594999551773071,0.457680195569992,0.0,0.457680195569992,0.887649655342102,0.804755747318268,0.705984234809876,5.93294382095337,4.286665439605713,2.027977637602458,3.3147683143615723,97.71978281117325,96.8930778481744,0.9159003126831424,0.8735424882781723,26.957773208618164,25.519889059658937,26.960838317871097,37.47481094984693,47.88164702045448,0.4208675533072618,0.1824414576700511
-amubarvimab;bd45_21;brii_196;p2c_1f11,B,,,2.12,ju_2020_nature,10.1038/s41586-020-2380-z,7e8m,E,H,L,03/02/21,2.09,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 527)",,RBD,homo sapiens (9606),R403 T415 G416 N417 D420 Y421 Y453 L455 F456 R457 K458 S459 N460 Y473 Q474 A475 F486 N487 Y489 Q493 S494 Y495 Y501 Y505,K417N N501Y,,,RBD,False,False,H:S30 S31 N32 Y33 Y52 S53 G54 G55 S56 Y58 R94 L96 V97 V98 Y99 D101;L:S30 S30A S31 Y32 Q90 Y91 G92 S93,EVQLVESGGGLVQPGGSLRLSCAASGITVSSNYMNWVRQAPGKGLEWVSLIYSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYHCARDLVVYGMDVWGQGTTVTVSS,EIVLTQSPGTLSISPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPTFGQGTKLEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGP,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVKGFNCYFPLQSYGFQPTYGVGYQPYRVVVLSFELLHAPATVCGP,,0.806627571582794,0.604901313781738,0.469731539487839,0.0,0.469731539487839,0.890851616859436,0.782432734966278,0.653874278068543,5.79969501495361,3.889882802963257,2.1447279756893742,3.1076622009277344,97.63849821703336,97.03031474832582,0.9184391725953712,0.8667697875832223,29.34839630126953,27.19294934209429,28.324119567871097,28.5653600028306,32.27080636498517,0.2005613357989986,0.1631862178489821
-bd45_7;p2b_2f6,B,,,5.14,ju_2020_nature,10.1038/s41586-020-2380-z,8dcc,E,H,L,07/06/22,2.57,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 526)",,RBD,homo sapiens (9606),Y351 K444 G446 G447 N448 Y449 N450 L452 I472 V483 E484 G485 F490 L492 Q493 S494,,,,RBD,False,True,H:S30 S31 G31A Y32 H53 G98 I99 V100 V100A V100B P100C R100H;L:G30B Y30C N31 Y32 E50,QVQLQESGPGLVKPSETLSLTCTVSGYSISSGYYWGWIRQPPGKGLEWIGSIYHSGSTYYNPSLKTRVTISVDTSKNQFSLKLSSVTAADTAVYYCARAVVGIVVVPAAGRRAFDIWGQGTMVTVSS,QSALTQPPSASGSPGQSVTISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYEVSKRPSGVPDRFSGSKSGNTASLTVSGLQAEDEADYYCSSYAGSNNLVFGGGTKLTVL,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,,KNLCPFGEVFNATRFASVYAWNRKRISNCVYDYSVLYNSASFSTFKCYGVSPTKLKDLCFTYVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDTSTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLTFELLDAPPTVCG,,0.779910862445831,0.605188310146332,0.472482472658157,0.0,0.472482472658157,0.856767952442169,0.776917576789856,0.806709885597229,7.29232597351074,4.219140529632568,2.249224998168091,3.4182815551757812,97.19018392155026,97.32025286418748,0.9073658369599488,0.8607524475911202,29.55217933654785,27.74894137274779,28.309181213378903,30.19989435806049,34.6484895742348,0.2291970218536952,0.1572482637418456
-bd45_22;p2c_1a3,B,,,2.47,ju_2020_nature,10.1038/s41586-020-2380-z,7cdj,E,H,L,06/19/20,3.4,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 526)",,RBD,homo sapiens (9606),Y449 L452 L455 F456 G476 S477 T478 P479 C480 N481 V483 E484 G485 F486 N487 Y489 F490 L492 Q493 S494,,,,RBD,False,True,L:S30 Y32 L91 N92 S93 Y94;H:Y33 Y50 S52 S52A S53 G54 S55 T56 F96 S97 H98 Q99 Q100 L100A,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMSWIRQAPGKGLEWVSYISSSGSTIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARDFSHQQLVPSWGQGTLVTVSS,DIQLTQSPSFLSASVGDRVTITCRASQGISSYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCQQLNSYPLTFGGGTKVEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,0.804550170898438,0.655625939369202,0.529589176177979,0.0,0.529589176177979,0.873290419578552,0.78179919719696,0.559738159179688,4.29560947418213,4.319523096084595,2.2729202048269994,3.5641493797302246,96.98280673538328,96.97517601506055,0.9009612852404294,0.859393807173975,29.33012962341309,27.404896638158995,28.285560607910156,29.053955817539126,31.906904000955564,0.1927061390351921,0.1608966397629959
-p22a_1d1,B,,,5.79,zhang_2021_nat_comms,10.1038/s41467-021-24514-w,7chs,E,H,L,07/06/20,2.4,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 526)",,RBD,homo sapiens (9606),R403 D405 E406 T415 G416 K417 D420 Y421 Y453 L455 F456 R457 K458 S459 N460 Y473 Q474 A475 F486 N487 Y489 F490 L492 Q493 Y495 G496 F497 Q498 N501 Y505,,,,RBD,False,True,H:S30 S31 N32 Y33 Y52 S53 G54 G55 S56 Y58 R94 R96 D97 Y98 Y99 D101;L:S30 Y32 H90 L91 N92 Y94,EVQLVESGGGLIQPGGSLRLSCAASGFTVSSNYMSWVRQAPGKGLEWVSVIYSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRDYYGMDVWGQGTTVTVSS,DIQLTQSPSFLSASVGDRVTITCRASQGISSYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLHLNSYRTFGLGTKVEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,0.799001395702362,0.583963930606842,0.443157732486725,0.0,0.443157732486725,0.887962281703949,0.783845901489258,0.580890655517578,4.91616106033325,3.8282817602157593,2.26178554607675,3.11441421508789,97.40743986108662,96.75478221302072,0.919711764602881,0.8600319811246402,29.322518348693848,27.32672814436949,28.218334197998047,29.90523019047135,33.48768084795022,0.2129387496796693,0.1617372981596133
-p5a_1d2,B,,,14.02,zhang_2021_nat_comms,10.1038/s41467-021-24514-w,7cho,A,B,C,07/06/20,2.56,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 526)",,RBD,homo sapiens (9606),R403 D405 T415 G416 K417 D420 Y421 Y453 L455 F456 R457 K458 S459 N460 Y473 Q474 A475 F486 N487 Y489 Q493 N501 G502 G504 Y505,,,,RBD,False,True,B:S30 S31 N32 Y33 Y52 S53 G54 G55 S56 Y58 R94 L96 Q97 V98 G99 A100 T100A D100C D101;C:G30A A30B G30C Y31 S95A,EVQLVESGGGLIQPGGSLRLSCAASGFIVSSNYMSWVRQAPGKGLEWVSIIYSGGSTYYADSVKGRFTISRDNSNNTLYLQMNSLRAEDTAVYYCARALQVGATSDYFDYWGQGTLVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNIGAGYDVHWYQQLPGTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAITGLQAEDETDYYCQSCDSSLSVVVFGGGTKLTVL,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCG,,0.840566754341126,0.695268392562866,0.592147290706635,0.0,0.592147290706635,0.902671635150909,0.818431437015533,0.5360067486763,3.70838093757629,4.224268436431885,2.5905382789884297,3.5311367511749268,95.49891960723396,95.8846969442526,0.9089658019474836,0.8413878468162714,29.70927906036377,27.910024175490378,28.62742042541504,28.847876247700896,31.60096486393412,0.19691031850727,0.155568632470583
-p5a_3c8,B,,,1.3,zhang_2021_nat_comms,10.1038/s41467-021-24514-w,7chp,E,H,L,07/06/20,2.36,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(333, 522)",,RBD,homo sapiens (9606),R403 D405 E406 R408 T415 G416 K417 D420 Y421 Y449 Y453 L455 F456 R457 K458 S459 N460 Y473 Q474 A475 F486 N487 Y489 Q493 S494 Y495 G496 F497 Q498 N501 Y505,,,,RBD,False,True,L:S30 S31 Y32 H90 L91 N92 S93 Y94;H:S31 N32 Y33 Y52 S53 G54 G55 S56 Y58 R94 L96 Q97 E98 H99 D101,EVQLVESGGGLIQPGGSLRLSCAASGFTVSSNYMSWVRQAPGKGLEWVSFIYSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDLQEHGMDVWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQGISSYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHLNSYPPGYTFGQGTKLEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPA,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPA,,,TNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPA,,0.799769759178162,0.636229515075684,0.505165874958038,0.0,0.505165874958038,0.873420715332031,0.791917443275452,0.623001635074616,4.9553861618042,3.5803418159484863,1.945987765655582,2.9488518238067627,97.97371806493044,96.72675060619106,0.9255255894656254,0.8783303373121849,29.55667209625244,27.446456582910205,28.21571350097656,29.73038928410532,33.446187252617214,0.2329041029431821,0.1604514663965165
-p5a_3c12,B,,,8.47,zhang_2021_nat_comms,10.1038/s41467-021-24514-w,7d0d,A,H,L,09/09/20,3.8,S,severe acute respiratory syndrome coronavirus 2 (2697049),"(27, 1146)","(327, 528)",spike,homo sapiens (9606),G416 K417 F456 A475 V483 E484 G485 F486 N487 Y489 F490 Q493,,,,RBD,False,True,L:Y30A S30C N30D N30E Y91;H:S31A L50 Y52 W53 D54 D56 R58 F97 L98 T99 Y100B,QITLKESGPTLVKPTQTLTLTCTFSGFSLSTSGVGVGWIRQPPGKALEWLALIYWDDDKRYSPSLKSRLTITKDTSKNQVVLTMTNMDPVDTATYYCAHSLFLTVGYSSSWSPFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLYSSNNKNYLAWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYSTPHTFGQGTKLEIK,MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT,AYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELD,AYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELD,VRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK,VRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK,AYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDPPEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELD,VRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPK,0.801107525825501,0.621712863445282,0.496211916208267,0.0,0.496211916208267,0.877331376075745,0.781339168548584,0.592956483364105,4.11549758911133,7.736447334289551,6.066557600040628,6.794885158538818,82.95154693193497,90.5823181115092,0.8142294848482255,0.6673523088520965,29.801297187805176,27.44481049316726,28.826437950134277,27.01779102581493,30.210954111641698,0.2179942246544585,0.1604690751968928
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