Merge remote-tracking branch 'origin/main' into tab_2_correlation

.gitignore

@@ -1,5 +1,5 @@
 .envrc
-
-boltz_results/
-
 **/__pycache__/
+output/
+sequences/
+boltz_results/

Dockerfile

@@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     wget build-essential git && \
     rm -rf /var/lib/apt/lists/*
 
-COPY --from=uv-fetcher /uv /uvx /usr/local/bin/
+COPY --from=uv-fetcher /uv /uvx /bin/
 
 ENV VIRTUAL_ENV=/modules/.venv
 RUN uv venv "$VIRTUAL_ENV" && . "$VIRTUAL_ENV/bin/activate"
@@ -17,35 +17,28 @@ ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 # Set the working directory to the user's home directory
 WORKDIR /app
 
-COPY pyproject.toml uv.lock /app/
+# Create a non-root user and set permissions
+RUN useradd -m -u 1000 appuser && \
+    chown -R appuser:appuser /app
+
+# Switch to non-root user
+USER appuser
 
-RUN uv sync --frozen --active --directory /app --inexact
+COPY pyproject.toml uv.lock README.md ./
+COPY folding-studio ./folding-studio
 
-COPY folding-studio /app/folding-studio
-RUN cd /app/folding-studio && uv pip install -e .
+RUN uv sync --locked --no-install-project --no-dev
+
+COPY folding_studio_demo ./folding_studio_demo
+RUN uv sync --locked --no-dev
 
-COPY app.py /app/app.py
-COPY molecule.py /app/molecule.py
 # COPY pred.cif /app/boltz_results/pred_model_0.cif
 # COPY plddt_0.npz /app/boltz_results/plddt_0.npz
 COPY spr_af_scores_mapped.csv /app/spr_af_scores_mapped.csv
 
 EXPOSE 7860
 ENV GRADIO_SERVER_NAME="0.0.0.0"
-ARG FOLDING_PROJECT_CODE
+ARG FOLDING_PROJECT_CODE="folding_dev"
 ENV FOLDING_PROJECT_CODE=$FOLDING_PROJECT_CODE
 
-# Create directory for HTML output
-RUN mkdir -p /app/output/html
-
-# Create volume for HTML output
-VOLUME ["/app/output/html"]
-
-# Create a non-root user and set permissions
-RUN useradd -m -u 1000 appuser && \
-    chown -R appuser:appuser /app
-
-# Switch to non-root user
-USER appuser
-
-CMD ["python3", "app.py"]
+CMD ["uv", "run", "folding-studio-demo"]

Makefile

@@ -1,5 +1,5 @@
 # Variables
-DOCKER_IMAGE_NAME = folding-studio
+DOCKER_IMAGE_NAME = folding-studio-demo
 DOCKER_IMAGE_TAG = latest
 PORT = 7860
 OUTPUT_DIR = ./output

folding_studio_demo/__init__.py

@@ -0,0 +1,12 @@
+"""Folding Studio Demo."""
+
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.StreamHandler(),
+    ],
+)

app.py → folding_studio_demo/app.py

@@ -1,192 +1,148 @@
-from Bio.PDB import MMCIFParser, PDBIO
-from folding_studio.client import Client
-from folding_studio.query.boltz import BoltzQuery, BoltzParameters
-from pathlib import Path
-import gradio as gr
-import hashlib
+"""Folding Studio Demo App."""
+
 import logging
-import numpy as np
-import os
-import plotly.graph_objects as go
+
+import gradio as gr
+from folding_studio_data_models import FoldingModel
+from gradio_molecule3d import Molecule3D
 import pandas as pd
+import numpy as np
 from scipy.stats import spearmanr
+import plotly.graph_objects as go
 
-from molecule import molecule
+from folding_studio_demo.predict import predict
 
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.StreamHandler(),
-    ]
-)
 logger = logging.getLogger(__name__)
 
 
-def convert_cif_to_pdb(cif_path: str, pdb_path: str) -> None:
-    """Convert a .cif file to .pdb format using Biopython.
-    
-    Args:
-        cif_path (str): Path to input .cif file
-        pdb_path (str): Path to output .pdb file
-    """
-    # Parse the CIF file
-    parser = MMCIFParser()
-    structure = parser.get_structure("structure", cif_path)
-    
-    # Save as PDB
-    io = PDBIO()
-    io.set_structure(structure)
-    io.save(pdb_path)
-
-def call_boltz(seq_file: Path | str, api_key: str, output_dir: Path) -> None:
-    """Call Boltz prediction."""
-    # Initialize parameters with CLI-provided values
-    parameters = {
-        "recycling_steps": 3,
-        "sampling_steps": 200,
-        "diffusion_samples": 1,
-        "step_scale": 1.638,
-        "msa_pairing_strategy": "greedy",
-        "write_full_pae": False,
-        "write_full_pde": False,
-        "use_msa_server": True,
-        "seed": 0,
-        "custom_msa_paths": None,
+MOLECULE_REPS = [
+    {
+        "model": 0,
+        "chain": "",
+        "resname": "",
+        "style": "cartoon",
+        "color": "alphafold",
+        # "residue_range": "",
+        "around": 0,
+        "byres": False,
+        # "visible": False,
+        # "opacity": 0.5
     }
-    
-    # Create a client using API key
-    logger.info("Authenticating client with API key")
-    client = Client.from_api_key(api_key=api_key)
+]
 
-    # Define query
-    seq_file = Path(seq_file)
-    query = BoltzQuery.from_file(seq_file, query_name="gradio", parameters=BoltzParameters(**parameters))
-    query.save_parameters(output_dir)
+DEFAULT_PROTEIN_SEQ = ">protein description\nMALWMRLLPLLALLALWGPDPAAA"
 
-    logger.info("Payload: %s", query.payload)
+MODEL_CHOICES = [
+    # ("AlphaFold2", FoldingModel.AF2),
+    # ("OpenFold", FoldingModel.OPENFOLD),
+    # ("SoloSeq", FoldingModel.SOLOSEQ),
+    ("Boltz-1", FoldingModel.BOLTZ),
+    ("Chai-1", FoldingModel.CHAI),
+    ("Protenix", FoldingModel.PROTENIX),
+]
 
-    # Send a request
-    logger.info("Sending request to Folding Studio API")
-    response = client.send_request(query, project_code=os.environ["FOLDING_PROJECT_CODE"])
 
-    # Access confidence data
-    logger.info("Confidence data: %s", response.confidence_data)
+def sequence_input() -> gr.Textbox:
+    """Sequence input component.
 
-    response.download_results(output_dir=output_dir, force=True, unzip=True)
-    logger.info("Results downloaded to %s", output_dir)
+    Returns:
+        gr.Textbox: Sequence input component
+    """
+    sequence = gr.Textbox(
+        label="Protein Sequence",
+        value=DEFAULT_PROTEIN_SEQ,
+        lines=2,
+        placeholder="Enter a protein sequence or upload a FASTA file",
+    )
+    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:
+            return gr.Textbox()
+        try:
+            with open(file.name, "r") as f:
+                content = f.read().strip()
+            return gr.Textbox(value=content)
+        except Exception as e:
+            logger.error(f"Error reading file: {e}")
+            return gr.Textbox()
+
+    file_input.change(fn=_process_file, inputs=[file_input], outputs=[sequence])
+    return sequence
+
+
+def simple_prediction(api_key: str) -> None:
+    """Simple prediction tab.
 
-def predict(sequence: str, api_key: str) -> str:
-    """Predict protein structure from amino acid sequence using Boltz model.
-    
     Args:
-        sequence (str): Amino acid sequence to predict structure for
-        api_key (str): Folding API key
-        
-    Returns:
-        str: HTML iframe containing 3D molecular visualization
+        api_key (str): Folding Studio API key
     """
-    
-    # Set up unique output directory based on sequence hash
-    seq_id = hashlib.sha1(sequence.encode()).hexdigest()
-    seq_file = Path(f"sequence_{seq_id}.fasta")
-    _write_fasta_file(seq_file, sequence)
-    output_dir = Path(f"sequence_{seq_id}")
-    output_dir.mkdir(parents=True, exist_ok=True)
-    
-    # Check if prediction already exists
-    pred_cif = list(output_dir.rglob("*_model_0.cif"))
-    if not pred_cif:
-        # Run Boltz prediction
-        logger.info(f"Predicting {seq_file.stem}")
-        call_boltz(seq_file=seq_file, api_key=api_key, output_dir=output_dir)
-        logger.info("Prediction done. Output directory: %s", output_dir)
-    else:
-        logger.info("Prediction already exists. Output directory: %s", output_dir)
-
-    # output_dir = Path("boltz_results") # debug
-    # Convert output CIF to PDB
-    pred_cif = list(output_dir.rglob("*_model_0.cif"))[0]
-    logger.info("Output file: %s", pred_cif)
-    
-    converted_pdb_path = str(output_dir / "pred.pdb")
-    convert_cif_to_pdb(str(pred_cif), str(converted_pdb_path))
-    logger.info("Converted PDB file: %s", converted_pdb_path)
-
-
-    # Generate molecular visualization
-    mol = _create_molecule_visualization(
-        converted_pdb_path,
-        sequence,
-    )    
-
-    plddt_file = list(pred_cif.parent.glob("plddt_*.npz"))[0]
-    logger.info("plddt file: %s", plddt_file)
-    plddt_vals = np.load(plddt_file)["plddt"]
-
-    return _wrap_in_iframe(mol), add_plddt_plot(plddt_vals=plddt_vals)
-
-
-def _write_fasta_file(filepath: Path, sequence: str) -> None:
-    """Write sequence to FASTA file."""
-    with open(filepath, "w") as f:
-        f.write(f">A|protein\n{sequence}")
-
-
-def _create_molecule_visualization(pdb_path: Path, sequence: str) -> str:
-    """Create molecular visualization using molecule module."""
-    return molecule(
-        str(pdb_path),
-        lenSeqs=1,
-        num_res=len(sequence),
-        selectedResidues=list(range(1, len(sequence) + 1)),
-        allSeqs=[sequence],
-        sequences=[{
-            "Score": 0,
-            "RMSD": 0, 
-            "Recovery": 0,
-            "Mean pLDDT": 0,
-            "seq": sequence
-        }],
+    gr.Markdown(
+        """
+        ### 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.
+
+        Select a model to run the inference with and enter a protein sequence or upload a FASTA file.
+        """
     )
+    with gr.Row():
+        dropdown = gr.Dropdown(
+            label="Model",
+            choices=MODEL_CHOICES,
+            scale=0,
+            value=FoldingModel.BOLTZ,
+        )
+        with gr.Column():
+            sequence = sequence_input()
+
+    predict_btn = gr.Button("Predict")
+
+    with gr.Row():
+        mol_output = Molecule3D(label="Protein Structure", reps=MOLECULE_REPS)
+        metrics_plot = gr.Plot(label="pLDDT")
+
+    predict_btn.click(
+        fn=predict,
+        inputs=[sequence, api_key, dropdown],
+        outputs=[mol_output, metrics_plot],
+    )
+
+
+def model_comparison(api_key: str) -> None:
+    """Model comparison tab.
 
+    Args:
+        api_key (str): Folding Studio API key
+    """
 
-def _wrap_in_iframe(content: str) -> str:
-    """Wrap content in an HTML iframe with appropriate styling and permissions."""
-    return f"""<iframe 
-        name="result" 
-        style="width: 100%; height: 100vh;"
-        allow="midi; geolocation; microphone; camera; display-capture; encrypted-media;"
-        sandbox="allow-modals allow-forms allow-scripts allow-same-origin allow-popups allow-top-navigation-by-user-activation allow-downloads"
-        allowfullscreen=""
-        allowpaymentrequest=""
-        frameborder="0"
-        srcdoc='{content}'
-    ></iframe>"""
-
-def add_plddt_plot(plddt_vals: list[float]) -> str:
-    """Create a plot of metrics."""
-    visible = True
-    plddt_trace = go.Scatter(
-            x=np.arange(len(plddt_vals)),
-            y=plddt_vals,
-            hovertemplate="<i>pLDDT</i>: %{y:.2f} <br><i>Residue index:</i> %{x}<br>",
-            name="seq",
-            visible=visible,
+    with gr.Row():
+        model = gr.Dropdown(
+            label="Model",
+            choices=MODEL_CHOICES,
+            multiselect=True,
+            scale=0,
+            min_width=300,
+            value=[FoldingModel.BOLTZ, FoldingModel.CHAI, FoldingModel.PROTENIX],
         )
-    
-    plddt_fig = go.Figure(data=[plddt_trace])
-    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),
+        with gr.Column():
+            sequence = sequence_input()
+
+    predict_btn = gr.Button("Compare Models")
+
+    with gr.Row():
+        mol_output = Molecule3D(label="Protein Structure", reps=MOLECULE_REPS)
+        metrics_plot = gr.Plot(label="pLDDT")
+
+    predict_btn.click(
+        fn=predict,
+        inputs=[sequence, api_key, model],
+        outputs=[mol_output, metrics_plot],
     )
-    return plddt_fig
 
 def fake_predict_and_correlate(spr_data_with_scores: pd.DataFrame, score_cols: list[str]) -> tuple[pd.DataFrame, go.Figure]:
     """Fake predict structures of all complexes and correlate the results."""
@@ -243,31 +199,29 @@ def fake_predict_and_correlate(spr_data_with_scores: pd.DataFrame, score_cols: l
 
     return spr_data_with_scores[cols_to_show], corr_ranking_plot, corr_plot
 
-demo = gr.Blocks(title="Folding Studio: structure prediction with Boltz-1")
+def __main__():
+    with gr.Blocks(title="Folding Studio Demo") as demo:
+        gr.Markdown(
+            """
+            # Folding Studio: Harness the Power of Protein Folding 🧬
 
-with demo:
-    with gr.Tabs() as tabs:
-        with gr.TabItem("Inference"):
-            gr.Markdown("# Input")
-            with gr.Row():
-                with gr.Column():
-                    sequence = gr.Textbox(label="Sequence", value="")
-                    api_key = gr.Textbox(label="Folding API Key", type="password")
-            gr.Markdown("# Output")
-            with gr.Row():
-                predict_btn = gr.Button("Predict")
-            with gr.Row():
-                with gr.Column():
-                    mol_output = gr.HTML()
-                with gr.Column():
-                    metrics_plot = gr.Plot(label="pLDDT")
-                
-            predict_btn.click(
-                fn=predict,
-                inputs=[sequence, api_key],
-                outputs=[mol_output, metrics_plot]
-            )
-        with gr.TabItem("Correlations"):
+            Folding Studio is a platform for protein structure prediction.
+            It uses the latest AI-powered folding models to predict the structure of a protein.
+
+            Available models are : AlphaFold2, OpenFold, SoloSeq, Boltz-1, Chai and Protenix.
+
+            ## API Key
+            To use the Folding Studio API, you need to provide an API key.
+            You can get your API key by asking to the Folding Studio team.
+            """
+        )
+        api_key = gr.Textbox(label="Folding Studio API Key", type="password")
+        gr.Markdown("## Demo Usage")
+        with gr.Tab("🚀 Simple Prediction"):
+            simple_prediction(api_key)
+        with gr.Tab("📊 Model Comparison"):
+            model_comparison(api_key)
+        with gr.Tab("🔍 Correlations"):
             gr.Markdown("# Upload binding affinity data")
             spr_data_with_scores = pd.read_csv("spr_af_scores_mapped.csv")
             with gr.Row():
@@ -292,8 +246,6 @@ with demo:
                 "confidence_score_boltz",
                 "ptm_boltz",
                 "iptm_boltz", 
-                # "ligand_iptm_boltz",
-                # "protein_iptm_boltz",
                 "complex_plddt_boltz",
                 "complex_iplddt_boltz",
                 "complex_pde_boltz",
@@ -324,7 +276,4 @@ with demo:
                 outputs=[prediction_dataframe, correlation_ranking_plot, correlation_plot]
             )
 
-    
-
-demo.launch()
-
+    demo.launch()

folding_studio_demo/model_fasta_validators.py

@@ -0,0 +1,367 @@
+"""Utils for validating the FASTA files for the AF3 like models."""
+
+import logging
+import re
+import shutil
+from abc import abstractmethod
+from collections import defaultdict
+from enum import Enum
+from pathlib import Path
+
+from Bio import SeqIO
+
+
+class EntityType(str, Enum):
+    """Enum for the entity type of a given sequence."""
+
+    DNA = "dna"
+    RNA = "rna"
+    PROTEIN = "protein"
+    PEPTIDE = "peptide"
+    ION = "ion"
+    LIGAND = "ligand"
+    SMILES = "smiles"
+    CCD = "ccd"
+
+
+def get_entity_type(sequence: str) -> EntityType:
+    """Get the entity type of a given sequence.
+
+    The entity type is determined based on the sequence composition.
+
+    Args:
+        sequence (str): The input sequence.
+
+    Returns:
+        EntityType: The entity type of the input sequence.
+    """
+    DNA_SEQUENCE_SET = set("ACGT")
+    RNA_SEQUENCE_SET = set("ACGU")
+    PROTEIN_SEQUENCE_SET = set("ACDEFGHIKLMNPQRSTVWY")
+
+    # Detect IONS (e.g., Mg2+, Na+, Cl-)
+    if re.fullmatch(r"[A-Za-z]{1,2}[\d\+\-]*", sequence):
+        return EntityType.ION
+
+    # Detect DNA
+    if set(sequence.upper()).issubset(DNA_SEQUENCE_SET):
+        return EntityType.DNA
+
+    # Detect RNA
+    elif set(sequence.upper()).issubset(RNA_SEQUENCE_SET):
+        return EntityType.RNA
+
+    # Detect PROTEIN
+    elif set(sequence.upper()).issubset(PROTEIN_SEQUENCE_SET):
+        return EntityType.PROTEIN
+
+    # Default to LIGAND
+    return EntityType.LIGAND
+
+
+def has_multiple_chains(header: str) -> bool:
+    """Check if a given header contains multiple chains in RCSB format.
+
+    A header with multiple chains will have the following in the header:
+    ```
+    Chains A, B, C, ...
+    ```
+    where `A`, `B`, `C`, ... are the chain identifiers.
+
+
+    Args:
+        header (str): The input header string containing chain information.
+
+    Returns:
+        bool: True if the header contains multiple chains, False otherwise.
+    """
+    match = re.search(r"chains?\s+([A-Za-z, ]+)", header, re.I)
+    return len(match.group(1).replace(" ", "").split(",")) > 1 if match else False
+
+
+class BaseFastaValidator:
+    """Base class for validating FASTA files."""
+
+    @abstractmethod
+    def is_valid_fasta(self, fasta_path: Path) -> tuple[bool, str | None]:
+        """Validate whether a given FASTA file follows the required format.
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
+        """
+        raise NotImplementedError("Subclasses must implement this method")
+
+    @abstractmethod
+    def transform_fasta(self, fasta_path: Path) -> str:
+        """Transform a FASTA file into the required format.
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            Transformed FASTA content in the required format.
+        """
+        raise NotImplementedError("Subclasses must implement this method")
+
+    def process_directory(self, input_dir: str, output_dir: str) -> None:
+        """Process all FASTA files in the input directory, validate or transform them, and save them to the output directory.
+
+        Args:
+            input_dir (str): Path to the directory containing FASTA files.
+            output_dir (str): Path to the output directory where processed files will be saved.
+        """
+
+        output_path = Path(output_dir)
+        output_path.mkdir(parents=True, exist_ok=True)
+
+        for fasta_file in Path(input_dir).glob("*.fasta"):
+            output_file = output_path / fasta_file.name
+            if has_multiple_chains(fasta_file.read_text()):
+                logging.warning(
+                    f"Skipping {fasta_file} because it contains multiple chains in a single sequence.\n"
+                    "Please split multiple chains into separate sequences using the following format:\n"
+                    ">Chain A\n"
+                    "MTEIVLKFL...\n"
+                    ">Chain B\n"
+                    "MTEIVLKFL...\n\n"
+                    "Instead of:\n"
+                    ">Chains A, B\n"
+                    "MTEIVLKFL..."
+                )
+                continue
+            if self.is_valid_fasta(fasta_file):
+                shutil.copy(fasta_file, output_file)
+            else:
+                transformed_content = self.transform_fasta(fasta_file)
+                output_file.write_text(transformed_content)
+
+
+class BoltzFastaValidator(BaseFastaValidator):
+    """Validate whether a given FASTA file follows the required format for Boltz."""
+
+    SUPPORTED_ENTITY_TYPES = {
+        EntityType.PROTEIN,
+        EntityType.RNA,
+        EntityType.DNA,
+        EntityType.SMILES,
+        EntityType.CCD,
+    }
+
+    def is_valid_fasta(self, fasta_path: Path) -> tuple[bool, str | None]:
+        """Validate whether a given FASTA file follows the required format.
+
+        The expected FASTA header format is:
+        ```
+        >CHAIN_ID|ENTITY_TYPE
+        ```
+        where `ENTITY_TYPE` must be one of: "protein", "rna", "dna", "smiles" or "ccd".
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
+        """
+        with fasta_path.open("r") as f:
+            for record in SeqIO.parse(f, "fasta"):
+                header_parts = record.id.split("|")
+                if not (1 < len(header_parts) <= 3):
+                    msg = "BOLTZ Validation Error: Invalid header format. Expected '>CHAIN_ID|ENTITY_TYPE'"
+                    return False, msg
+                if header_parts[1].lower() not in self.SUPPORTED_ENTITY_TYPES:
+                    return (
+                        False,
+                        f"BOLTZ Validation Error: Invalid entity type '{header_parts[1]}'. Supported types: {', '.join(self.SUPPORTED_ENTITY_TYPES)}",
+                    )
+        return True, None
+
+    def transform_fasta(self, fasta_path: Path) -> str:
+        """Transform a FASTA file into the '>CHAIN_ID|ENTITY_TYPE|MSA_ID' format.
+
+        This function extracts chain identifiers from the FASTA header and determines
+        the entity type (DNA, RNA, or PROTEIN) based on the sequence composition.
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            Transformed FASTA content in the required format.
+        """
+        transformed_lines = []
+
+        with fasta_path.open("r") as f:
+            for record_index, record in enumerate(SeqIO.parse(f, "fasta")):
+                chain = chr(ord("A") + record_index)
+                # extract entity type
+                entity_type = get_entity_type(str(record.seq))
+                transformed_lines.append(f">{chain.upper()}|{entity_type.value}")
+                # append sequence
+                transformed_lines.append(str(record.seq))
+
+        return "\n".join(transformed_lines)
+
+
+class ChaiFastaValidator(BaseFastaValidator):
+    """Validate whether a given FASTA file follows the required format for Chai."""
+
+    SUPPORTED_ENTITY_TYPES = EntityType.__members__.values()
+
+    def is_valid_fasta(self, fasta_path: Path) -> tuple[bool, str | None]:
+        """Validate whether a given FASTA file follows the required format.
+
+        The expected FASTA header format is:
+        ```
+        >ENTITY_TYPE|name=NAME
+        ```
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
+        """
+
+        seen_names = set()
+        with fasta_path.open("r") as f:
+            for record in SeqIO.parse(f, "fasta"):
+                # validate header format
+                match = re.match(r"^([A-Za-z]+)\|name=([\w\-]+)$", record.description)
+                if not match:
+                    return (
+                        False,
+                        "CHAI Validation Error: Invalid header format. Expected '>ENTITY_TYPE|name=NAME'",
+                    )
+                # validate entity type
+                entity_type, name = match.groups()
+                if entity_type not in self.SUPPORTED_ENTITY_TYPES or not name:
+                    return (
+                        False,
+                        f"CHAI Validation Error: Invalid entity type '{entity_type}'. Supported types: {', '.join(self.SUPPORTED_ENTITY_TYPES)}",
+                    )
+                # check uniqueness of name
+                if name in seen_names:
+                    return (
+                        False,
+                        f"CHAI Validation Error: Duplicate name '{name}'. Each sequence must have a unique name",
+                    )
+                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)}'",
+                    )
+
+        return True, None
+
+    def transform_fasta(self, fasta_path: Path) -> str:
+        """Transform a FASTA file into the '>TYPE|name=NAME' format by ensuring each main header
+        is unique (adding a number if necessary).
+
+        The expected output format is:
+            '>protein|name=NAME'
+            'SEQUENCE'
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            Transformed FASTA content in the required Chai format.
+        """
+        transformed_lines = []
+        header_map = {}
+
+        with fasta_path.open("r") as f:
+            for record in SeqIO.parse(f, "fasta"):
+                main_header = record.description.split("|")[0].strip()
+
+                if main_header not in header_map:
+                    header_map[main_header] = 1
+                    updated_header = main_header
+
+                else:
+                    header_map[main_header] += 1
+                    updated_header = main_header + "_" + str(header_map[main_header])
+
+                entity_type = get_entity_type(str(record.seq))
+                header = f">{entity_type.value}|name={updated_header}"
+
+                transformed_lines.append(header)
+                transformed_lines.append(str(record.seq))
+
+        return "\n".join(transformed_lines)
+
+
+class ProtenixFastaValidator(BaseFastaValidator):
+    """Validate whether a given FASTA file follows the required format for Protenix."""
+
+    def is_valid_fasta(self, fasta_path: Path) -> tuple[bool, str | None]:
+        """Validate whether a given FASTA file follows the required format.
+
+        The expected FASTA header format is:
+        ```
+        > UNIQUE ID[|...]
+        ```
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            tuple[bool, str | None]: Tuple containing a boolean indicating if the format is correct and an error message if not
+        """
+        seen_headers = set()
+
+        with fasta_path.open("r") as f:
+            for record in SeqIO.parse(f, "fasta"):
+                main_header = record.description.split("|")[0].strip()
+                if main_header in seen_headers:
+                    return (
+                        False,
+                        f"PROTENIX Validation Error: Duplicate header '{main_header}'. Each sequence must have a unique header",
+                    )
+                seen_headers.add(main_header)
+
+        return True, None
+
+    def transform_fasta(self, fasta_path: Path) -> str:
+        """Transform a FASTA file into the '>NAME|Chain X' format by ensuring each main header
+        is unique (adding a number if necessary).
+
+        The expected output format is:
+            '>protein_1 | Chain A'
+            'SEQUENCE'
+            '>protein_2 | Chain B'
+            'SEQUENCE'
+
+        Args:
+            fasta_path (Path): Path to the FASTA file.
+
+        Returns:
+            Transformed FASTA content in the required Protenix format.
+        """
+        transformed_lines = []
+        header_count = defaultdict(int)
+
+        with fasta_path.open("r") as f:
+            for record in SeqIO.parse(f, "fasta"):
+                header_parts = [part.strip() for part in record.description.split("|")]
+                main_header = header_parts[0]
+
+                # Ensure unique headers
+                header_count[main_header] += 1
+                updated_main_header = (
+                    f"{main_header}_{header_count[main_header]}"
+                    if header_count[main_header] > 1
+                    else main_header
+                )
+
+                transformed_lines.append(f">{updated_main_header}")
+                transformed_lines.append(str(record.seq))
+
+        return "\n".join(transformed_lines)

molecule.py → folding_studio_demo/molecule.py

@@ -1,15 +1,16 @@
-import logging 
+import logging
 
 # Configure logging
 logging.basicConfig(
     level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
+    format="%(asctime)s - %(levelname)s - %(message)s",
     handlers=[
         logging.StreamHandler(),
-    ]
+    ],
 )
 logger = logging.getLogger(__name__)
 
+
 def read_mol(molpath):
     with open(molpath, "r") as fp:
         lines = fp.readlines()
@@ -19,10 +20,7 @@ def read_mol(molpath):
     return mol
 
 
-def molecule(
-    input_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences
-):
-
+def molecule(input_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences):
     options = ""
     pred_mol = "["
     seqdata = "{"
@@ -265,5 +263,5 @@ def molecule(
         </script>
         </body></html>"""
     )
-    
-    return x
+
+    return x

folding_studio_demo/predict.py

@@ -0,0 +1,279 @@
+"""Predict protein structure using Folding Studio."""
+
+import hashlib
+import logging
+import os
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import plotly.graph_objects as go
+from Bio import SeqIO
+from Bio.PDB import PDBIO, MMCIFParser
+from folding_studio.client import Client
+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 FoldingModel
+
+from folding_studio_demo.model_fasta_validators import (
+    BaseFastaValidator,
+    BoltzFastaValidator,
+    ChaiFastaValidator,
+    ProtenixFastaValidator,
+)
+
+logger = logging.getLogger(__name__)
+
+SEQUENCE_DIR = Path("sequences")
+SEQUENCE_DIR.mkdir(parents=True, exist_ok=True)
+
+OUTPUT_DIR = Path("output")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def convert_cif_to_pdb(cif_path: str, pdb_path: str) -> None:
+    """Convert a .cif file to .pdb format using Biopython.
+
+    Args:
+        cif_path (str): Path to input .cif file
+        pdb_path (str): Path to output .pdb file
+    """
+    # Parse the CIF file
+    parser = MMCIFParser()
+    structure = parser.get_structure("structure", cif_path)
+
+    # Save as PDB
+    io = PDBIO()
+    io.set_structure(structure)
+    io.save(pdb_path)
+
+
+def add_plddt_plot(plddt_vals: list[float]) -> str:
+    """Create a plot of metrics."""
+    visible = True
+    plddt_trace = go.Scatter(
+        x=np.arange(len(plddt_vals)),
+        y=plddt_vals,
+        hovertemplate="<i>pLDDT</i>: %{y:.2f} <br><i>Residue index:</i> %{x}<br>",
+        name="seq",
+        visible=visible,
+    )
+
+    plddt_fig = go.Figure(data=[plddt_trace])
+    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),
+    )
+    return plddt_fig
+
+
+def _write_fasta_file(
+    sequence: str, directory: Path = SEQUENCE_DIR
+) -> tuple[str, Path]:
+    """Write sequence to FASTA file.
+
+    Args:
+        sequence (str): Sequence to write to FASTA file
+        directory (Path): Directory to write FASTA file to (default: SEQUENCE_DIR)
+
+    Returns:
+        tuple[str, Path]: Tuple containing the sequence ID and the path to the FASTA file
+    """
+    seq_id = hashlib.sha1(sequence.encode()).hexdigest()
+    seq_file = directory / f"sequence_{seq_id}.fasta"
+    with open(seq_file, "w") as f:
+        f.write(sequence)
+    return seq_id, seq_file
+
+
+class AF3Model:
+    def __init__(
+        self, api_key: str, model_name: str, query: Query, validator: BaseFastaValidator
+    ):
+        self.api_key = api_key
+        self.model_name = model_name
+        self.query = query
+        self.validator = validator
+
+    def call(self, seq_file: Path | str, output_dir: Path) -> None:
+        """Predict protein structure from amino acid sequence using AF3 model.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file containing amino acid sequence
+            output_dir (Path): Path to output directory
+        """
+        # Validate FASTA format before calling
+        is_valid, error_msg = self.check_file_description(seq_file)
+        if not is_valid:
+            logger.error(error_msg)
+            raise gr.Error(error_msg)
+
+        # Create a client using API key
+        logger.info("Authenticating client with API key")
+        client = Client.from_api_key(api_key=self.api_key)
+
+        # Define query
+        query: Query = self.query.from_file(path=seq_file, query_name="gradio")
+        query.save_parameters(output_dir)
+
+        logger.info("Payload: %s", query.payload)
+
+        # Send a request
+        logger.info(f"Sending {self.model_name} request to Folding Studio API")
+        response = client.send_request(
+            query, project_code=os.environ["FOLDING_PROJECT_CODE"]
+        )
+
+        # Access confidence data
+        logger.info("Confidence data: %s", response.confidence_data)
+
+        response.download_results(output_dir=output_dir, force=True, unzip=True)
+        logger.info("Results downloaded to %s", output_dir)
+
+    def format_fasta(self, sequence: str) -> str:
+        """Format sequence to FASTA format."""
+        return f">{self.model_name}\n{sequence}"
+
+    def predictions(self, output_dir: Path) -> list[Path]:
+        """Get the path to the prediction."""
+        raise NotImplementedError("Not implemented")
+
+    def has_prediction(self, output_dir: Path) -> bool:
+        """Check if prediction exists in output directory."""
+        return any(self.predictions(output_dir))
+
+    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
+        """
+        input_rep = list(SeqIO.parse(seq_file, "fasta"))
+        if not input_rep:
+            error_msg = f"{self.model_name.upper()} Validation Error: No sequence found"
+            return False, error_msg
+
+        is_valid, error_msg = self.validator.is_valid_fasta(seq_file)
+        if not is_valid:
+            return False, error_msg
+
+        return True, None
+
+
+class ChaiModel(AF3Model):
+    def __init__(self, api_key: str):
+        super().__init__(api_key, "Chai", ChaiQuery, ChaiFastaValidator())
+
+    def call(self, seq_file: Path | str, output_dir: Path) -> None:
+        """Predict protein structure from amino acid sequence using Chai model.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file containing amino acid sequence
+            output_dir (Path): Path to output directory
+        """
+        super().call(seq_file, output_dir)
+
+    def predictions(self, output_dir: Path) -> list[Path]:
+        """Get the path to the prediction."""
+        return list(output_dir.rglob("*_model_[0-9].cif"))
+
+
+class ProtenixModel(AF3Model):
+    def __init__(self, api_key: str):
+        super().__init__(api_key, "Protenix", ProtenixQuery, ProtenixFastaValidator())
+
+    def call(self, seq_file: Path | str, output_dir: Path) -> None:
+        """Predict protein structure from amino acid sequence using Protenix model.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file containing amino acid sequence
+            output_dir (Path): Path to output directory
+        """
+        super().call(seq_file, output_dir)
+
+    def predictions(self, output_dir: Path) -> list[Path]:
+        """Get the path to the prediction."""
+        return list(output_dir.rglob("*_model_[0-9].cif"))
+
+
+class BoltzModel(AF3Model):
+    def __init__(self, api_key: str):
+        super().__init__(api_key, "Boltz", BoltzQuery, BoltzFastaValidator())
+
+    def call(self, seq_file: Path | str, output_dir: Path) -> None:
+        """Predict protein structure from amino acid sequence using Boltz model.
+
+        Args:
+            seq_file (Path | str): Path to FASTA file containing amino acid sequence
+            output_dir (Path): Path to output directory
+        """
+
+        super().call(seq_file, output_dir)
+
+    def predictions(self, output_dir: Path) -> list[Path]:
+        """Get the path to the prediction."""
+        return list(output_dir.rglob("*_model_[0-9].cif"))
+
+
+def predict(sequence: str, api_key: str, model_type: FoldingModel) -> tuple[str, str]:
+    """Predict protein structure from amino acid sequence using Boltz model.
+
+    Args:
+        sequence (str): Amino acid sequence to predict structure for
+        api_key (str): Folding API key
+        model (FoldingModel): Folding model to use
+
+    Returns:
+        tuple[str, str]: Tuple containing the path to the PDB file and the pLDDT plot
+    """
+
+    # Set up unique output directory based on sequence hash
+    seq_id, seq_file = _write_fasta_file(sequence)
+    output_dir = OUTPUT_DIR / seq_id / model_type
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    if model_type == FoldingModel.BOLTZ:
+        model = BoltzModel(api_key)
+    elif model_type == FoldingModel.CHAI:
+        model = ChaiModel(api_key)
+    elif model_type == FoldingModel.PROTENIX:
+        model = ProtenixModel(api_key)
+    else:
+        raise ValueError(f"Model {model_type} not supported")
+
+    # Check if prediction already exists
+    if not model.has_prediction(output_dir):
+        # Run Boltz prediction
+        logger.info(f"Predicting {seq_id}")
+        model.call(seq_file=seq_file, output_dir=output_dir)
+        logger.info("Prediction done. Output directory: %s", output_dir)
+    else:
+        logger.info("Prediction already exists. Output directory: %s", output_dir)
+
+    # output_dir = Path("boltz_results")  # debug
+
+    # Convert output CIF to PDB
+    if not model.has_prediction(output_dir):
+        raise gr.Error("No prediction found")
+
+    pred_cif = model.predictions(output_dir)[0]
+    logger.info("Output file: %s", pred_cif)
+
+    converted_pdb_path = str(output_dir / f"pred_{seq_id}.pdb")
+    convert_cif_to_pdb(str(pred_cif), str(converted_pdb_path))
+    logger.info("Converted PDB file: %s", converted_pdb_path)
+
+    plddt_file = list(pred_cif.parent.glob("plddt_*.npz"))[0]
+    logger.info("plddt file: %s", plddt_file)
+    plddt_vals = np.load(plddt_file)["plddt"]
+
+    return converted_pdb_path, add_plddt_plot(plddt_vals=plddt_vals)

pyproject.toml

@@ -1,14 +1,27 @@
 [project]
-name = "fs"
+name = "folding-studio-demo"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
     "gradio==5.30.0",
+    "gradio-molecule3d>=0.0.7",
     "ipython>=9.2.0",
     "jupyter>=1.1.1",
     "numpy>=2.2.6",
     "plotly>=6.1.1",
     "scipy>=1.15.3",
+    "folding-studio",
+    "biopython>=1.85",
 ]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.uv.sources]
+folding-studio = { path = "./folding-studio" }
+
+[project.scripts]
+folding-studio-demo = "folding_studio_demo.app:__main__"

uv.lock

@@ -183,6 +183,36 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/50/cd/30110dc0ffcf3b131156077b90e9f60ed75711223f306da4db08eff8403b/beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b", size = 187285 },
 ]
 
+[[package]]
+name = "biopython"
+version = "1.85"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/db/ca/1d5fab0fedaf5c2f376d9746d447cdce04241c433602c3861693361ce54c/biopython-1.85.tar.gz", hash = "sha256:5dafab74059de4e78f49f6b5684eddae6e7ce46f09cfa059c1d1339e8b1ea0a6", size = 19909902 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/73/c3a1323a3fe0d07212b09c04fb903e2cbb98aebfbb58e55e8717473e1bc0/biopython-1.85-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:db8822adab0cd75a6e6ae845acf312addd8eab5f9b731c191454b961fc2c2cdc", size = 2787585 },
+    { url = "https://files.pythonhosted.org/packages/ff/cf/299524e896fa49beb7588143e1509cce4848572215ebafb8eea83a861820/biopython-1.85-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8e2bbe58cc1a592b239ef6d68396745d3fbfaafc668ce38283871d8ff070dbab", size = 2765608 },
+    { url = "https://files.pythonhosted.org/packages/b1/dd/be3e95b72a35ee6d52c84412e15af828951e5c69175080d4619985fd54ce/biopython-1.85-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5916eb56df7ecd4a3babc07a48d4894c40cfb45dc18ccda1c148d0131017ce04", size = 3237552 },
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