add dockerfile and methods for secretion scores

Dockerfile.secretion

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+# Use official Rosetta as base image
+FROM rosettacommons/rosetta:latest
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    wget \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set up working directory
+WORKDIR /app
+
+# Copy Aggrescan3D package
+COPY aggrescan3d /app/aggrescan3d
+
+# Install Aggrescan3D and its dependencies
+RUN pip install --no-cache-dir /app/aggrescan3d
+
+# Default command
+CMD ["/bin/bash"]

secretion_scores.py

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+from __future__ import annotations
+import pathlib
+import pyrosetta
+from pyrosetta.rosetta.protocols.protein_interface_design import movers
+
+import aggrescan
+
+pyrosetta.init()
+
+def get_sap_scores(structure_path: str | pathlib.Path) -> list[float]:
+    """Calculate per-residue SAP scores.
+
+    Args:
+        structure_path: Path to the structure file.
+
+    Returns:
+        Dictionary of per-residue SAP scores.
+    """
+    pose = pyrosetta.pose_from_file(str(structure_path))
+    all_residues_selector = (
+        pyrosetta.rosetta.core.select.residue_selector.TrueResidueSelector()
+    )
+    per_res_sap_scores = (
+        pyrosetta.rosetta.core.pack.guidance_scoreterms.sap.calculate_per_res_sap(
+            pose,
+            all_residues_selector,
+            all_residues_selector,
+            all_residues_selector,
+        )
+    )
+    return list(per_res_sap_scores)
+
+
+def get_dG_ins_pred_scores(structure_path: str | pathlib.Path) -> list[float]:
+    """Calculate per-residue dG_ins_pred scores.
+
+    The dGins,pred is predicted over sub-sequences using SecretionOptimizationMover.
+    For middle residues, a window size of 19 is used. For residues near sequence ends,
+    smaller windows are used to ensure accurate scoring.
+    
+    Args:
+        structure_path: Path to the structure file.
+        
+    Returns:
+        List of per-residue dG_ins scores.
+    """
+    pose = pyrosetta.pose_from_file(str(structure_path))
+    num_chains = pose.num_chains()
+    secoptmover = movers.SecretionOptimizationMover()
+    max_window_size = 19
+    per_res_scores = []
+    
+    for chain_id in range(1, num_chains + 1):
+        sequence = pose.chain_sequence(chain_id)
+        for i in range(len(sequence)):
+            # Calculate window boundaries
+            if i < max_window_size // 2:
+                # Near start: use smaller window
+                start = 0
+                end = i + (max_window_size // 2) + 1
+            elif i > len(sequence) - (max_window_size // 2) - 1:
+                # Near end: use smaller window
+                end = len(sequence)
+                start = i - (max_window_size // 2)
+            else:
+                # Middle: use full window size
+                start = i - (max_window_size // 2)
+                end = i + (max_window_size // 2) + 1
+                
+            sub_seq = sequence[start:end]
+            dg_ins = secoptmover.dG_ins_for_window(sub_seq)
+            per_res_scores.append(dg_ins)
+        
+    return per_res_scores
+
+
+def get_aggrescan_scores(structure_path: str | pathlib.Path) -> dict[str, float]:
+    """Calculation of Aggrescan scores.
+
+    Args:
+        structure_path: Path to the structure file.
+
+    Returns:
+        Dictionary of aggrescan scores.
+    """
+    agg_residues = aggrescan.run_aggrescan3d(str(structure_path))
+    agg_scores = [r.score for r in agg_residues]
+    return agg_scores