gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/torchdrug/references/protein_modeling.md

Protein Modeling

Overview

TorchDrug provides extensive support for protein-related tasks including sequence analysis, structure prediction, property prediction, and protein-protein interactions. Proteins are represented as graphs where nodes are amino acid residues and edges represent spatial or sequential relationships.

Available Datasets

Protein Function Prediction

Enzyme Function:

• EnzymeCommission (17,562 proteins): EC number classification (7 levels)
• BetaLactamase (5,864 sequences): Enzyme activity prediction

Protein Characteristics:

• Fluorescence (54,025 sequences): GFP fluorescence intensity
• Stability (53,614 sequences): Thermostability prediction
• Solubility (62,478 sequences): Protein solubility classification
• BinaryLocalization (22,168 proteins): Subcellular localization (membrane vs. soluble)
• SubcellularLocalization (8,943 proteins): 10-class localization prediction

Gene Ontology:

• GeneOntology (46,796 proteins): GO term prediction across biological process, molecular function, and cellular component

Protein Structure Prediction

• Fold (16,712 proteins): Structural fold classification (1,195 classes)
• SecondaryStructure (8,678 proteins): 3-state or 8-state secondary structure prediction
• ContactPrediction via ProteinNet: Residue-residue contact maps

Protein Interaction

Protein-Protein Interactions:

• HumanPPI (1,412 proteins, 6,584 interactions): Human protein interaction network
• YeastPPI (2,018 proteins, 6,451 interactions): Yeast protein interaction network
• PPIAffinity (2,156 protein pairs): Binding affinity measurements

Protein-Ligand Binding:

• BindingDB (~1.5M entries): Comprehensive binding affinity database
• PDBBind (20,000+ complexes): 3D structure-based binding data
  • Refined set: High-quality crystal structures
  • Core set: Diverse benchmark set

Large-Scale Protein Databases

• AlphaFoldDB: Access to 200M+ predicted protein structures
• ProteinNet: Standardized dataset for structure prediction

Task Types

NodePropertyPrediction

Predict properties at the residue (node) level, such as secondary structure or contact maps.

Use Cases:

• Secondary structure prediction (helix, sheet, coil)
• Residue-level disorder prediction
• Post-translational modification sites
• Binding site prediction

PropertyPrediction

Predict protein-level properties like function, stability, or localization.

Use Cases:

• Enzyme function classification
• Subcellular localization
• Protein stability prediction
• Gene ontology term prediction

InteractionPrediction

Predict interactions between protein pairs or protein-ligand pairs.

Key Features:

• Handles both sequence and structure inputs
• Supports symmetric (PPI) and asymmetric (protein-ligand) interactions
• Multiple negative sampling strategies

ContactPrediction

Specialized task for predicting spatial proximity between residues in folded structures.

Applications:

• Structure prediction from sequence
• Protein folding pathway analysis
• Validation of predicted structures

Protein Representation Models

Sequence-Based Models

ESM (Evolutionary Scale Modeling):

• Pre-trained transformer model on 250M sequences
• State-of-the-art for sequence-only tasks
• Available in multiple sizes (ESM-1b, ESM-2)
• Captures evolutionary and structural information

ProteinBERT:

• BERT-style masked language model
• Pre-trained on UniProt sequences
• Good for transfer learning

ProteinLSTM:

• Bidirectional LSTM for sequence encoding
• Lightweight and fast
• Good baseline for sequence tasks

ProteinCNN / ProteinResNet:

• Convolutional architectures
• Capture local sequence patterns
• Faster than transformer models

Structure-Based Models

GearNet (Geometry-Aware Relational Graph Network):

• Incorporates 3D geometric information
• Edge types based on sequential, radius, and K-nearest neighbors
• State-of-the-art for structure-based tasks
• Supports both backbone and full-atom representations

GCN/GAT/GIN on Protein Graphs:

• Standard GNN architectures adapted for proteins
• Flexible edge definitions (sequence, spatial, contact)

SchNet:

• Continuous-filter convolutions
• Handles 3D coordinates directly
• Good for structure prediction and protein-ligand binding

Feature-Based Models

Statistic Features:

• Amino acid composition
• Sequence length statistics
• Motif counts

Physicochemical Features:

• Hydrophobicity scales
• Charge properties
• Secondary structure propensity
• Molecular weight, pI

Protein Graph Construction

Edge Types

Sequential Edges:

• Connect adjacent residues in sequence
• Captures primary structure

Spatial Edges:

• K-nearest neighbors in 3D space
• Radius cutoff (e.g., Cα atoms within 10Å)
• Captures tertiary structure

Contact Edges:

• Based on heavy atom distances
• Typically < 8Å threshold

Node Features

Residue Identity:

• One-hot encoding of 20 amino acids
• Learned embeddings

Position Information:

• 3D coordinates (Cα, N, C, O)
• Backbone angles (phi, psi, omega)
• Relative spatial position encodings

Physicochemical Properties:

• Hydrophobicity
• Charge
• Size
• Secondary structure

Training Workflows

Pre-training Strategies

Self-Supervised Pre-training:

• Masked residue prediction (like BERT)
• Distance prediction between residues
• Angle prediction (phi, psi, omega)
• Dihedral angle prediction
• Contact map prediction

Pre-trained Model Usage:

from torchdrug import models

# Load pre-trained ESM
model = models.ESM(path="esm1b_t33_650M_UR50S.pt")

# Fine-tune on downstream task
task = tasks.PropertyPrediction(
    model, task=["stability"],
    criterion="mse", metric=["mae", "rmse"]
)

Multi-Task Learning

Train on multiple related tasks simultaneously:

• Joint prediction of function, localization, and stability
• Improves generalization and data efficiency
• Shares representations across tasks

Best Practices

For Sequence-Only Tasks:

1. Start with pre-trained ESM or ProteinBERT
2. Fine-tune with small learning rate (1e-5 to 1e-4)
3. Use frozen embeddings for small datasets
4. Apply dropout for regularization

For Structure-Based Tasks:

1. Use GearNet with multiple edge types
2. Include geometric features (angles, dihedrals)
3. Pre-train on large structure databases
4. Use data augmentation (rotations, crops)

For Small Datasets:

1. Transfer learning from pre-trained models
2. Multi-task learning with related tasks
3. Data augmentation (sequence mutations, structure perturbations)
4. Strong regularization (dropout, weight decay)

Common Use Cases

Enzyme Engineering

• Predict enzyme activity from sequence
• Design mutations to improve stability
• Screen for desired catalytic properties

Antibody Design

• Predict binding affinity
• Optimize antibody sequences
• Predict immunogenicity

Drug Target Identification

• Predict protein function
• Identify druggable sites
• Analyze protein-ligand interactions

Protein Structure Prediction

• Predict secondary structure from sequence
• Generate contact maps for tertiary structure
• Refine AlphaFold predictions

Integration with Other Tools

AlphaFold Integration

Load AlphaFold-predicted structures:

from torchdrug import data

# Load AlphaFold structure
protein = data.Protein.from_pdb("alphafold_structure.pdb")

# Use in TorchDrug workflows

ESMFold Integration

Use ESMFold for structure prediction, then analyze with TorchDrug models.

Rosetta/PyRosetta

Generate structures with Rosetta, import to TorchDrug for analysis.