Chapter 6: Structure Prediction from Sequence: SWISS-MODEL Homology & AlphaFold2 / ColabFold
Chapter 6: Structure Prediction from Sequence: SWISS-MODEL Homology & AlphaFold2 / ColabFold
Overcoming crystal structure absences: homology modeling using SWISS-MODEL and MODELLER; AI-driven structure prediction with AlphaFold2 and ColabFold; validating target models via pLDDT confidence scores, PAE error matrices, and Ramachandran plots.
1. Homology Modeling vs. AI-Driven Structure Prediction
When an experimental crystal or cryo-EM structure is unavailable in the PDB, computational structural biology offers two primary routes:
Homology Modeling (SWISS-MODEL / MODELLER)
Aligns query sequence against homologous PDB templates. Highly reliable when sequence identity $>40\%$.
- Template search with BLAST / HHblits
- Rigid backbone coordinate copying
- Loop modeling & sidechain rotamer placement
- Energy minimization via CHARMM force field
Deep Learning (AlphaFold2 & ColabFold)
Evoformer attention mechanisms operating on Multiple Sequence Alignments (MSAs) and pair representations.
- Near-experimental atomic accuracy (RMSD $\approx 1.5\text{ \AA}$)
- Predicts de novo folds without homologous crystal templates
- Generates local per-residue pLDDT confidence
- Generates inter-residue PAE error matrices
2. Evaluating AlphaFold Predicted Models for Docking
Not all regions of an AlphaFold predicted structure are suitable for molecular docking. You must evaluate structural metrics prior to grid box placement:
| pLDDT Range | Confidence Level | Structural Interpretation | Docking Feasibility |
|---|---|---|---|
| > 90 | Very High | High-accuracy backbone & sidechain rotamers | Excellent for targeted docking |
| 70 – 90 | Confident | Reliable backbone; minor surface sidechain variance | Suitable with flexible residue sampling |
| 50 – 70 | Low | Tentative backbone fold; flexible loops | Caution: high false-positive risk |
| < 50 | Very Low | Intrinsically Disordered Region (IDR) / ribbon-like | Unusable; strip IDRs before docking |
Stereochemical Validation: Always run PROCHECK or MolProbity to verify that $>90\%$ of residues reside in favored regions of the Ramachandran plot and zero steric clashes exist before converting the model into PDBQT.
Common Pitfalls & Troubleshooting Checklist
- Java Heap Exhaustion (GATK / Picard): If processes terminate with
java.lang.OutOfMemoryError, set your environment variable explicitly:export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp". - Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with
samtools index -@ 4 sample.bam. - Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority:
conda config --set channel_priority strict. - Chromosome Naming Contamination: Always verify whether reference FASTA headers use
chr1or1; divergent naming causes silent alignment drops.