Curriculum Modules
Click on any chapter below to explore complete code, stereochemical diagrams, and docking protocols.
1: Protein 3D Architecture, Coordinate Formats & PDB/mmCIF
Peptide bond geometry, Ramachandran dihedral angles, secondary structure motifs, PDB legacy format limitations, and mmCIF macromolecular data structures.
2: Molecular Visualization with PyMOL & ChimeraX
PyMOL CLI scripting, structural selections, ray tracing, cartoon/surface models, ligand contact pockets, and UCSF ChimeraX cryo-EM density mapping.
3: Classical Homology Modeling & Template Refinement
Comparative modeling principles, target-template alignments, SWISS-MODEL, MODELLER spatial restraint satisfaction, energy minimization, and PROCHECK validation.
4: The AlphaFold Revolution: Evoformer & Invariant Point Attention
AlphaFold2 deep learning architecture: MSA representation, Pair representation, triangular self-attention, and the 3D Structure Module with Invariant Point Attention (IPA).
5: Interpreting Structural Confidence: pLDDT, PAE & Disorder
Per-residue confidence metric (pLDDT bands), Predicted Aligned Error (PAE) matrices, domain orientations, and intrinsically disordered regions (IDRs).
6: Ultra-Fast Protein Folding: ESMFold, ColabFold & OmegaFold
Protein language models without MSAs (ESM-2 embeddings), ESMFold architecture, ColabFold MMseqs2 acceleration, and scaling to proteome-wide predictions.
7: Protein Complex Modeling & AlphaFold-Multimer
Quaternary structure prediction, AlphaFold-Multimer, cross-chain pairing, interface confidence (ipTM), predicting homooligomers and transient protein-protein interactions.
8: Small Molecule Cheminformatics & Chemical Databases
PubChem, ChEMBL, ZINC20, SMILES strings, InChIKey, RDKit Python cheminformatics library, Morgan fingerprints, Lipinski's Rule of 5, and ADMET filters.
9: Molecular Docking Principles, Receptor Prep & Grid Boxes
Rigid receptor vs flexible ligand, PROPKA protonation, Gasteiger charges, defining search grid bounding boxes, and PDBQT conversion.
10: Virtual Screening with AutoDock Vina & Smina
AutoDock Vina scoring function, parallel high-throughput screening, interpreting binding free energy (kcal/mol), and pose RMSD clustering.
11: Molecular Dynamics Simulations with GROMACS
Force fields (CHARMM/AMBER), periodic boundary solvent boxes, counter-ion neutralization, energy minimization, NVT/NPT equilibration, and production MD trajectories.
12: Generative De Novo Protein Design: RFdiffusion & ProteinMPNN
Denoising diffusion probabilistic models for protein backbones (RFdiffusion), motif scaffolding, de novo binder design, and sequence sequence design with ProteinMPNN.
13: Structure-Based Drug Design Case Study: Kinase Inhibitor Discovery
End-to-end case study: Kinase target preparation (EGFR / BRAF), pocket analysis, virtual screening of 100,000 compounds, binding pose refinement, and MD stability verification.
📚 Foundational Literature & Methodology Benchmarks
Deep Learning Structure Prediction
- Jumper J, Evans R, Pritzel A, et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. doi:10.1038/s41586-021-03819-2
- Abramson J, Adler J, Dunger J, et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493–500. doi:10.1038/s41586-024-07487-w
- Lin Z, Akin H, Rao R, et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model (ESMFold). Science 379(6637):1123–1130. doi:10.1126/science.ade2574
Docking, Screening & Molecular Dynamics
- Trott O, Olson AJ. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. doi:10.1002/jcc.21334
- Abraham MJ, Murtola T, Schulz R, et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1-2:19–25. doi:10.1016/j.softx.2015.06.001
- van Kempen M, Kim SS, Tumescheit C, et al. (2024). Fast and accurate protein structure search with Foldseek. Nat Biotechnol 42:243–246. doi:10.1038/s41587-023-01773-0
De Novo Generative Design
- Watson JL, Juergens D, Bennett NR, et al. (2023). De novo design of protein structure and function with RFdiffusion. Nature 620:1089–1100. doi:10.1038/s41586-023-06415-8
- Dauparas J, Anishchenko I, Bennett N, et al. (2022). Robust deep learning–based protein sequence design using ProteinMPNN. Science 378(6615):49–56. doi:10.1126/science.add2187
- Berman HM, Westbrook J, Feng Z, et al. (2000). The Protein Data Bank. Nucleic Acids Res 28(1):235–242. doi:10.1093/nar/28.1.235