ai/ml for protein structure
Here is a quick summary of the three main AI protein structure prediction packages. I plan on exploring each of these in future posts (table below).
| Feature | AlphaFold3 [1][2][3] | ProteinMPNN [4][5][6] | RFdiffusion [7][8] |
|---|---|---|---|
| Primary Purpose | Predict 3D structures & interactions of biomolecular complexes | Design amino acid sequences for given protein backbones (inverse folding) | Generate novel protein structures & complexes via diffusion |
| Key Innovation | Diffusion-based architecture for multi-molecule prediction | Graph neural network (GNN) with evolutionary/structure-aware training | Diffusion-based structure generation with conditional constraints |
| Input | Molecular components (proteins, DNA, RNA, ligands) | 3D protein structure (PDB format) | Structural constraints/motifs (PDB format) |
| Output | 3D coordinates of molecular complexes | Amino acid sequences (Multi-FASTA) | Novel 3D protein structures (PDB format) |
| Key Applications | - Drug target identification - Biomolecular interaction analysis | - Enzyme design - Vaccine development - Thermostable proteins | - Binder design - Symmetric assemblies - Motif scaffolding |
| Accuracy | 50-100% improvement over specialized tools in interactions | 52.4% native sequence recovery vs 32.9% for Rosetta | Generates diverse, experimentally validated structures |
| Unique Capabilities | Predicts post-translational modifications & ion interactions | Temperature parameter controls sequence diversity (0.1-0.5 low, ≥0.5 high) | Partial diffusion for design diversification |
| Computational Approach | Unified framework for multiple molecule types | Message-passing neural networks with MSA processing | Progressive refinement through diffusion steps |
| Integration Potential | AlphaFold Server for academic use | Chained with RFdiffusion in drug discovery pipelines | Combined with ProteinMPNN for sequence-structure co-design |
| Availability | Free server for non-commercial use. Commercial through isomorphic labs. | Open-source, NVIDIA NIM implementation. | Open-source (GitHub) |
Here are 5 examples of protein structure analysis using tools such as Alphafold, RFDiffsion, PyTorch, and TensorFlow. These projects leverage rapid prototyping and biological data analysis. Each can be extended with additional visualization or statistical analysis components.
| Project | Tools | Key Steps |
|---|---|---|
| Alphafold | Alphafold local install | Anaconda, Homebrew |
| RFDiffusion | RFDiffusion local install | Anaconda, Homebrew |
| ProteinMPNN | ProteinMPNN local install | Anaconda, Homebrew |
| Dark Kinome Analysis | Analysis of Dark Kinome Structures from Alphafold and RFDiffusion | RMSD, pLLDT |
| Protein Sequence Classifier (coming soon...) | PyTorch, Transformers | Embed sequences, train classifier |
| Distance Map Predictor (coming soon...) | TensorFlow, CNNs | Process PDB files, train CNN |
| Structure Visualization (coming soon...) | ProteinShake, Py3Dmol | Load structures, render 3D |
| Contact Prediction (coming soon...) | ESM Model, PyTorch | Use pretrained model, predict contacts |
| Simple Fold Metrics (coming soon...) | Biopython, Pandas | Calculate RMSD, torsion angles |