structure prediction methods
Here is a quick summary of the top 3 protein protein prediction methods:
| 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) |