Protein Science
Protein representation learning, structure prediction, dynamics, and generative protein design.
- Protein design as sequence–structure–function inference, from inverse folding and backbone diffusion to computational filters and experimental evidence.
- How metastable protein conformations become equilibrium ensembles and kinetic models, and what learned samplers must preserve beyond structural plausibility.
- How sequence, alignments, residue graphs, backbone frames, surfaces, and multimodal objectives shape what protein embeddings can support.
- How coevolutionary constraints, pairwise geometric reasoning, residue frames, and all-atom diffusion shaped AlphaFold—and where structure prediction stops.
- An introduction to protein structure, function, and computational design — from amino acids to the RFDiffusion/ProteinMPNN pipeline.