Editing Protein Engineering with AI (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Protein engineering''' — The modification or de novo design of proteins to achieve desired properties (stability, activity, specificity).
* '''Directed evolution''' — A laboratory technique using iterative rounds of random mutation and selection to improve proteins; Nobel Prize 2018.
* '''Sequence-function landscape''' — The mapping from protein sequence to function; engineering navigates this landscape.
* '''Fitness landscape''' — The mapping from sequence to some fitness measure (stability, activity); engineering seeks fitness peaks.
* '''Zero-shot variant effect prediction''' — Predicting which protein mutations improve function without any experimental data on that protein.
* '''Protein stability engineering''' — Designing mutations that increase thermostability, solubility, or shelf life.
* '''Enzyme design''' — Engineering or creating new enzymes with desired catalytic activity.
* '''Binding affinity optimization''' — Improving how tightly a protein binds its target; key for antibody and drug engineering.
* '''Protein language model (PLM)''' — A language model pre-trained on protein sequences; ESM-2, ProGen2, ProtGPT2.
* '''Inverse folding''' — Given a target 3D structure, design a sequence that will fold to it; ESMFold, ProteinMPNN.
* '''RFDiffusion''' — A diffusion model generating novel protein backbone structures conditioned on binding constraints.
* '''ProteinMPNN''' — A message-passing neural network for inverse folding; sequence design for given protein backbones.
* '''Antibody engineering''' — Designing antibodies with desired binding specificity, affinity, and biophysical properties.
* '''Directed evolution in silico''' — Using ML fitness predictors to simulate directed evolution without wet lab experiments.
* '''ProGen / ESM-2 / Progen2''' — Large protein language models enabling sequence generation and variant prediction.
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