Editing Protein Engineering (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Protein engineering AI accelerates the two core engineering tasks: '''variant optimization''' (improving an existing protein) and '''de novo design''' (creating entirely new proteins).

'''Variant effect prediction''': Given a protein sequence and a mutation, predict whether the mutation improves or worsens a desired property. Zero-shot approaches use protein language model log-likelihood — a mutation that the PLM assigns higher probability than the wild-type is more evolutionarily "accepted" and likely functionally tolerable. ESM-2 log-ratios correlate significantly with experimental fitness data (DMS). This enables in silico screening of millions of variants.

'''Inverse folding for sequence design''': Given a desired 3D structure (from AlphaFold or experimental data), design sequences that will fold to that structure. ProteinMPNN (Dauparas et al., 2022) is the standard tool — it takes a backbone structure as input and generates sequences compatible with that backbone, achieving experimental success rates of 50–80% for designed sequences. This replaces computationally expensive Rosetta-based design.

'''RFDiffusion for de novo backbone generation''': RFDiffusion (Watson et al., 2023) generates novel protein backbone structures by running the diffusion process in the space of protein structures. It can generate binders to arbitrary targets, symmetric assemblies, enzyme active sites, and more — with experimental validation. Combined with ProteinMPNN (RFDiffusion → backbone → ProteinMPNN → sequence → validate), this pipeline has created proteins binding previously undruggable targets.

'''Antibody engineering''': Antibodies are the dominant class of biologic drugs (~$250B market). AI systems like AbMap, AntiBERTy, IgLM, and proprietary tools at Absci, Insilico Medicine, and BigHat Biosciences design and optimize antibodies by training on billions of known antibody sequences. They predict binding affinity, developability (manufacturability), and immunogenicity.
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