Editing Protein Engineering with AI (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Protein Engineering AI Methods
! Method !! Design Type !! Experimental Success Rate !! Key Advantage
|-
| Directed evolution (wet lab) || Optimization || High (iterative) || No model needed
|-
| ESM-2 zero-shot || Variant ranking || ~50-70% top variants better || No data needed
|-
| ProteinMPNN || Sequence for backbone || 50-80% fold correctly || Fast, reliable
|-
| RFDiffusion || De novo binder/design || 10-30% experimental success || Entirely new proteins
|-
| Rosetta + ML || Stability engineering || 30-60% improvement || Interpretable energy terms
|}

'''Failure modes''': In silico predictions not matching wet lab results (correlation ~0.5-0.7, not 1.0). Epistatic interactions — individual beneficial mutations are incompatible in combination. Distribution shift — models trained on natural protein diversity may not generalize to extreme engineering targets. Fitness function misspecification — optimizing the wrong property (stability alone doesn't mean functional). False negatives — discarding viable variants due to model uncertainty.
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