We present TrainESM2, an executable agent skill that trains a 9.6M-parameter ESM-2 protein language model on Swiss-Prot from raw sequences to deployed weights.
Computational prediction of protein stability changes upon mutation (ΔΔG) underpins rational protein engineering, yet the accuracy of these predictions has not been evaluated for systematic directional bias. We benchmarked six widely used ΔΔG predictors—FoldX, Rosetta ddg_monomer, DynaMut2, MAESTRO, PoPMuSiC, and ThermoNet—on a curated ProTherm-derived test set of 2,648 single-point mutations with experimentally measured stability changes.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.
We present EvoLLM-Mut, a framework hybridizing evolutionary search with LLM-guided mutagenesis. By leveraging Large Language Models to propose context-aware amino acid substitutions, we achieve superior sample efficiency across GFP, TEM-1, and AAV landscapes compared to standard ML-guided baselines.