I used Claude to train a protein language model that can generate an extremophilic version of any enzyme, maintaining its function, while allowing it to survive extreme temperatures, pH, salinity, etc. This allows engineering and deploying functional enzymes for applications that require extreme resilience. This project was exceptionally complex. Collecting the training data required several datasets, tools, analyses, and filters (GTDB, GenomeSPOT, signalP6, mmseq2). Training the ESM2 adapter and classifiers required meticulous care to avoid biases in the training data and overfitting. And creating the final masked PL generative pipeline was equally complex to retain enzymatic function, avoid biases, and to generalize across any enzyme (ESM, ProteinMPNN, Swiss-prot, foldseq, mmseqs2, M-CSA, etc). Claude made this possible, both with the coding and with setting up the many mini-experiments I needed to weed out biases and structure the problem correctly. A 6-12-month scoping project was compressed into 5 days. The complex workflow can be tracked in my github repo attached and through my labnotebook (https://github.com/jayman1466/Extremophilic-Protein-Translator/blob/main/labnotebook.md)
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