BACKGROUND: Deep mutational scanning (DMS) provides comprehensive maps of protein variant effects but remains experimentally intensive. Machine learning (ML) approaches have the potential to reduce experimental burden of DMS by predicting the functional impact of substitutions from limited data. RESULTS: We introduced a ML classifier trained on normalised DMS scores from SARS-CoV-2 main protease (Mpro) to categorise amino acid substitutions as functional (wild-type-like) or non-functional. Using brute-force feature selection, we identified minimal subsets of six substitution scores per residue that enable accurate classification of the remaining substitutions, achieving minimum (worst accuracy) scores exceeding 90%. Models including support vector machines, random forests, and logistic regression were evaluated without retraining (zero-shot prediction) against additional SARS-CoV-2 Mpro datasets and against unrelated datasets. The zero-shot performance of the models was strongest for other enzymes and more modest when applied to DMS systems that assess protein folding and/or protein-protein interactions. CONCLUSION: The results show that targeted DMS combined with ML can reduce sequencing and reagent costs while preserving classification accuracy, offering a practical route to accelerate variant effect prediction.

With the advent of powerful neural language models, AI-based systems to assist developers in coding tasks are becoming widely available; Copilot is one such system. Copilot uses Codex, a large language model (LLM), to complete code conditioned on a preceding "prompt". Codex, however, is trained on public GitHub repositories, viz., on code that may include bugs and vulnerabilities. Previous studies [1], [2] show Codex reproduces vulnerabilities seen in training. In this study, we examine how prone Codex is to generate an interesting bug category, single statement bugs, commonly referred to as simple, stupid bugs or SStuBs in the MSR community. We find that Codex and similar LLMs do help avoid some SStuBs, but do produce known, verbatim SStuBs as much as 2x as likely than known, verbatim correct code. We explore the consequences of the Codex generated SStuBs and propose avoidance strategies that suggest the possibility of reducing the production of known, verbatim SStubs, and increase the possibility of producing known, verbatim fixes.

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