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This article details PottsMPNN, a new machine-learning framework that enhances computational protein design by incorporating physical principles to generate novel amino acid sequences rather than merely mimicking natural ones. By shifting the success metric from reproducing evolutionary sequences to optimizing for structural stability and energy landscapes, this approach expands the functional repertoire of designed proteins for potential therapeutic applications.
Looking beyond natural sequences
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A protein’s function is determined by its structure, and structure — the way a protein folds — is determined by its sequence of amino acids, the building blocks of proteins. Many methods for designing novel proteins, including examples that could bind to a disease-causing molecule in our cells, involve a two-step process: The structure comes first, and then a machine-learning framework generates a repertoire of sequences that could potentially adopt that structure. In…
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