Zero-shot protein stability prediction by inverse folding models: a free energy interpretation
Jes Frellsen, Maher M. Kassem, Tone Bengtsen, Lars Olsen, Kresten Lindorff-Larsen, Jesper Ferkinghoff-Borg, Wouter Boomsma
摘要
Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A better understanding would be of interest not only from a theoretical perspective, but also potentially provide the basis for stronger zero-shot stability prediction. In this paper, we take steps to clarify the free-energy foundations of inverse folding models. Our derivation reveals the standard practice of likelihood ratios as a simplistic approximation and suggests several paths towards better estimates of the relative stability. We empirically assess these approaches and demonstrate that considerable gains in zero-shot performance can be achieved with fairly simple means.
- Equal contribution. ‡ Work performed while employed at Novonesis.
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- Language models enable zero-shot prediction of the effects of mutations on protein functionJoshua Meier, Roshan Rao, Robert Verkuil, Jason Liu 等NeurIPS 2021 · 被引用 969 次
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