Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?

ABSTRACT: Since AlphaFold2's rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in protein redesign and potential for therapeutic repurposing. We address two challenges: evaluating tools and combinations ability to detect the effects of multiple concurrent mutations in protein variants, and leveraging large-scale datasets to compare modeling-free methods, namely force fields, which handle point mutations well with limited backbone rearrangement, and inverse folding tools, which excel at native sequence recovery but may struggle with non-natural proteins. Debuting TriCombine, a tool that identifies residue triangles in input stru

SUBMITTER: Cianferoni D 

PROVIDER: S-EPMC12267658 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature