Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes.

ABSTRACT: Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins inv

SUBMITTER: Zink S 

PROVIDER: S-EPMC12413084 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature