High- throughput discovery of inhibitory protein fragments with AlphaFold

• 成果类型: Article
• 署名作者: Savinov, Andrew; Swanson, Sebastian; Keating, Amy E.; Li, Gene - Wei
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Howard Hughes Medical Institute; Massachusetts Institute of Technology (MIT); Novo Nordisk
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14576
• DOI: 10.1073/pnas.2322412122
• 发表日期: 2025-02-11
• 关键词: dna-synthesis technologies; c-terminal linker; large-scale; z-ring; ftsz; peptide; tail; insight; DESIGN; zipa

摘要

Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native- like binding interactions. Recently developed experimental approaches allow for high- throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind to protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full- length proteins in a high- throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. Comparisons with experimental measurements establish that our approach is a sensitive predictor of fragment function: Evaluating inhibitory fragments from known protein-protein interaction interfaces, we find 87% are predicted by FragFold to bind in a native- like mode. Across full protein sequences, 68% of FragFold- predicted binding peaks match experimentally measured inhibitory peaks. Deep mutational scanning experiments support the predicted binding modes and uncover superior inhibitory peptides in high throughput. Further, FragFold is able to predict previously unknown protein binding modes, explaining prior genetic and biochemical data. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.