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 predictde novowhich of the many possible protein fragments bind their 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 protein targets in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. These predictions were compared with experimentally measured peaks of inhibitory activity inE. coli. We establish that our approach is a sensitive predictor of protein fragment function: Evaluating inhibitory fragments derived from known protein-protein interaction interfaces, we found 87% were 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. This is true even when the underlying inhibitory mechanism is unclear from existing structural data, and we find FragFold is able to predict novel binding modes for inhibitory fragments of unknown structure, explaining previous genetic and biochemical data for these fragments. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.
