Peptides can regulate protein interactions by binding to specific interfaces, and fragments of larger proteins have high potential to function in this manner. Recently developed experimental methods allow massively parallel measurement of protein fragment-based inhibition in vivo. However, we have lacked comparable computational methods to predict which protein fragments act as inhibitors and how they bind. Here, we report an approach, FragFold, which leverages high-throughput AlphaFold predictions of protein–fragment binding to tackle these problems at scale. FragFold is successful at predicting inhibitory protein fragments and their binding modes across diverse protein structures and functions. This approach stands to enable proteome-wide discovery of inhibitory protein fragments and aid the interpretation of high-throughput experimental measurements of inhibitory activity.
Code used in this paper has been deposited in GitHub: https://github.com/swanss/FragFold Data have been deposited in Figshare: https://figshare.com/articles/dataset/Source_Data_for_Savinov_and_Swanson_et_al_2023/24841269