Improving Peptide-Protein Docking with AlphaFold-Multimer using Forced Sampling

Abstract

Protein interactions are key in vital biological process. In many cases, particularly often in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions of other proteins. The flexible nature of peptides enable rapid, yet specific, regulation of important functions in the cell, such as the cell life-cycle. Because of this, understanding the molecular details of these interactions are crucial to understand and alter their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has caused a leap in accuracy for computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact as well as its accuracy in modeling the resulting interaction complexes are benchmarked against established methods in the fields of peptide-protein interaction prediction and modeling. We find that AlphaFold-Multimer consistently produces predicted interaction complexes with a median DockQ of 0.47 for all 112 complexes investigated. Additionally, it can be used to separate interacting from non-interacting pairs of peptides and proteins with ROC-AUC and PR-AUC of 0.78 and 0.61, respectively, best among the method benchmarked. However, the most interestingly result is the possibility to improve AlphaFold by enabling dropout at inference to sample a wider part of the conformational space. This improves the median DockQ from 0.47 to 0.56 for rank 1 and the median best DockQ improves from 0.58 to 0.72. This scheme of generating more structures with AlphaFold should be generally useful for many application involving multiple states, flexible regions and disorder.