AlphaCRV: A Pipeline for Identifying Accurate Binder Topologies in Mass-Modeling with AlphaFold

Abstract

The speed and accuracy of deep learning-based structure prediction algorithms makes it now possible to perform in silico "pull-downs" to identify protein-protein interactions at a proteome-wide scale. However, existing scoring algorithms struggle to accurately identify correct interactions at such a large scale, resulting in an excessive number of false positives. Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold.