actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions

Abstract

One of the main advantages of deep learning models of protein structure, such as Alphafold2, is their ability to accurately estimate the confidence of a generated structural model, which allows us to focus on highly confident predictions.The ipTM score provides a confidence estimate of interchain contacts in protein-protein interactions. However, interactions, in particular motif-mediated interactions, often also contain regions that remain flexible upon binding. These non-interacting flanking regions are assigned low confidence values and will affect iPTM, as it considers all interchain residue pairs, and two models of the same motif-domain interaction, but differing in the length of their flanking regions, would be assigned very different values. Here we propose actifpTM (actual interface pTM), a modified ipTM measure, that focuses on the confident region of an interaction, resulting in a more robust measure of interaction confidence, even when not the full interaction is structured. actifpTM has been incorporated into ColabFold.