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.