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

Abstract

Summary: 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 noninteracting flanking regions are assigned low confidence values and will affect ipTM, as it considers all interchain residue-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 actual interface pTM (actifpTM), a modified ipTM measure, that focuses on the residues participating in the interaction, resulting in a more robust measure of interaction confidence. Besides, actifpTM is calculated both for the full complex as well as for each pair of chains, making it well-suited for evaluating multi-chain complexes with a particularly critical binding interface, such as antibody-antigen interactions.

Availability and implementation: The method is available as part of the ColabFold (https://github.com/sokrypton/ColabFold) repository, installable both locally or usable with Colab notebook.