Discriminating physiological from non-physiological interfaces in structures of proteins complexes" is a community-wide study that provides insights into properties of homodimeric interfaces

Overview

The KVL is happy to announce the project's conclusion, resulting in a paper about Artificial intelligence techniques applied to computational structural biology.

Work Summary

The paper by a large group of authors, centered around 3DBioInfo Elixir Community, presents a community-wide study of an important problem of differentiation between experimentally determined or modeled physiological and non-physiological protein-protein complexes/interfaces. The authors designed and generated a large benchmark set of physiological and non-physiological homodimeric complexes and evaluated a large set of scoring functions and AlphaFold predictions on their ability to discriminate the non-physiological interfaces.

The problem of separating physiological from non-physiological interfaces (sometimes, in earlier studies, called “biological” vs. “non-biological” respectively) is very difficult. The core reason for this difficulty is the lack of a clear “iron-clad” distinction between the two categories in a living cell, where co-localized proteins extensively interact in a crowded environment in various interaction modes, heavily dominated by “transient” interactions/encounters. Still, the ability to identify key physiologically significant interfaces in the variety of possible configurations of a protein-protein complex is important.

The paper is available for download from: here

Impact

This machine learning paper presents a major data resource and methodological development in an important direction for molecular and cellular biology. As such, it will be of significant interest to the biological community. It also shows state-of-the-art different techniques related to data protein interactions.