Protein interaction interface region prediction by geometric deep learning

By Bowen Dai, C. Bailey‐Kellogg. 2021

109 citations recorded by the source corpus.

MOTIVATION Protein-protein interactions drive wide-ranging molecular processes, and characterizing at the atomic level how proteins interact (beyond just the fact that they interact) can provide key insights into understanding and controlling this machinery. Unfortunately, experimental determination of three-dimensional protein complex structures remains difficult and does not scale to the increasingly large sets of proteins whose interactions are of interest. Computational methods are thus required to meet the demands of large-scale, high-throughput prediction of how proteins interact, but unfortunately both physical modeling and machine learning methods suffer from poor precision and/or recall. RESULTS In order to improve performance in predicting protein interaction interfaces, we leverage the best properties of both data- and physics-driven methods to develop a unified Geometric Deep Neural Network, "PInet" (Protein Interface Network). PInet consumes pairs of point clouds encoding the structures of two partner proteins, in order to predict their structural regions mediating interaction. To make such predictions, PInet learns and utilizes models capturing both geometrical and physicochemical molecular surface complementarity. In application to a set of benchmarks, PInet simultaneously predicts the interface regions on both interacting proteins, achieving performance equivalent to or even much better than the state-of-the-art predictor for each dataset. Furthermore, since PInet is based on joint segmentation of a representation of a protein surfaces, its predictions are meaningful in terms of the underlying physical complementarity driving molecular recognition. AVAILABILITY PInet scripts and models are available at https://github.com/FTD007/PInet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “…other protein representations have been proposed for (graph) DL methods for individual structure prediction (Jing et al., 2020), protein-protein interactions (Dai & Bailey-Kellogg, 2021; Eismann et al., 2020; Townshend et al., 2019), or protein function prediction (Gligorijević et al., 2021).”
Protein Representation Learning by Geometric Structure Pretraining (2022, background): “Another potential direction is to extract features from protein surfaces [24, 75, 19].”
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, background): “Relevant to protein-protein interaction (PPI) is the task of protein interface prediction where GNNs have showed promise (Fout et al., 2017; Townshend et al., 2019; Liu et al., 2020; Xie and Xu, 2021; Dai and Bailey-Kellogg, 2021).”
Geometric Transformers for Protein Interface Contact Prediction (2021, background): “Likewise, Dai & Bailey-Kellogg (2021) do so with an attention-based GNN.”