Deep Learning for Protein–Protein Interaction Site Prediction

Abstract

Protein–protein interactions (PPIs) are central to cellular functions. Experimental methods for predicting PPIs are well developed but are time and resource expensive and suffer from high false-positive error rates at scale. Computational prediction of PPIs is highly desirable for a mechanistic understanding of cellular processes and offers the potential to identify highly selective drug targets. In this chapter, details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI—a task known as PPI site prediction—are outlined. The key decisions to be made in defining a supervised machine learning project in this domain are here highlighted. Alternative training regimes for deep learning models to address shortcomings in existing approaches and provide starting points for further research are discussed. This chapter is written to serve as a companion to developing deep learning approaches to protein–protein interaction site prediction, and an introduction to developing geometric deep learning projects operating on protein structure graphs.

Protein–protein interaction prediction with deep learning: A comprehensive review

“…Proteins adopt complex 3-D structures to perform biological functions via physical contact between effectors and regulators. The effectors may be characterised as the molecules that activate or suppress the regulator’s function and alter gene expression as a result [204] , [205] . Therefore, predicting which residues are involved in PPIs may help structure-based drug discovery, improve the accuracy of protein–protein docking, and obtain richer annotation of protein function [206] , [207] .…”

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…The global advantage of methods based on machine learning is the processing of multidimensional and multivariate data from several omics or horizontal omics ( Das et al, 2020 ; Jamasb et al, 2021 ). Prediction of interactions is highly efficient ( Terayama et al, 2019 ; Balogh et al, 2022 ), but machine learning requires large computational resources and large datasets of good quality ( Hashemifar et al, 2018 ; Y.…”

Machine learning solutions for predicting protein–protein interactions

“…Specific and derived databases organize structures according to given properties, 39 like ProtCID, a data resource for structural information on protein interactions 43 . Furthermore, curated and processed small datasets are shared to enable benchmarking of novel methods 17 …”

“…43 Furthermore, curated and processed small datasets are shared to enable benchmarking of novel methods. 17…”