Sequence-based protein-protein interaction prediction and its applications in drug discovery

TL;DR

This review discusses the latest sequence-based computational methods for predicting protein-protein interactions, emphasizing deep learning and transformer models, and highlights their applications in drug discovery and therapeutic development.

Contribution

It provides a comprehensive overview of current PPI prediction techniques, data curation methods, and explores their practical applications in drug discovery and therapeutic design.

Findings

Transformer-based models enhance PPI prediction accuracy.

Sequence-based methods facilitate target identification and drug development.

PPI prediction accelerates therapeutic peptide and antibody design.

Abstract

Aberrant protein-protein interactions (PPIs) underpin a plethora of human diseases, and disruption of these harmful interactions constitute a compelling treatment avenue. Advances in computational approaches to PPI prediction have closely followed progress in deep learning and natural language processing. In this review, we outline the state-of the-art for sequence-based PPI prediction methods and explore their impact on target identification and drug discovery. We begin with an overview of commonly used training data sources and techniques used to curate these data to enhance the quality of the training set. Subsequently, we survey various PPI predictor types, including traditional similarity-based approaches, and deep learning-based approaches with a particular emphasis on the transformer architecture. Finally, we provide examples of PPI prediction in systems-level proteomics analyses, target identification, and design of therapeutic peptides and antibodies. We also take the opportunity to showcase the potential of PPI-aware drug discovery models in accelerating therapeutic development.