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 methods for sequence-based PPI prediction 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 transformer architecture. Finally, we provide examples of PPI prediction in system-level proteomics analyses, target identification, and designs of therapeutic peptides and antibodies. This review sheds light on sequence-based PPI prediction, a broadly applicable alternative to structure-based methods, from a unique perspective that emphasizes their roles in the drug discovery process and rigorous model assessment.