DeepHomo2.0: improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning

• Published in: Briefings in bioinformatics Vol. 24; no. 1
• Main Authors: Lin, Peicong, Yan, Yumeng, Huang, Sheng-You
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19-01-2023; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological activity; Computational Biology - methods; Deep Learning; Machine Learning; Monomers; Predictions; Protein interaction; Protein structure; Proteins; Proteins - chemistry; State-of-the-art reviews; Test sets; protein–protein interaction; transformer features; deep learning; homo-oligomers; residue–residue contact prediction
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbac499

Abstract
Protein–protein interactions play an important role in many biological processes. However, although structure prediction for monomer proteins has achieved great progress with the advent of advanced deep learning algorithms like AlphaFold, the structure prediction for protein–protein complexes remains an open question. Taking advantage of the Transformer model of ESM-MSA, we have developed a deep learning-based model, named DeepHomo2.0, to predict protein–protein interactions of homodimeric complexes by leveraging the direct-coupling analysis (DCA) and Transformer features of sequences and the structure features of monomers. DeepHomo2.0 was extensively evaluated on diverse test sets and compared with eight state-of-the-art methods including protein language model-based, DCA-based and machine learning-based methods. It was shown that DeepHomo2.0 achieved a high precision of >70% with experimental monomer structures and >60% with predicted monomer structures for the top 10 predicted contacts on the test sets and outperformed the other eight methods. Moreover, even the version without using structure information, named DeepHomoSeq, still achieved a good precision of >55% for the top 10 predicted contacts. Integrating the predicted contacts into protein docking significantly improved the structure prediction of realistic Critical Assessment of Protein Structure Prediction homodimeric complexes. DeepHomo2.0 and DeepHomoSeq are available at http://huanglab.phys.hust.edu.cn/DeepHomo2/.