SVSBI: sequence-based virtual screening of biomolecular interactions

• Published in: Communications biology Vol. 6; no. 1; pp. 536 - 12
• Main Authors: Shen, Li, Feng, Hongsong, Qiu, Yuchi, Wei, Guo-Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.05.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2397; Algorithms; Amino acid sequence; Biology; Biomedical and Life Sciences; Drug development; Drug Discovery - methods; Embedding; Language; Life Sciences; Ligands; Molecular Docking Simulation; Natural language processing; Protein engineering; Protein interaction; Proteins; Proteins - metabolism
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-023-04866-3

Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K -embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering. A sequence-based virtual screening method uses natural language processing algorithms and optimized deep K -embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking.