PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction

• Published in: Journal of cheminformatics 2024-06, Vol.16 (1), p.66-16, Article 66
• Main Authors: Jeevan, Kandel, Palistha, Shrestha, Tayara, Hilal, Chong, Kil T.
• Format: Article
• Language: English
• Subjects: Binding sites; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Datasets; Deep learning; Documentation and Information in Chemistry; Drug discovery; Engineering; Ligands; Machine learning; Neural networks; Predictions; Protein families; Proteins; R&D; Representations; Research & development; Semantics; Software; Sparsity; Theoretical and Computational Chemistry
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-024-00865-6

Accurate ligand binding site prediction (LBSP) within proteins is essential for drug discovery. We developed ProteinUNetResNetV2.0 (PUResNetV2.0), leveraging sparse representation of protein structures to improve LBSP accuracy. Our training dataset included protein complexes from 4729 protein families. Evaluations on benchmark datasets showed that PUResNetV2.0 achieved an 85.4% Distance Center Atom (DCA) success rate and a 74.7% F1 Score on the Holo801 dataset, outperforming existing methods. However, its performance in specific cases, such as RNA, DNA, peptide-like ligand, and ion binding site prediction, was limited due to constraints in our training data. Our findings underscore the potential of sparse representation in LBSP, especially for oligomeric structures, suggesting PUResNetV2.0 as a promising tool for computational drug discovery.