期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 15, 期 9, 页码 15122-15145出版社
MDPI AG
DOI: 10.3390/ijms150915122
关键词
PL-PatchSurfer; surface patch; ligand prediction; virtual screening; 3D Zernike descriptor
资金
- Lilly Research Award Program
- National Institute of General Medical Sciences of the National Institutes of Health [R01GM097528]
- National Science Foundation [IIS1319551, DBI1262189, IOS1127027]
- National Research Foundation of Korea - Korean Government [NRF-2011-220-C00004]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1319551] Funding Source: National Science Foundation
Structure-based computational methods have been widely used in exploring protein-ligand interactions, including predicting the binding ligands of a given protein based on their structural complementarity. Compared to other protein and ligand representations, the advantages of a surface representation include reduced sensitivity to subtle changes in the pocket and ligand conformation and fast search speed. Here we developed a novel method named PL-PatchSurfer (Protein-Ligand PatchSurfer). PL-PatchSurfer represents the protein binding pocket and the ligand molecular surface as a combination of segmented surface patches. Each patch is characterized by its geometrical shape and the electrostatic potential, which are represented using the 3D Zernike descriptor (3DZD). We first tested PL-PatchSurfer on binding ligand prediction and found it outperformed the pocket-similarity based ligand prediction program. We then optimized the search algorithm of PL-PatchSurfer using the PDBbind dataset. Finally, we explored the utility of applying PL-PatchSurfer to a larger and more diverse dataset and showed that PL-PatchSurfer was able to provide a high early enrichment for most of the targets. To the best of our knowledge, PL-PatchSurfer is the first surface patch-based method that treats ligand complementarity at protein binding sites. We believe that using a surface patch approach to better understand protein-ligand interactions has the potential to significantly enhance the design of new ligands for a wide array of drug-targets.
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