Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015
| Autor: | Chengfei Yan, Xianjin Xu, Xiaoqin Zou |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Protein Conformation Computational biology Protein Serine-Threonine Kinases Ligands Machine learning computer.software_genre Article 03 medical and health sciences Drug Discovery Humans HSP90 Heat-Shock Proteins Physical and Theoretical Chemistry Databases Protein Lead Finder Binding Sites Chemistry Drug discovery business.industry Intracellular Signaling Peptides and Proteins computer.file_format Ligand (biochemistry) Protein Data Bank Computer Science Applications Molecular Docking Simulation 030104 developmental biology Protein–ligand docking Searching the conformational space for docking Docking (molecular) Drug Design Artificial intelligence business Statistical potential computer Protein Binding |
| Zdroj: | Journal of Computer-Aided Molecular Design. 31:689-699 |
| ISSN: | 1573-4951 0920-654X |
| DOI: | 10.1007/s10822-017-0038-1 |
| Popis: | The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding. |
| Databáze: | OpenAIRE |
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