Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2
| Autor: | Kurkcuoglu Soner, Zeynep, Koukos, Panos, Citro, Nevia, Trellet, Mikael E., Garcia Lopes Maia Rodrigues, Joao, de Sousa Moreira, Irina, Roel-touris, Jorge, Melquiond, Adrien S. J., Geng, Cunliang, Schaarschmidt, Jörg, Xue, Li C., Vangone, Anna, Bonvin, A. M. J. J., NMR Spectroscopy, Sub NMR Spectroscopy |
|---|---|
| Přispěvatelé: | NMR Spectroscopy, Sub NMR Spectroscopy |
| Jazyk: | angličtina |
| Předmět: |
0301 basic medicine
030103 biophysics Intermolecular contacts Protein Conformation Receptors Cytoplasmic and Nuclear Computational biology Crystallography X-Ray Ligands Machine learning computer.software_genre Article Docking 03 medical and health sciences Farnesoid X Nuclear Receptor Drug Discovery Humans Physical and Theoretical Chemistry Binding affinities Virtual screening Binding Sites biology business.industry Chemistry Limiting Haddock Ligand (biochemistry) biology.organism_classification Computer Science Applications Molecular Docking Simulation 030104 developmental biology Binding affinity Docking (molecular) Drug Design Computer-Aided Design Thermodynamics Artificial intelligence Ranking Drug design data resource business computer Software D3R Protein Binding Protein ligand |
| Zdroj: | Journal of Computer-Aided Molecular Design Journal of Computer-Aided Molecular Design, 32(1), 175 Sygma NARCIS PubMed Central Journal of Computer-Aided Molecular Design, 32(1), 175. Springer Netherlands |
| ISSN: | 1573-4951 0920-654X |
| DOI: | 10.1007/s10822-017-0049-y |
| Popis: | We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening. Electronic supplementary material The online version of this article (doi:10.1007/s10822-017-0049-y) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |