Login Register Cart 0
Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe

A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina

Author(s): Vsevolod Yu. Tanchuk, Volodymyr O. Tanin, Andriy I. Vovk and Gennady Poda

Volume 12, Issue 3, 2015

Page: [170 - 178] Pages: 9

DOI: 10.2174/1570163812666150825110208

Price: $65

Abstract

Molecular docking of small molecules in the protein binding sites is the most widely used computational technique in modern structure-based drug discovery. Although accurate prediction of binding modes of small molecules can be achieved in most cases, estimation of their binding affinities remains mediocre at best. As an attempt to improve the correlation between the inhibitory constants, pKi, and scoring, we created a new, hybrid scoring function. The new function is a linear combination of the terms of the scoring functions of AutoDock and AutoDock Vina. It was trained on 2,412 protein-ligand complexes from the PDBbind database (www.pdbbind.org.cn, version 2012) and validated on a set of 313 complexes released in the 2013 version as a test set. The new function was included in a modified version of AutoDock. The hybrid scoring function showed a statistically significant improvement in both training and test sets in terms of correlation with and root mean square and mean absolute errors in prediction of pKi values. It was also tested on the CSAR 2014 Benchmark Exercise dataset (team T) and produced reasonably good results.

Keywords: AutoDock, AutoDock Vina, hybrid scoring function, molecular docking, scoring, virtual screening.

Graphical Abstract

[1]
Goodsell DS1, Olson AJ. Automated docking of substrates to proteins by simulated annealing. Proteins 1990; 8: 195-202.
[2]
Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30: 2785-91.
[3]
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2009; 31: 455-61.
[4]
Carlsson J, Coleman RG, Setola V, et al. Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat Chem Biol 2011; 7: 769-78.
[5]
Mysinger MM, Weiss DR, Ziarek JJ, et al. Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4. Proc Natl Acad Sci USA 2012; 109: 5517-22.
[6]
Weiss DR, Ahn S, Sassano MF, et al. Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor. ACS Chem Biol 2013; 8: 1018-26.
[7]
Fischer M, Coleman RG, Fraser JS, Shoichet BK. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 2014; 6: 575-83.
[8]
London N, Farelli JD, Brown SD, et al. Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases. Biochemistry 2015; 54: 528-37.
[9]
Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review. AAPS J 2012; 14: 133-41.
[10]
Smith RD, Dunbar JB Jr, Ung PM, et al. CSAR benchmark exercise of 2010: Combined evaluation across all submitted scoring functions. J Chem Inf Model 2011; 51: 2115-31.
[11]
Damm-Ganamet KL, Smith RD, Dunbar JB, Stuckey JA, Carlson HA. CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series. J Chem Inf Model 2013; 53: 1853-70.
[12]
Li Y, Liu Z, Li J, et al. Comparative Assessment of Scoring Functions on an Updated Benchmark: I. Compilation of the Test Set. J Chem Inf Model 2014; 54: 1700-16.
[13]
Li Y, Liu Z, Li J, et al. Comparative Assessment of Scoring Functions on an Updated Benchmark: II. Evaluation Methods and General Results. J Chem Inf Model 2014; 54: 1717-36.
[14]
Cornell WD, Cieplak P, Bayly CI, et al. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J Am Chem Soc 1995; 117: 5179-97.
[15]
Wang R, Fang X, Lu Y, Wang S. The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures. J Med Chem 2004; 47: 2977-80.
[16]
Press W, Teukolsky S, Vetterling V, Flannery B. Numerical recipes in C: the art of scientific computing Cambridge University Press: New York. 1995.
[17]
Tanchuk V, Tanin V, Vovk A. Multithreaded version of AutoDock 42 suitable for massive virtual screening of potential biologically active compounds (enzyme inhibitors) Third International Conference "High Performance Computing" HPC-UA 2013 (Ukraine, Kyiv, October 7-11, 2013), 399-401.http://hpc-ua.org/hpc-ua-13/files/proceedings/76.pdf
[18]
Bikadi Z, Hazai E. Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J Cheminform 2009; 1: 15.
[19]
Wang L, Wu Y, Deng Y, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015; 137: 2695-703.

Rights & Permissions Print Cite
Article Metrics
56
4
2
1
© 2024 Bentham Science Publishers | Privacy Policy