Enhanced Virtual Screening by Combined Use of Two Docking Methods: Getting the Most on a Limited Budget

Flexible ligand docking is a routine part of a modern structure-based lead discovery process. As of today, there are quite a number of commercial docking programs that can be used to screen large databases (hundreds of thousands to millions of compounds). However, limiting factors such as the number of commercial software licenses needed to perform docking simultaneously on multiple processors ("software cost") and the relatively long time required per molecule to get good results ("quality-to-speed") should be taken into account when planning a large docking run. How can we optimize the efficiency of selecting lead candidates by docking, in respect to the quality of the results, search speed, and software cost? We present a combination of two methods, our "fast-free-approximate" in-house docking program and the "slow-costly-accurate" ICM-Dock, as an example of one solution to the problem. Our proposed protocol is illustrated by a series of virtual screening experiments aimed at identifying active compounds in the MDL Drug Data Report database. In more than half of the 20 cases examined, at least several actives per protein target were identified in approximately 24 hours per target.

[1]  M Rarey,et al.  Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.

[2]  Robert P. Sheridan,et al.  Flexibases: A way to enhance the use of molecular docking methods , 1994, J. Comput. Aided Mol. Des..

[3]  Jonathan W. Essex,et al.  A review of protein-small molecule docking methods , 2002, J. Comput. Aided Mol. Des..

[4]  Robert P. Sheridan,et al.  FLOG: A system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure , 1994, J. Comput. Aided Mol. Des..

[5]  Paul D Lyne,et al.  Structure-based virtual screening: an overview. , 2002, Drug discovery today.

[6]  Luhua Lai,et al.  Further development and validation of empirical scoring functions for structure-based binding affinity prediction , 2002, J. Comput. Aided Mol. Des..

[7]  Ruben Abagyan,et al.  Comparative study of several algorithms for flexible ligand docking , 2003, J. Comput. Aided Mol. Des..

[8]  Gisbert Schneider,et al.  Virtual screening and fast automated docking methods. , 2002, Drug discovery today.

[9]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[10]  R. Clark,et al.  Consensus scoring for ligand/protein interactions. , 2002, Journal of molecular graphics & modelling.

[11]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[12]  Ruben Abagyan,et al.  ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation , 1994, J. Comput. Chem..

[13]  J. Irwin,et al.  Lead discovery using molecular docking. , 2002, Current opinion in chemical biology.

[14]  R. Abagyan,et al.  Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. , 1994, Journal of molecular biology.

[15]  R Abagyan,et al.  High-throughput docking for lead generation. , 2001, Current opinion in chemical biology.

[16]  I. Kuntz,et al.  Matching chemistry and shape in molecular docking. , 1993, Protein engineering.

[17]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[18]  R Abagyan,et al.  Flexible protein–ligand docking by global energy optimization in internal coordinates , 1997, Proteins.

[19]  T Lengauer,et al.  Two-stage method for protein-ligand docking. , 1999, Journal of medicinal chemistry.

[20]  W Patrick Walters,et al.  A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance , 2004, Proteins.

[21]  Alexander D. MacKerell,et al.  Consideration of Molecular Weight during Compound Selection in Virtual Target-Based Database Screening , 2003, J. Chem. Inf. Comput. Sci..