The sensitivity of the results of molecular docking to induced fit effects: Application to thrombin, thermolysin and neuraminidase

This paper describes the application of PRO−LEADS to the flexible docking of ligands into crystallographically derived enzyme structures that are assumed to be rigid. PRO−LEADS uses a Tabu search methodology to perform the flexible search and an empirically derived estimate of the binding affinity to drive the docking process. The paper tests the extent to which the assumption of a rigid enzyme compromises the accuracy of the results. All-pairs docking experiments are performed for three enzymes (thrombin, thermolysin and influenza virus neuraminidase) based on six or more ligand-enzyme crystal structures for each enzyme. In 76% of the cases, PRO−LEADS can successfully identify the correct ligand conformation as the lowest energy configuration when the enzyme structure is derived from that ligand's crystal structure, but the methodology only docks 49% of the cases successfully when the ligand is docked against enzyme crystal structures derived from other ligands. Small movements in the enzyme structure lead to an under-prediction in the energy of the correct binding mode by up to 14 kJ/mol and in some cases this under-prediction can lead to the native mode not being recognised as the lowest energy solution. The type of movements responsible for mis-docking are: the movement of sidechains as a result of changes in Cα position; the movement of sidechains without changes in Cα position; the movement of flexible portions of main chains to facilitate the formation of hydrogen bonds; and the movement of metal atoms bound to the enzyme active site. The work illustrates that the assumption of a rigid active site can lead to errors in identification of the correct binding mode and the assessment of binding affinity, even for enzymes which show relatively small shift in atomic positions from one ligand to the next. A good docking code, such as PRO−LEADS, can usually dock successfully if there is induced fit in relatively rigid enzymes but there remains the need to develop improved strategies for dealing with enzyme flexibility. The work implies that treatments of enzyme flexibility which focus only on sidechain rotations will not deal with the critical shifts responsible for mis-docking of ligands in thrombin, thermolysin and neuraminidase. The paper demonstrates the utility of all pairs docking experiments as a method of assessing the effectiveness of docking methodologies in dealing with enzyme flexibility.

[1]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1977, Journal of molecular biology.

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

[3]  I. Kuntz,et al.  Conformational analysis of flexible ligands in macromolecular receptor sites , 1992 .

[4]  F. Glover,et al.  In Modern Heuristic Techniques for Combinatorial Problems , 1993 .

[5]  C. Reeves Modern heuristic techniques for combinatorial problems , 1993 .

[6]  J. Scott Dixon,et al.  A good ligand is hard to find: Automated docking methods , 1993 .

[7]  A. Itai,et al.  DEVELOPMENT OF AN EFFICIENT AUTOMATED DOCKING METHOD , 1993 .

[8]  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..

[9]  A. Leach,et al.  Ligand docking to proteins with discrete side-chain flexibility. , 1994, Journal of molecular biology.

[10]  R. Glen,et al.  Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. , 1995, Journal of molecular biology.

[11]  Richard S. Judson,et al.  Docking flexible molecules: A case study of three proteins , 1995, J. Comput. Chem..

[12]  J. Scott Dixon,et al.  Flexible ligand docking using a genetic algorithm , 1995, J. Comput. Aided Mol. Des..

[13]  Gennady M Verkhivker,et al.  Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. , 1995, Chemistry & biology.

[14]  Kevin P. Clark,et al.  Flexible ligand docking without parameter adjustment across four ligand–receptor complexes , 1995, J. Comput. Chem..

[15]  Ajay N. Jain Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities , 1996, J. Comput. Aided Mol. Des..

[16]  Thomas Lengauer,et al.  Computational methods for biomolecular docking. , 1996, Current opinion in structural biology.

[17]  David S. Goodsell,et al.  Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4 , 1996, J. Comput. Aided Mol. Des..

[18]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[19]  G. V. Paolini,et al.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes , 1997, J. Comput. Aided Mol. Des..

[20]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[21]  David E. Clark,et al.  A comparison of heuristic search algorithms for molecular docking , 1997, J. Comput. Aided Mol. Des..

[22]  Colin McMartin,et al.  QXP: Powerful, rapid computer algorithms for structure-based drug design , 1997, J. Comput. Aided Mol. Des..

[23]  Thomas Lengauer,et al.  Multiple automatic base selection: Protein–ligand docking based on incremental construction without manual intervention , 1997, J. Comput. Aided Mol. Des..

[24]  I. Kuntz,et al.  Molecular docking to ensembles of protein structures. , 1997, Journal of molecular biology.

[25]  D. E. Clark,et al.  Flexible docking using tabu search and an empirical estimate of binding affinity , 1998, Proteins.