On the Effects of Permuted Input on Conformational Sampling of Drug‐like Molecules: an Evaluation of Stochastic Proximity Embedding

Conformational sampling is a problem of central importance in computer‐aided drug design. A good conformational search method must not exhibit any intrinsic bias, and must provide confidence that important regions of conformational space are not missed during the search. A recent study by Carta et al. showed that this is not always the case, and that several popular conformational search methods, such as Omega, are very sensitive to the relative ordering of atoms and bonds in the connection table. Here, we examine the performance of a newer method known as stochastic proximity embedding, or SPE, using five diverse bioactive ligands extracted from the PDB. Our results confirm that the conformational ensembles produced by SPE using different permuted inputs are statistically indistinguishable, and well within the range of variability that would be expected from the stochastic nature of the method itself. This, along with the results of a more comprehensive comparative study (Agrafiotis et al., J. Chem. Info. Model, 2007, in press), provides further evidence that SPE is one of the most robust and competitive conformational search methods described to date.

[1]  M. Saunders.  Stochastic exploration of molecular mechanics energy surfaces. Hunting for the global minimum , 1987 .

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

[3]  Thomas A. Halgren.  Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94 , 1996, J. Comput. Chem..

[4]  N. Go,et al.  Ring Closure and Local Conformational Deformations of Chain Molecules , 1970 .

[5]  D C Spellmeyer,et al.  Conformational analysis using distance geometry methods. , 1997, Journal of molecular graphics & modelling.

[6]  Jonas Boström,et al.  Reproducing the conformations of protein-bound ligands: A critical evaluation of several popular conformational searching tools , 2001, J. Comput. Aided Mol. Des..

[7]  Thomas A. Halgren,et al.  Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules , 1996, J. Comput. Chem..

[8]  M. Karplus,et al.  Prediction of the folding of short polypeptide segments by uniform conformational sampling , 1987, Biopolymers.

[9]  Dimitris K. Agrafiotis,et al.  A distance geometry heuristic for expanding the range of geometries sampled during conformational search , 2006, J. Comput. Chem..

[10]  Jonas Boström,et al.  Assessing the performance of OMEGA with respect to retrieving bioactive conformations. , 2003, Journal of molecular graphics & modelling.

[11]  Thomas A. Halgren,et al.  Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular. interactions , 1996, J. Comput. Chem..

[12]  D. Ferguson,et al.  A new approach to probing conformational space with molecular mechanics: Random incremental pulse search , 1989 .

[13]  Robert E. Bruccoleri,et al.  Chain closure with bond angle variations , 1985 .

[14]  David G. Lloyd,et al.  Permuting input for more effective sampling of 3D conformer space , 2006, J. Comput. Aided Mol. Des..

[15]  Thomas A. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[16]  D. Agrafiotis,et al.  Combinatorial informatics in the post-genomics era , 2002, Nature Reviews Drug Discovery.

[17]  Dimitris K. Agrafiotis,et al.  Stochastic proximity embedding , 2003, J. Comput. Chem..

[18]  G. Chang,et al.  An internal-coordinate Monte Carlo method for searching conformational space , 1989 .

[19]  A. Leach.  A Survey of Methods for Searching the Conformational Space of Small and Medium‐Sized Molecules , 1992 .

[20]  T. Bhat,et al.  The Protein Data Bank and the challenge of structural genomics , 2000, Nature Structural Biology.

[21]  Gordon M. Crippen,et al.  Note rapid calculation of coordinates from distance matrices , 1978 .

[22]  Xiang Yao,et al.  Advanced Biological and Chemical Discovery (ABCD): Centralizing Discovery Knowledge in an Inherently Decentralized World , 2007, J. Chem. Inf. Model..

[23]  Huafeng Xu,et al.  A self-organizing principle for learning nonlinear manifolds , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Eric J. Martin,et al.  Conformational Sampling of Bioactive Molecules: A Comparative Study , 2007, J. Chem. Inf. Model..

[25]  Thomas A. Halgren,et al.  Merck molecular force field. IV. conformational energies and geometries for MMFF94 , 1996, J. Comput. Chem..

[26]  W. C. Still,et al.  The multiple minimum problem in molecular modeling. Tree searching internal coordinate conformational space , 1988 .

[27]  Dimitris K. Agrafiotis,et al.  Conformational Sampling by Self-Organization , 2003, J. Chem. Inf. Comput. Sci..

[28]  Michael D. Miller,et al.  Comparison of Knowledge-Based and Distance Geometry Approaches for Generation of Molecular Conformations , 2001, J. Chem. Inf. Comput. Sci..

[29]  P. Charifson,et al.  Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand reorganization upon binding. , 2004, Journal of medicinal chemistry.

[30]  Thierry Langer,et al.  Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms , 2005, J. Chem. Inf. Model..

[31]  Kenneth M. Merz,et al.  Can we separate active from inactive conformations? , 2002, J. Comput. Aided Mol. Des..