Differences between high- and low-affinity complexes of enzymes and nonenzymes.

Physical differences in small molecule binding between enzymes and nonenzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of nonenzymes. High- and low-affinity ligands of nonenzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and nonenzymes have significant ramifications for scoring functions and structure-based drug design. In particular, nonenzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of nonenzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions despite very similar distributions of amino acids in the overall protein sequences.

[1]  Heather A Carlson,et al.  Exploring protein-ligand recognition with Binding MOAD. , 2006, Journal of molecular graphics & modelling.

[2]  Michael G. Lerner,et al.  Binding MOAD (Mother Of All Databases) , 2005, Proteins.

[3]  Thierry Langer,et al.  Recent Advances in Docking and Scoring , 2005 .

[4]  A. Hopkins,et al.  Ligand efficiency: a useful metric for lead selection. , 2004, Drug discovery today.

[5]  Christopher W Murray,et al.  Fragment-based lead discovery: leads by design. , 2005, Drug discovery today.

[6]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[7]  R. Abagyan,et al.  Comprehensive identification of "druggable" protein ligand binding sites. , 2004, Genome informatics. International Conference on Genome Informatics.

[8]  P. Hajduk,et al.  Predicting protein druggability. , 2005, Drug discovery today.

[9]  Shaomeng Wang,et al.  M-score: a knowledge-based potential scoring function accounting for protein atom mobility. , 2006, Journal of medicinal chemistry.

[10]  I. Luque,et al.  Structure-based prediction of binding affinities and molecular design of peptide ligands. , 1998, Methods in enzymology.

[11]  K. Dill,et al.  Partitioning of nonpolar solutes into bilayers and amorphous n-alkanes , 1990 .

[12]  Dudley H. Williams,et al.  Understanding noncovalent interactions: ligand binding energy and catalytic efficiency from ligand-induced reductions in motion within receptors and enzymes. , 2004, Angewandte Chemie.

[13]  R. Nolen 'Between a rock and a hard place'. , 2002, Journal of the American Veterinary Medical Association.

[14]  Daniel R. Caffrey,et al.  Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.

[15]  Kerim Babaoglu,et al.  Deconstructing fragment-based inhibitor discovery , 2006, Nature chemical biology.

[16]  C. Chothia,et al.  Hydrophobic bonding and accessible surface area in proteins , 1974, Nature.

[17]  B Honig,et al.  Reconciling the magnitude of the microscopic and macroscopic hydrophobic effects. , 1991, Science.

[18]  Yuichi Sugiyama,et al.  Druggability: selecting optimized drug candidates. , 2005, Drug discovery today.

[19]  P. Hajduk,et al.  Druggability indices for protein targets derived from NMR-based screening data. , 2005, Journal of medicinal chemistry.

[20]  A. Bogan,et al.  Anatomy of hot spots in protein interfaces. , 1998, Journal of molecular biology.

[21]  Christopher L. McClendon,et al.  Reaching for high-hanging fruit in drug discovery at protein–protein interfaces , 2007, Nature.

[22]  I. Kuntz,et al.  The maximal affinity of ligands. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Natasja Brooijmans,et al.  Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.

[24]  Ryan T. Strachan,et al.  Screening the receptorome: an efficient approach for drug discovery and target validation. , 2006, Drug discovery today.

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

[26]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[27]  Renxiao Wang,et al.  The PDBbind database: methodologies and updates. , 2005, Journal of medicinal chemistry.

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

[29]  P. Hajduk Fragment-based drug design: how big is too big? , 2006, Journal of medicinal chemistry.

[30]  W. Delano Unraveling hot spots in binding interfaces: progress and challenges. , 2002, Current opinion in structural biology.

[31]  John C. Norvell,et al.  Structural genomics programs at the US National Institute of General Medical Sciences , 2000, Nature Structural Biology.

[32]  Hiroki Shirai,et al.  Use of Amino Acid Composition to Predict Ligand-Binding Sites , 2007, J. Chem. Inf. Model..

[33]  E. Jaeger,et al.  Docking: successes and challenges. , 2005, Current pharmaceutical design.

[34]  M. Congreve,et al.  Fragment-based lead discovery , 2004, Nature Reviews Drug Discovery.

[35]  H. Kubinyi Drug research: myths, hype and reality , 2003, Nature Reviews Drug Discovery.

[36]  S. Lampel,et al.  The druggable genome: an update. , 2005, Drug discovery today.

[37]  Alan C. Cheng,et al.  Structure-Based Identification of Small Molecule Binding Sites Using a Free Energy Model , 2006, J. Chem. Inf. Model..

[38]  G. Crippen,et al.  Prediction of Physicochemical Parameters by Atomic Contributions. , 1999 .

[39]  K. Sharp,et al.  Travel depth, a new shape descriptor for macromolecules: application to ligand binding. , 2006, Journal of molecular biology.

[40]  L. Amzel,et al.  Compensating Enthalpic and Entropic Changes Hinder Binding Affinity Optimization , 2007, Chemical biology & drug design.

[41]  J. T. Metz,et al.  Ligand efficiency indices as guideposts for drug discovery. , 2005, Drug discovery today.

[42]  Christopher D. Thanos,et al.  Hot-spot mimicry of a cytokine receptor by a small molecule , 2006, Proceedings of the National Academy of Sciences.