Protein Pharmacophore Selection Using Hydration-Site Analysis

Virtual screening using pharmacophore models is an efficient method to identify potential lead compounds for target proteins. Pharmacophore models based on protein structures are advantageous because a priori knowledge of active ligands is not required and the models are not biased by the chemical space of previously identified actives. However, in order to capture most potential interactions between all potentially binding ligands and the protein, the size of the pharmacophore model, i.e. number of pharmacophore elements, is typically quite large and therefore reduces the efficiency of pharmacophore based screening. We have developed a new method to select important pharmacophore elements using hydration-site information. The basic premise is that ligand functional groups that replace water molecules in the apo protein contribute strongly to the overall binding affinity of the ligand, due to the additional free energy gained from releasing the water molecule into the bulk solvent. We computed the free energy of water released from the binding site for each hydration site using thermodynamic analysis of molecular dynamics (MD) simulations. Pharmacophores which are colocalized with hydration sites with estimated favorable contributions to the free energy of binding are selected to generate a reduced pharmacophore model. We constructed reduced pharmacophore models for three protein systems and demonstrated good enrichment quality combined with high efficiency. The reduction in pharmacophore model size reduces the required screening time by a factor of 200-500 compared to using all protein pharmacophore elements. We also describe a training process using a small set of known actives to reliably select the optimal set of criteria for pharmacophore selection for each protein system.

[1]  Robert Abel,et al.  Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding , 2007, Proceedings of the National Academy of Sciences.

[2]  Richard A. Lewis,et al.  Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. , 2004, Journal of medicinal chemistry.

[3]  Peter Willett,et al.  GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D , 2006, J. Comput. Aided Mol. Des..

[4]  J. Richardson,et al.  Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. , 1999, Journal of molecular biology.

[5]  W Pangborn,et al.  Comparison of ternary complexes of Pneumocystis carinii and wild-type human dihydrofolate reductase with coenzyme NADPH and a novel classical antitumor furo[2,3-d]pyrimidine antifolate. , 1997, Acta crystallographica. Section D, Biological crystallography.

[6]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[7]  Themis Lazaridis,et al.  Solvent Reorganization Energy and Entropy in Hydrophobic Hydration , 2000 .

[8]  W. Kabsch A solution for the best rotation to relate two sets of vectors , 1976 .

[9]  K K Kannan,et al.  1.9 Å x‐ray study shows closed flap conformation in crystals of tethered HIV‐1 PR , 2001, Proteins.

[10]  H. Berendsen,et al.  Interaction Models for Water in Relation to Protein Hydration , 1981 .

[11]  A. Wlodawer,et al.  Structure-based inhibitors of HIV-1 protease. , 1993, Annual review of biochemistry.

[12]  Mengang Xu,et al.  Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling , 2011, J. Chem. Inf. Model..

[13]  Hoover,et al.  Canonical dynamics: Equilibrium phase-space distributions. , 1985, Physical review. A, General physics.

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

[15]  Sung-Hou Kim,et al.  Structural basis for chemical inhibition of human blood coagulation factor Xa. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Berk Hess,et al.  GROMACS 3.0: a package for molecular simulation and trajectory analysis , 2001 .

[17]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[18]  S. Stanley Young,et al.  Automated Pharmacophore Identification for Large Chemical Data Sets. , 1999 .

[19]  Leslie A Kuhn,et al.  Side‐chain flexibility in protein–ligand binding: The minimal rotation hypothesis , 2005, Protein science : a publication of the Protein Society.

[20]  Yvonne Connolly Martin Distance Comparisons: A New Strategy for Examining Three-Dimensional Structure—Activity Relationships , 1995 .

[21]  Stefan Senger,et al.  Structure- and property-based design of factor Xa inhibitors: pyrrolidin-2-ones with acyclic alanyl amides as P4 motifs. , 2006, Bioorganic & medicinal chemistry letters.

[22]  Marvin Waldman,et al.  Application of structure‐based focusing to the estrogen receptor , 2001, J. Comput. Chem..

[23]  B. Berne,et al.  Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. , 2008, Journal of the American Chemical Society.

[24]  K Osterlund,et al.  Unexpected binding mode of a cyclic sulfamide HIV-1 protease inhibitor. , 1997, Journal of medicinal chemistry.

[25]  M. Hatada,et al.  Novel binding mode of highly potent HIV-proteinase inhibitors incorporating the (R)-hydroxyethylamine isostere. , 1991, Journal of medicinal chemistry.

[26]  Irene T Weber,et al.  HIV-1 protease: structure, dynamics, and inhibition. , 2007, Advances in pharmacology.

[27]  P K Bryant,et al.  The structure of Pneumocystis carinii dihydrofolate reductase to 1.9 A resolution. , 1994, Structure.

[28]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[29]  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..

[30]  H. Berendsen,et al.  Molecular dynamics with coupling to an external bath , 1984 .

[31]  W Pangborn,et al.  Ligand-induced conformational changes in the crystal structures of Pneumocystis carinii dihydrofolate reductase complexes with folate and NADP+. , 2000, Biochemistry.

[32]  S. Nosé A molecular dynamics method for simulations in the canonical ensemble , 1984 .

[33]  Sébastien Maignan,et al.  Molecular structures of human factor Xa complexed with ketopiperazine inhibitors: preference for a neutral group in the S1 pocket. , 2003, Journal of medicinal chemistry.

[34]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[35]  Xin Chen,et al.  Automated Pharmacophore Identification for Large Chemical Data Sets1 , 1999, J. Chem. Inf. Comput. Sci..

[36]  Bin Ye,et al.  Crystal structures of two potent nonamidine inhibitors bound to factor Xa. , 2002, Biochemistry.

[37]  David D L Minh,et al.  The entropic cost of protein-protein association: a case study on acetylcholinesterase binding to fasciculin-2. , 2005, Biophysical journal.

[38]  Michel F Sanner,et al.  FLIPDock: Docking flexible ligands into flexible receptors , 2007, Proteins.

[39]  A. Spada,et al.  Crystal structures of human factor Xa complexed with potent inhibitors. , 2000, Journal of medicinal chemistry.

[40]  M. Parrinello,et al.  Polymorphic transitions in single crystals: A new molecular dynamics method , 1981 .

[41]  Andrew Smellie,et al.  Identification of Common Functional Configurations Among Molecules , 1996, J. Chem. Inf. Comput. Sci..

[42]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[43]  Hans Matter,et al.  Design and quantitative structure-activity relationship of 3-amidinobenzyl-1H-indole-2-carboxamides as potent, nonchiral, and selective inhibitors of blood coagulation factor Xa. , 2002, Journal of medicinal chemistry.

[44]  R. Huber,et al.  Structure of human des(1-45) factor Xa at 2.2 A resolution. , 1993, Journal of molecular biology.

[45]  M. Lill Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. , 2011, Biochemistry.

[46]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[47]  David E. Shaw,et al.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results , 2006, J. Comput. Aided Mol. Des..