Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling

Database screening using receptor-based pharmacophores is a computer-aided drug design technique that uses the structure of the target molecule (i.e. protein) to identify novel ligands that may bind to the target. Typically receptor-based pharmacophore modeling methods only consider a single or limited number of receptor conformations and map out the favorable binding patterns in vacuum or with a limited representation of the aqueous solvent environment, such that they may suffer from neglect of protein flexibility and desolvation effects. Site-Identification by Ligand Competitive Saturation (SILCS) is an approach that takes into account these, as well as other, properties to determine 3-dimensional maps of the functional group-binding patterns on a target receptor (i.e. FragMaps). In this study, a method to use the FragMaps to automatically generate receptor-based pharmacophore models is presented. It converts the FragMaps into SILCS pharmacophore features including aromatic, aliphatic, hydrogen-bond donor and acceptor chemical functionalities. The method generates multiple pharmacophore hypotheses that are then quantitatively ranked using SILCS grid free energies. The pharmacophore model generation protocol is validated using three different protein targets, including using the resulting models in virtual screening. Improved performance and efficiency of the SILCS derived pharmacophore models as compared to published docking studies, as well as a recently developed receptor-based pharmacophore modeling method is shown, indicating the potential utility of the approach in rational drug design.

[1]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[2]  A. Leach,et al.  Prediction of Protein—Ligand Interactions. Docking and Scoring: Successes and Gaps , 2006 .

[3]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[4]  Alexander D. MacKerell,et al.  Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations , 2004, J. Comput. Chem..

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

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

[7]  N. Vermeulen,et al.  The role of water molecules in computational drug design. , 2010, Current topics in medicinal chemistry.

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

[9]  Kevin Patel,et al.  Importance of Receptor Flexibility in Binding of Cyclam Compounds to the Chemokine Receptor CXCR4 , 2011, J. Chem. Inf. Model..

[10]  Alexander D. MacKerell,et al.  A small-molecule inhibitor of BCL6 kills DLBCL cells in vitro and in vivo. , 2010, Cancer cell.

[11]  Chandra Verma,et al.  Role of protein flexibility in the discovery of new drugs , 2011 .

[12]  Alexander D. MacKerell,et al.  Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation , 2009, PLoS Comput. Biol..

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

[14]  Felice C. Lightstone,et al.  Accounting for water molecules in drug design , 2011, Expert opinion on drug discovery.

[15]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1978, Archives of biochemistry and biophysics.

[16]  P. Goodford.  A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[17]  J. Bolin,et al.  Crystal structures of Escherichia coli and Lactobacillus casei dihydrofolate reductase refined at 1.7 A resolution. I. General features and binding of methotrexate. , 1982, The Journal of biological chemistry.

[18]  D. Joseph-McCarthy,et al.  Automated generation of MCSS‐derived pharmacophoric DOCK site points for searching multiconformation databases , 2003, Proteins.

[19]  Alexander D. MacKerell,et al.  Balancing target flexibility and target denaturation in computational fragment‐based inhibitor discovery , 2012, J. Comput. Chem..

[20]  Jeffrey M. Blaney,et al.  Structure-activity relationships of dihydrofolated reductase inhibitors , 1984 .

[21]  Simon Cross,et al.  GRID-Based Three-Dimensional Pharmacophores I: FLAPpharm, a Novel Approach for Pharmacophore Elucidation , 2012, J. Chem. Inf. Model..

[22]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[23]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[24]  Lennart Nilsson,et al.  Molecular dynamics simulation of galanin in aqueous and nonaqueous solution , 1992 .

[25]  S. Teague.  Implications of protein flexibility for drug discovery , 2003, Nature Reviews Drug Discovery.

[26]  P. Cozzini.  Target Flexibility: An Emerging Consideration in Drug Discovery and Design , 2009 .

[27]  Alexander D. MacKerell,et al.  Prospects of Modulating Protein–Protein Interactions , 2012 .

[28]  Felice C Lightstone,et al.  Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap , 2013, Expert opinion on drug discovery.

[29]  Lingle Wang,et al.  Ligand binding to protein-binding pockets with wet and dry regions , 2011, Proceedings of the National Academy of Sciences.

[30]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[31]  George W. A. Milne,et al.  Discovery of novel, non-peptide HIV-1 protease inhibitors by pharmacophore searching. , 1996, Journal of medicinal chemistry.

[32]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[33]  Jianpeng Ma,et al.  CHARMM: The biomolecular simulation program , 2009, J. Comput. Chem..

[34]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[35]  M. M. Sanders,et al.  From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling , 2012 .

[36]  Alexander D. MacKerell,et al.  Computational approaches for the design of protein–protein interaction inhibitors , 2013 .

[37]  Alexander D. MacKerell,et al.  Identification and validation of human DNA ligase inhibitors using computer-aided drug design. , 2008, Journal of medicinal chemistry.

[38]  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.

[39]  Markus A. Lill,et al.  Protein Pharmacophore Selection Using Hydration-Site Analysis , 2012, J. Chem. Inf. Model..

[40]  Alexander D. MacKerell,et al.  Inclusion of Multiple Fragment Types in the Site Identification by Ligand Competitive Saturation (SILCS) Approach , 2013, J. Chem. Inf. Model..

[41]  Richard A. Lewis,et al.  Three-dimensional pharmacophore methods in drug discovery. , 2010, Journal of medicinal chemistry.

[42]  Alexander D. MacKerell,et al.  The novel BH3 α-helix mimetic JY-1-106 induces apoptosis in a subset of cancer cells (lung cancer, colon cancer and mesothelioma) by disrupting Bcl-xL and Mcl-1 protein–protein interactions with Bak , 2013, Molecular Cancer.

[43]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..

[44]  Alexander D. MacKerell,et al.  Reproducing Crystal Binding Modes of Ligand Functional Groups Using Site-Identification by Ligand Competitive Saturation (SILCS) Simulations , 2011, J. Chem. Inf. Model..