An Accurate Metalloprotein-Specific Scoring Function and Molecular Docking Program Devised by a Dynamic Sampling and Iteration Optimization Strategy

Metalloproteins, particularly zinc metalloproteins, are promising therapeutic targets, and recent efforts have focused on the identification of potent and selective inhibitors of these proteins. However, the ability of current drug discovery and design technologies, such as molecular docking and molecular dynamics simulations, to probe metal-ligand interactions remains limited because of their complicated coordination geometries and rough treatment in current force fields. Herein we introduce a robust, multiobjective optimization algorithm-driven metalloprotein-specific docking program named MpSDock, which runs on a scheme similar to consensus scoring consisting of a force-field-based scoring function and a knowledge-based scoring function. For this purpose, in this study, an effective knowledge-based zinc metalloprotein-specific scoring function based on the inverse Boltzmann law was designed and optimized using a dynamic sampling and iteration optimization strategy. This optimization strategy can dynamically sample and regenerate decoy poses used in each iteration step of refining the scoring function, thus dramatically improving both the effectiveness of the exploration of the binding conformational space and the sensitivity of the ranking of the native binding poses. To validate the zinc metalloprotein-specific scoring function and its special built-in docking program, denoted MpSDockZn, an extensive comparison was performed against six universal, popular docking programs: Glide XP mode, Glide SP mode, Gold, AutoDock, AutoDock4Zn, and EADock DSS. The zinc metalloprotein-specific knowledge-based scoring function exhibited prominent performance in accurately describing the geometries and interactions of the coordination bonds between the zinc ions and chelating agents of the ligands. In addition, MpSDockZn had a competitive ability to sample and identify native binding poses with a higher success rate than the other six docking programs.

[1]  Ruibo Wu,et al.  A Transferable Non-bonded Pairwise Force Field to Model Zinc Interactions in Metalloproteins. , 2011, Journal of chemical theory and computation.

[2]  John Roderick,et al.  Discovery of novel hydantoins as selective non-hydroxamate inhibitors of tumor necrosis factor-alpha converting enzyme (TACE). , 2007, Bioorganic & medicinal chemistry letters.

[3]  P. Schoenmakers,et al.  Automatic selection of optimal Savitzky-Golay smoothing. , 2006, Analytical chemistry.

[4]  I. Muegge.  PMF scoring revisited. , 2006, Journal of medicinal chemistry.

[5]  Angelo Vedani,et al.  A new force field for modeling metalloproteins , 1990 .

[6]  Wei Wang,et al.  Angiotensin-I-converting enzyme inhibitory peptides: Chemical feature based pharmacophore generation. , 2011, European journal of medicinal chemistry.

[7]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[8]  Xiaomin Luo,et al.  PDTD: a web-accessible protein database for drug target identification , 2008, BMC Bioinformatics.

[9]  P A Kollman,et al.  Calculation and prediction of binding free energies for the matrix metalloproteinases. , 2000, Journal of medicinal chemistry.

[10]  Joannis Apostolakis,et al.  GlamDock: Development and Validation of a New Docking Tool on Several Thousand Protein-Ligand Complexes , 2007, J. Chem. Inf. Model..

[11]  Andreas Steffen,et al.  AIScore Chemically Diverse Empirical Scoring Function Employing Quantum Chemical Binding Energies of Hydrogen-Bonded Complexes , 2008, J. Chem. Inf. Model..

[12]  Vincent Zoete,et al.  Toward On-The-Fly Quantum Mechanical/Molecular Mechanical (QM/MM) Docking: Development and Benchmark of a Scoring Function , 2014, J. Chem. Inf. Model..

[13]  D. Baker,et al.  An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. , 2003, Journal of molecular biology.

[14]  Gerhard Klebe,et al.  AffinDB: a freely accessible database of affinities for protein–ligand complexes from the PDB , 2005, Nucleic Acids Res..

[15]  Arthur J. Olson,et al.  AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins , 2014, J. Chem. Inf. Model..

[16]  SHENG-YOU HUANG,et al.  An iterative knowledge‐based scoring function to predict protein–ligand interactions: I. Derivation of interaction potentials , 2006, J. Comput. Chem..

[17]  Kenneth M Merz,et al.  Structural Survey of Zinc Containing Proteins and the Development of the Zinc AMBER Force Field (ZAFF). , 2010, Journal of chemical theory and computation.

[18]  Natalia Artemenko,et al.  Distance Dependent Scoring Function for Describing Protein-Ligand Intermolecular Interactions , 2008, J. Chem. Inf. Model..

[19]  Kirti Patel,et al.  Analysis of the structural consensus of the zinc coordination centers of metalloprotein structures. , 2007, Biochimica et biophysica acta.

[20]  Yuzhen Ye,et al.  Solution structure of Mycobacterium tuberculosis NmtR in the apo state: insights into Ni(II)-mediated allostery. , 2012, Biochemistry.

[21]  Hualiang Jiang,et al.  Knowledge-Based Scoring Functions in Drug Design. 1. Developing a Target-Specific Method for Kinase-Ligand Interactions , 2010, J. Chem. Inf. Model..

[22]  Jing Li,et al.  Knowledge-Based Scoring Functions in Drug Design: 3. A Two-Dimensional Knowledge-Based Hydrogen-Bonding Potential for the Prediction of Protein-Ligand Interactions , 2011, J. Chem. Inf. Model..

[23]  Thomas Stützle,et al.  Empirical Scoring Functions for Advanced Protein-Ligand Docking with PLANTS , 2009, J. Chem. Inf. Model..

[24]  I. Muegge.  Effect of ligand volume correction on PMF scoring , 2001, J. Comput. Chem..

[25]  Xiaoqin Zou,et al.  An iterative knowledge‐based scoring function for protein–protein recognition , 2008, Proteins.

[26]  D. Baker,et al.  A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys. , 2004, Nucleic acids research.

[27]  Zhi Chen,et al.  An Improved PMF Scoring Function for Universally Predicting the Interactions of a Ligand with Protein, DNA, and RNA , 2008, J. Chem. Inf. Model..

[28]  Rolf Schiffmann,et al.  Metal-mediated inhibition of Escherichia coli methionine aminopeptidase: structure-activity relationships and development of a novel scoring function for metal-ligand interactions. , 2006, Journal of medicinal chemistry.

[29]  Y. Pang.  Successful molecular dynamics simulation of two zinc complexes bridged by a hydroxide in phosphotriesterase using the cationic dummy atom method , 2001, Proteins.

[30]  Cristina Nativi,et al.  Structure-based approach to nanomolar, water soluble matrix metalloproteinases inhibitors (MMPIs). , 2010, European journal of medicinal chemistry.

[31]  Alessio Innocenti,et al.  Carbonic anhydrase inhibitors. Synthesis, molecular structures, and inhibition of the human cytosolic isozymes I and II and transmembrane isozymes IX, XII (cancer-associated) and XIV with novel 3-pyridinesulfonamide derivatives. , 2011, European journal of medicinal chemistry.

[32]  Aurélien Grosdidier,et al.  SwissDock, a protein-small molecule docking web service based on EADock DSS , 2011, Nucleic Acids Res..

[33]  K. Dill,et al.  Statistical potentials extracted from protein structures: how accurate are they? , 1996, Journal of molecular biology.

[34]  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..

[35]  Xin Hu,et al.  Docking studies of matrix metalloproteinase inhibitors: zinc parameter optimization to improve the binding free energy prediction. , 2003, Journal of molecular graphics & modelling.

[36]  Robert J. Deeth,et al.  Molecular modelling for transition metal complexes: Dealing with d-electron effects , 2009 .

[37]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[38]  Alexander D. MacKerell.  Empirical force fields for biological macromolecules: Overview and issues , 2004, J. Comput. Chem..

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

[40]  Luhua Lai,et al.  SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex , 1998 .

[41]  Renxiao Wang,et al.  Systematic Derivation of AMBER Force Field Parameters Applicable to Zinc-Containing Systems. , 2010, Journal of chemical theory and computation.

[42]  Antonio Rosato,et al.  Counting the zinc-proteins encoded in the human genome. , 2006, Journal of proteome research.

[43]  Bing Wang,et al.  Simulations of allosteric motions in the zinc sensor CzrA. , 2012, Journal of the American Chemical Society.

[44]  Kentaro Kawai,et al.  Reverse hydroxamate-based selective TACE inhibitors. , 2004, Bioorganic & medicinal chemistry letters.

[45]  Philippe Grellier,et al.  Towards the synthesis of bisubstrate inhibitors of protein farnesyltransferase: Synthesis and biological evaluation of new farnesylpyrophosphate analogues. , 2010, Bioorganic & medicinal chemistry.

[46]  Jorge J. Moré,et al.  The NEOS Server , 1998 .

[47]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[48]  Jing Li,et al.  Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched? , 2011, J. Chem. Inf. Model..

[49]  Kenneth M Merz,et al.  Finding a needle in the haystack: computational modeling of Mg2+ binding in the active site of protein farnesyltransferase. , 2010, Biochemistry.

[50]  Xiaoqin Zou,et al.  Inclusion of Solvation and Entropy in the Knowledge-Based Scoring Function for Protein-Ligand Interactions , 2010, J. Chem. Inf. Model..

[51]  Fedor N. Novikov,et al.  Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening. , 2008, Journal of chemical information and modeling.

[52]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[53]  Xiaoqin Zou,et al.  An iterative knowledge‐based scoring function to predict protein–ligand interactions: II. Validation of the scoring function , 2006, J. Comput. Chem..

[54]  Naoya Nagata,et al.  Metal-ligand interactions: an analysis of zinc binding groups using the Protein Data Bank. , 2012, European journal of medicinal chemistry.

[55]  Haruki Nakamura,et al.  Announcing the worldwide Protein Data Bank , 2003, Nature Structural Biology.

[56]  Xicheng Wang,et al.  An improved adaptive genetic algorithm for protein–ligand docking , 2009, J. Comput. Aided Mol. Des..

[57]  E. Lomba,et al.  Determination of the interaction potential from the pair distribution function: an inverse Monte Carlo technique. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  George A. Kaminski,et al.  Electrostatic polarization is crucial in reproducing Cu(I) interaction energies and hydration. , 2011, The journal of physical chemistry. B.

[59]  D. Baker,et al.  Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[60]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[61]  Jean-Philip Piquemal,et al.  Polarizable molecular dynamics simulation of Zn(II) in water using the AMOEBA force field. , 2010, Journal of Chemical Theory and Computation.

[62]  Kenneth M. Merz,et al.  Force Field Design for Metalloproteins , 1991 .

[63]  Zhihai Liu,et al.  Comparative Assessment of Scoring Functions on a Diverse Test Set , 2009, J. Chem. Inf. Model..

[64]  Dhruva K. Chakravorty,et al.  Solution NMR refinement of a metal ion bound protein using metal ion inclusive restrained molecular dynamics methods , 2013, Journal of biomolecular NMR.

[65]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[66]  Laura Sola,et al.  Design and synthesis of novel isoxazole-based HDAC inhibitors. , 2010, European journal of medicinal chemistry.

[67]  Z. R. Li,et al.  PEARLS: Program for Energetic Analysis of Receptor-Ligand System , 2006, J. Chem. Inf. Model..

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

[69]  Richard D. Taylor,et al.  Improved protein–ligand docking using GOLD , 2003, Proteins.