A method for predicting individual residue contributions to enzyme specificity and binding-site energies, and its application to MTH1

A new method for predicting the energy contributions to substrate binding and to specificity has been developed. Conventional global optimization methods do not permit the subtle effects responsible for these properties to be modeled with sufficient precision to allow confidence to be placed in the results, but by making simple alterations to the model, the precisions of the various energies involved can be improved from about ±2 kcal mol−1 to ±0.1 kcal mol−1. This technique was applied to the oxidized nucleotide pyrophosphohydrolase enzyme MTH1. MTH1 is unusual in that the binding and reaction sites are well separated—an advantage from a computational chemistry perspective, as it allows the energetics involved in docking to be modeled without the need to consider any issues relating to reaction mechanisms. In this study, two types of energy terms were investigated: the noncovalent interactions between the binding site and the substrate, and those responsible for discriminating between the oxidized nucleotide 8-oxo-dGTP and the normal dGTP. Both of these were investigated using the semiempirical method PM7 in the program MOPAC. The contributions of the individual residues to both the binding energy and the specificity of MTH1 were calculated by simulating the effect of mutations. Where comparisons were possible, all calculated results were in agreement with experimental observations. This technique provides fresh insight into the binding mechanism that enzymes use for discriminating between possible substrates.

[1]  H. Kamiya,et al.  Human MTH1 protein hydrolyzes the oxidized ribonucleotide, 2-hydroxy-ATP. , 2001, Nucleic acids research.

[2]  William A. Goddard,et al.  pKa Values of Guanine in Water: Density Functional Theory Calculations Combined with Poisson-Boltzmann Continuum-Solvation Model , 2003 .

[3]  M. Shirakawa,et al.  A Molecular Basis for the Selective Recognition of 2-Hydroxy-dATP and 8-Oxo-dGTP by Human MTH1* , 2002, The Journal of Biological Chemistry.

[4]  P. Hobza Calculations on noncovalent interactions and databases of benchmark interaction energies. , 2012, Accounts of chemical research.

[5]  James J. P. Stewart,et al.  Application of localized molecular orbitals to the solution of semiempirical self‐consistent field equations , 1996 .

[6]  Michal Otyepka,et al.  Transferable scoring function based on semiempirical quantum mechanical PM6-DH2 method: CDK2 with 15 structurally diverse inhibitors , 2011, J. Comput. Aided Mol. Des..

[7]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

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

[9]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[10]  G. Scuseria,et al.  Gaussian 03, Revision E.01. , 2007 .

[11]  J. Breed,et al.  MTH1 Substrate Recognition—An Example of Specific Promiscuity , 2016, PloS one.

[12]  M. Shirakawa,et al.  Structure of Human MTH1, a Nudix Family Hydrolase That Selectively Degrades Oxidized Purine Nucleoside Triphosphates* , 2004, Journal of Biological Chemistry.

[13]  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..

[14]  Adam Pecina,et al.  The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. , 2016, Chemical communications.

[15]  Martin Korth,et al.  Third-Generation Hydrogen-Bonding Corrections for Semiempirical QM Methods and Force Fields , 2010 .

[16]  Pavel Hobza,et al.  A Transferable H-Bonding Correction for Semiempirical Quantum-Chemical Methods. , 2010, Journal of chemical theory and computation.

[17]  Pavel Hobza,et al.  Advanced Corrections of Hydrogen Bonding and Dispersion for Semiempirical Quantum Mechanical Methods. , 2012, Journal of chemical theory and computation.

[18]  Shina Caroline Lynn Kamerlin,et al.  hallenges in computational studies of enzyme structure , unction and dynamics , 2014 .

[19]  James J. P. Stewart,et al.  Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters , 2012, Journal of Molecular Modeling.

[20]  Kenneth M Merz,et al.  Refinement of protein crystal structures using energy restraints derived from linear-scaling quantum mechanics. , 2005, Acta crystallographica. Section D, Biological crystallography.

[21]  S. Grimme,et al.  A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. , 2010, The Journal of chemical physics.

[22]  Martin Korth,et al.  Recent Progress in Treating Protein–Ligand Interactions with Quantum-Mechanical Methods , 2016, International journal of molecular sciences.

[23]  James J. P. Stewart,et al.  Accuracy issues involved in modeling in vivo protein structures using PM7 , 2015, Proteins.

[24]  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.

[25]  Marcel L Verdonk,et al.  General and targeted statistical potentials for protein–ligand interactions , 2005, Proteins.

[26]  A. Becke Density-functional thermochemistry. III. The role of exact exchange , 1993 .

[27]  Jirí Cerný,et al.  Benchmark database of accurate (MP2 and CCSD(T) complete basis set limit) interaction energies of small model complexes, DNA base pairs, and amino acid pairs. , 2006, Physical chemistry chemical physics : PCCP.

[28]  Pavel Hobza,et al.  A reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 protease with 22 ligands. , 2010, The journal of physical chemistry. B.

[29]  Pavel Hobza,et al.  S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures , 2011, Journal of chemical theory and computation.

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

[31]  T. Helleday,et al.  Crystal structure of human MTH1 and the 8‐oxo‐dGMP product complex , 2011, FEBS letters.

[32]  James J. P. Stewart,et al.  An approach to creating a more realistic working model from a protein data bank entry , 2015, Journal of Molecular Modeling.

[33]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[34]  Li Fei Ji,et al.  Substituent effects on the properties of the hemi-bonded complexes (XH2P···NH2Y)+ (X, Y=H, F, Cl, Br, NH2, CH3, OH) , 2015, Journal of Molecular Modeling.

[35]  Pavel Hobza,et al.  A halogen-bonding correction for the semiempirical PM6 method , 2011 .

[36]  J. Stewart,et al.  A comparison of X-ray and calculated structures of the enzyme MTH1 , 2016, Journal of Molecular Modeling.

[37]  Y. Nakabeppu,et al.  Metabolic fate of oxidized guanine ribonucleotides in mammalian cells. , 1999, Biochemistry.

[38]  Arieh Warshel,et al.  Microscopic and semimicroscopic calculations of electrostatic energies in proteins by the POLARIS and ENZYMIX programs , 1993, J. Comput. Chem..

[39]  A. Klamt,et al.  COSMO : a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient , 1993 .