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

James J. P. Stewart

doi:10.1007/s00894-016-3119-5

Abstract

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.

Highlights

BackgroundFactual knowledge of how enzymes catalyze reactions comes from several sources, of which the more important are biochemical experimentation, X-ray analysis, and NMR analysis
The semiempirical method PM7 [3] has been shown to be useful for detecting errors in the X-ray structures of proteins [4], removing some of these errors [5], and exploring the applicability of these methods to the modeling of the entire MTH1 enzyme [6], a system within the Protein Data Bank [7] (PDB) file 3ZR0
A similar prediction was obtained using the B3LYP [28] functional with the DGDZVP basis set in Gaussian 09 [29] for both 8-oxo-dGMP and dGMP, with the syn-keto-keto being 3.39 kcal mol−1 more stable than the antiketo-keto and the syn-keto being more stable by 1.74 kcal mol−1 than the anti-keto, respectively

Summary

Introduction

Factual knowledge of how enzymes catalyze reactions comes from several sources, of which the more important are biochemical experimentation, X-ray analysis, and NMR analysis. In recent years these sources of data have been augmented by the development of computational chemistry modeling tools that can be used for investigating and understanding protein– ligand interactions (for reviews, see [1, 2]). The semiempirical method PM7 [3] has been shown to be useful for detecting errors in the X-ray structures of proteins [4], removing some of these errors [5], and exploring the applicability of these methods to the modeling of the entire MTH1 enzyme [6], a system within the Protein Data Bank [7] (PDB) file 3ZR0. With the possible exception of the POLARIS model of the program MOLARIS [8], what has not been available has been a simple method for

Methods

Results

Discussion

Conclusion