Modulation Of Enzyme Functional Propertiesстатья

Работа с статьей

[1] Modulation of enzyme functional properties / D. A. Suplatov, N. K. Panin, K. E. Kopylov, V. K. Švedas // Acta Naturae (русскоязычная версия, Спецвыпуск). — 2016. — Vol. 2. — P. 222–223. Modulation of enzyme functional properties is one of the challenging tasks of modern biotechnology and bioengineering. It can be done in two principally different ways – by changing protein structure or due to binding of modulating molecules (ligands, effectors,stabilizing agents). Extended substrate profile, increased stereoselectivity or improved enzyme stability is often achieved by implementing selective mutations. It can be rationalized nowadays by exploiting methods of bioinformatics, molecular modeling and computational screening of created in silico libraries of enzyme mutants. The same set of methods can be applied to search for modulating molecules and their binding sites. Until recently, the major interest was focused on studying the active sites of enzymes and search of competitive inhibitors that prevent binding of substrates and cofactors. However, computational structure analysis has revealed that enzymes along with previously annotated active sites possess a significant amount of previously unexplored potential binding pockets with non-identified functional role [Suplatov et al., 2014]. This comes along with the recently made suggestion that allostery – regulation of protein function by binding of low-molecular weight compounds in topologically independent regulatory sites – may be an intrinsic property of virtually all proteins [Gunasekaran et al., 2004]. It is of great interest to identify and characterize new binding sites in protein structures and understand their role in modulation of protein function [Suplatov & Švedas, 2015]. We have developed and evaluated new methodology that combines methods of bioinformatics, molecular modeling, theoretical chemistry and high-performance computing and helps to develop different approaches to modulate enzyme activity, selectivity and stability. The developed method was applied to understand structure-function relationship in several enzyme families: Ntn-hydrolases, penicillin-binding proteins, α/β-hydrolases. Function-related variable positions in corresponding enzyme families were identified and used as hotspots for mutations to increase stability and synthetic activity of penicillin acylase from Escherichia coli, expand substrate specificity of D-aminopeptidase from Ochrobactrum anthropi and introduce amidase activity into Candida antarctica lipase B [Shcherbakova et al., 2014; Suplatov et al., 2012, 2014, 2015; Khaliullin et al., 2013; 4-8]. Molecular modeling of in silico constructed mutants was used to evaluate effect of substitutions at function-related positions on stability as well as catalytic properties and to select the most promising variants for experimental evaluation. Isolated mutants of penicillin acylase, D-aminopeptidase and lipase B demonstrated significantly improved functional properties. The methodology has been also applied to search for previously unknown binding sites in the structure of the glyceraldehyde-3-phosphate dehydrogenase superfamily of enzymes and design specific inhibitors of these enzymes with a novel mechanism of action. The experimental study has shown that compounds selected by the computer screening are selective inhibitors of glyceraldehyde-3-phosphate dehydrogenase from Mycobacteria and do not supress activity of the human homolog. Analysis of the literature and DrugBank database has shown that the identified compounds have never been reported as glyceraldehyde-3-phosphate inhibitors and this property has been demonstrated for the first time. Our methodology can be used as a systematic tool to study structure-function relationship, characterize and rank enzyme binding sites, select function-related positions and use them as hotspots for mutation to rationalize different protein engineering approaches and design enzymes with requested functional properties. This work was supported by the Russian Science Foundation (grant #15-14-00069).

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