Lipase B from Candida antarctica (CaLB) is one of the most largely employed biocatalysts for the synthesis of chiral fine chemicals. The successful application of this enzyme has also been promoted by advanced computational methods able to simulate enantiodiscrimination at molecular and energy level. Quantitative prediction of enantioselectivity remains a challenging task, affordable by means of sophisticated and rigorous QM/MM methods or by hybrid methods that combine molecular mechanics with experimental data and regression analysis. Most of the methods reported in the literature aim to predict CaLB enantiopreference and to understand the structural basis of enantiodiscrimination. Various experimental problems, such as resolution of alcohols, amines and carboxylic acids, solvent effect, entropic contribution of substrates, are expected to receive beneficial indications from novel advanced computational methods. However, the choice of the appropriate strategy is crucial for success in solving specific problems within a realistic time frame and with a convenient computational cost. In order to be competitive with experimental work, the rational and computational approach should be ideally within a high throughput scheme. Therefore, automation of computational procedures, software and scoring steps represents a new emerging and promising perspective to make the planning of biotransformation more effective and rational.

Modelling and Predicting Enzyme Enantioselectivity: the Aid of Computational Methods for the Rational use of Lipase B from Candida antarctica

FERRARIO, VALERIO;EBERT, CYNTHIA;NITTI, PATRIZIA;PITACCO, GIULIANA;GARDOSSI, Lucia
2015

Abstract

Lipase B from Candida antarctica (CaLB) is one of the most largely employed biocatalysts for the synthesis of chiral fine chemicals. The successful application of this enzyme has also been promoted by advanced computational methods able to simulate enantiodiscrimination at molecular and energy level. Quantitative prediction of enantioselectivity remains a challenging task, affordable by means of sophisticated and rigorous QM/MM methods or by hybrid methods that combine molecular mechanics with experimental data and regression analysis. Most of the methods reported in the literature aim to predict CaLB enantiopreference and to understand the structural basis of enantiodiscrimination. Various experimental problems, such as resolution of alcohols, amines and carboxylic acids, solvent effect, entropic contribution of substrates, are expected to receive beneficial indications from novel advanced computational methods. However, the choice of the appropriate strategy is crucial for success in solving specific problems within a realistic time frame and with a convenient computational cost. In order to be competitive with experimental work, the rational and computational approach should be ideally within a high throughput scheme. Therefore, automation of computational procedures, software and scoring steps represents a new emerging and promising perspective to make the planning of biotransformation more effective and rational.
http://benthamscience.com/journals/current-biotechnology/volume/4/issue/2/page/87/
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2844905
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