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Open AccessArticle

A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV)

1
Laboratório de Bioinformática e Sistemas (LBS), Department of Computer Science, Universidade Federal de Minas Gerais, 31270-901 Belo Horizonte, Brazil
2
Laboratório de Biologia Computacional (COMBI-L). Centro de Ciências Computacionais-C3, Universidade Federal do Rio Grande, 96203-900 Rio Grande, Brazil
3
Laboratório de Modelagem Molecular e Bioinformática (LAMMB), Departamento de Ciências Exatas e Biológicas (DECEB). Universidade Federal de São João Del-Rei, Campus Sete Lagoas, 35701-970 Sete Lagoas, Brazil
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(2), 333; https://doi.org/10.3390/ijms20020333
Received: 20 November 2018 / Revised: 29 December 2018 / Accepted: 6 January 2019 / Published: 15 January 2019
(This article belongs to the Special Issue Industrial Enzymes: Structure, Function and Applications)
With the use of genetic engineering, modified and sometimes more efficient enzymes can be created for different purposes, including industrial applications. However, building modified enzymes depends on several in vitro experiments, which may result in the process being expensive and time-consuming. Therefore, computational approaches could reduce costs and accelerate the discovery of new technological products. In this study, we present a method, called structural signature variation (SSV), to propose mutations for improving enzymes’ activity. SSV uses the structural signature variation between target enzymes and template enzymes (obtained from the literature) to determine if randomly suggested mutations may provide some benefit for an enzyme, such as improvement of catalytic activity, half-life, and thermostability, or resistance to inhibition. To evaluate SSV, we carried out a case study that suggested mutations in β-glucosidases: Essential enzymes used in biofuel production that suffer inhibition by their product. We collected 27 mutations described in the literature, and manually classified them as beneficial or not. SSV was able to classify the mutations with values of 0.89 and 0.92 for precision and specificity, respectively. Then, we used SSV to propose mutations for Bgl1B, a low-performance β-glucosidase. We detected 15 mutations that could be beneficial. Three of these mutations (H228C, H228T, and H228V) have been related in the literature to the mechanism of glucose tolerance and stimulation in GH1 β-glucosidase. Hence, SSV was capable of detecting promising mutations, already validated by in vitro experiments, that improved the inhibition resistance of a β-glucosidase and, consequently, its catalytic activity. SSV might be useful for the engineering of enzymes used in biofuel production or other industrial applications. View Full-Text
Keywords: enzymes; prediction of mutations; second-generation biofuel enzymes; prediction of mutations; second-generation biofuel
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Mariano, D.C.B.; Santos, L.H.; Machado, K.S.; Werhli, A.V.; de Lima, L.H.F.; de Melo-Minardi, R.C. A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV). Int. J. Mol. Sci. 2019, 20, 333.

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