Testing and improving the performance of protein thermostability predictors for the engineering of cellulases.

IF 0.9 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Journal of Bioinformatics and Computational Biology Pub Date : 2023-04-01 DOI:10.1142/S0219720023300010

Anna Dotsenko, Jury Denisenko, Dmitrii Osipov, Aleksandra Rozhkova, Ivan Zorov, Arkady Sinitsyn

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引用次数: 1

Abstract

Thermostability of cellulases can be increased through amino acid substitutions and by protein engineering with predictors of protein thermostability. We have carried out a systematic analysis of the performance of 18 predictors for the engineering of cellulases. The predictors were PoPMuSiC, HoTMuSiC, I-Mutant 2.0, I-Mutant Suite, PremPS, Hotspot, Maestroweb, DynaMut, ENCoM ([Formula: see text] and [Formula: see text], mCSM, SDM, DUET, RosettaDesign, Cupsat (thermal and denaturant approaches), ConSurf, and Voronoia. The highest values of accuracy, F-measure, and MCC were obtained for DynaMut, SDM, RosettaDesign, and PremPS. A combination of the predictors provided an improvement in the performance. F-measure and MCC were improved by 14% and 28%, respectively. Accuracy and sensitivity were also improved by 9% and 20%, respectively, compared to the maximal values of single predictors. The reported values of the performance of the predictors and their combination may aid research in the engineering of thermostable cellulases as well as the further development of thermostability predictors.

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纤维素酶工程中蛋白质热稳定性预测因子的测试与改进。

纤维素酶的热稳定性可以通过氨基酸取代和蛋白质工程来提高。我们对18种纤维素酶工程预测因子的性能进行了系统的分析。预测因子为PoPMuSiC、HoTMuSiC、I-Mutant 2.0、I-Mutant Suite、PremPS、Hotspot、Maestroweb、DynaMut、ENCoM([公式:见文]和[公式:见文])、mCSM、SDM、DUET、RosettaDesign、Cupsat(热变性方法)、ConSurf和Voronoia。DynaMut、SDM、RosettaDesign和PremPS的准确度、F-measure和MCC值最高。这些预测因素的组合提高了性能。F-measure和MCC分别提高了14%和28%。与单一预测因子的最大值相比，准确度和灵敏度也分别提高了9%和20%。所报道的预测因子及其组合的性能值可能有助于热稳定性纤维素酶的工程研究以及热稳定性预测因子的进一步开发。

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来源期刊

Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

期刊介绍： The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.