Dataset Information

EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.

ABSTRACT: When bioprospecting for novel industrial enzymes, substrate promiscuity is a desirable property that increases the reusability of the enzyme. Among industrial enzymes, ester hydrolases have great relevance for which the demand has not ceased to increase. However, the search for new substrate promiscuous ester hydrolases is not trivial since the mechanism behind this property is greatly influenced by the active site's structural and physicochemical characteristics. These characteristics must be computed from the 3D structure, which is rarely available and expensive to measure, hence the need for a method that can predict promiscuity from sequence alone. Here we report such a method called EP-pred, an ensemble binary classifier, that combines three machine learning algorithms: SVM, KNN, and a Linear model. EP-pred has been evaluated against the Lipase Engineering Database together with a hidden Markov approach leading to a final set of ten sequences predicted to encode promiscuous esterases. Experimental results confirmed the validity of our method since all ten proteins were found to exhibit a broad substrate ambiguity.

SUBMITTER: Xiang R

PROVIDER: S-EPMC9599548 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.

Xiang Ruite R Fernandez-Lopez Laura L Robles-Martín Ana A Ferrer Manuel M Guallar Victor V

Biomolecules 20221021 10

When bioprospecting for novel industrial enzymes, substrate promiscuity is a desirable property that increases the reusability of the enzyme. Among industrial enzymes, ester hydrolases have great relevance for which the demand has not ceased to increase. However, the search for new substrate promiscuous ester hydrolases is not trivial since the mechanism behind this property is greatly influenced by the active site's structural and physicochemical characteristics. These characteristics must be c ...[more]

PMID: 36291739

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Project description:The harnessing of biocatalysts from extreme environment hot spring niche for biomass conversion is significant and promising owing to the special characteristics of extremozymes attributed by intriguing biogeochemistry and extreme conditions of these environments. Hence, in the present study 38 bacterial isolates obtained from hot springs of Manikaran (~ 95 °C), Kalath (~ 50 °C) and Vasist (~ 65 °C) of Himachal Pradesh were screened for glycosyl hydrolases by in situ enrichment technique using lignocellulosic biomass (LCB). Based on their hydrolytic potential 5 isolates were selected and they were Bacillus tequilensis (VCB1, VCB2 and VSDB4), and B. licheniformis (KBFB2 and KBFB3). Cellulolytic activity assayed by growth under submerged fermentation showed that B. tequilensis VCB1 had maximum FPA activity (3.38 IU ml-1) in 48 h, while B. licheniformis KBFB3 excelled for endoglucanase (EGA of 4.81 IU ml-1 in 24 h) and cellobiase (0.71 IU ml-1 in 48 h) activities. Among all the thermophilic biocatalysts evaluated, highest exoglucanase (0.06 IU ml-1) activity was observed in B. tequilensis VSDB4 while endoglucanase of B. licheniformis KBFB3 showed optimum specific activity at pH 7 and 70 °C. Further, the presence of celS, celB and xlnB genes in the isolates suggest their possible role in biomass conversion. Protein profiling by SDS-PAGE analysis revealed that cellulase isoforms migrated with molecular masses of 75 kDa. The endoglucanase activity of promising strain B. licheniformis KBFB3 was enhanced in the presence of Ca2+, mercaptoethanol and sodium hypochlorite whereas moderately inhibited by Cu2+, Zn2+, urea, SDS and H2O2. The results of this study indicate scope for the possible development of novel biocatalysts with multifunctional thermostable glycosyl hydrolases from hot springs for efficient hydrolysis of the complex lignocellulosic biomass into simple sugars and other derived bioproducts leading to biomass valorization.

Dataset Information

EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.

Publications

EP-Pred: A Machine Learning Tool for Bioprospecting Promiscuous Ester Hydrolases.

Similar Datasets

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