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Protein engineering of selected residues from conserved sequence regions of a novel Anoxybacillus ?-amylase.

ABSTRACT: The ?-amylases from Anoxybacillus species (ASKA and ADTA), Bacillus aquimaris (BaqA) and Geobacillus thermoleovorans (GTA, Pizzo and GtamyII) were proposed as a novel group of the ?-amylase family GH13. An ASKA yielding a high percentage of maltose upon its reaction on starch was chosen as a model to study the residues responsible for the biochemical properties. Four residues from conserved sequence regions (CSRs) were thus selected, and the mutants F113V (CSR-I), Y187F and L189I (CSR-II) and A161D (CSR-V) were characterised. Few changes in the optimum reaction temperature and pH were observed for all mutants. Whereas the Y187F (t1/2 43?h) and L189I (t1/2 36?h) mutants had a lower thermostability at 65°C than the native ASKA (t1/2 48?h), the mutants F113V and A161D exhibited an improved t1/2 of 51?h and 53?h, respectively. Among the mutants, only the A161D had a specific activity, k(cat) and k(cat)/K(m) higher (1.23-, 1.17- and 2.88-times, respectively) than the values determined for the ASKA. The replacement of the Ala-161 in the CSR-V with an aspartic acid also caused a significant reduction in the ratio of maltose formed. This finding suggests the Ala-161 may contribute to the high maltose production of the ASKA.

SUBMITTER: Ranjani V

PROVIDER: S-EPMC5376179 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Protein engineering of selected residues from conserved sequence regions of a novel Anoxybacillus α-amylase.

Ranjani Velayudhan V Janeček Stefan S Chai Kian Piaw KP Shahir Shafinaz S Abdul Rahman Raja Noor Zaliha Raja RN Chan Kok-Gan KG Goh Kian Mau KM

Scientific reports 20140728

The α-amylases from Anoxybacillus species (ASKA and ADTA), Bacillus aquimaris (BaqA) and Geobacillus thermoleovorans (GTA, Pizzo and GtamyII) were proposed as a novel group of the α-amylase family GH13. An ASKA yielding a high percentage of maltose upon its reaction on starch was chosen as a model to study the residues responsible for the biochemical properties. Four residues from conserved sequence regions (CSRs) were thus selected, and the mutants F113V (CSR-I), Y187F and L189I (CSR-II) and A1 ...[more]

PMID: 25069018

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Project description:BackgroundThe number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.ResultsTo determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features.ConclusionThe SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function.

Dataset Information

Protein engineering of selected residues from conserved sequence regions of a novel Anoxybacillus ?-amylase.

Publications

Protein engineering of selected residues from conserved sequence regions of a novel Anoxybacillus α-amylase.

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