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AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes.
1Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Plos One
|October 17, 2013
Summary
This study introduces a new sequence-based method to accurately predict acidic and alkaline enzymes. The developed AcalPred web-server aids in understanding enzyme adaptation to extreme pH environments.
Area of Science:
- Biochemistry
- Bioinformatics
- Enzymology
Background:
- Enzyme structure and activity are significantly influenced by environmental pH.
- While many enzymes function optimally between pH 6-8, some exhibit high efficiency in acidic (pH<5) or alkaline (pH>9) conditions.
- Enzyme activity correlates with amino acid sequences, making sequence analysis crucial for understanding pH adaptation and designing efficient enzymes.
Purpose of the Study:
- To develop a sequence-based computational method for discriminating between acidic and alkaline enzymes.
- To establish a reliable prediction model for enzyme adaptation to extreme pH environments.
- To provide a user-friendly tool for researchers studying enzyme function at different pH levels.
Main Methods:
- Feature selection using analysis of variance (ANOVA) on g-gap dipeptide compositions.
- Development of a prediction model using support vector machine (SVM).
- Rigorous evaluation through jackknife cross-validation.
Main Results:
- The developed method achieved an overall prediction accuracy of 96.7%.
- High prediction accuracies were obtained for both acidic enzymes (96.3%) and alkaline enzymes (97.1%).
- The proposed method demonstrated superior accuracy compared to previous approaches.
Conclusions:
- A novel and accurate sequence-based method, AcalPred, has been developed to predict acidic and alkaline enzymes.
- AcalPred is available as an online web-server, facilitating research on enzyme adaptation.
- This tool is expected to be valuable for molecular mechanism clarification and the design of novel, highly efficient enzymes.
