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Analysis of Cap-binding Proteins in Human Cells Exposed to Physiological Oxygen Conditions
Published on: December 28, 2016
Oxypred: prediction and classification of oxygen-binding proteins
S Muthukrishnan1, Aarti Garg, G P S Raghava
1Institute of Microbial Technology, Sector 39-A, Chandigarh 160036, India.
Genomics, Proteomics & Bioinformatics
|February 13, 2008
Summary
This study introduces Oxypred, a web server for identifying and categorizing oxygen-binding proteins. It utilizes support vector machine modules with high accuracy for predicting and classifying proteins like hemoglobin and myoglobin.
Area of Science:
- Bioinformatics
- Computational Biology
- Protein Science
Background:
- Oxygen-binding proteins are crucial for biological systems.
- Accurate prediction and classification of these proteins are vital for research.
Purpose of the Study:
- To develop a computational method for predicting and classifying oxygen-binding proteins.
- To create a user-friendly web server for this purpose.
Main Methods:
- Support Vector Machine (SVM) modules were developed using amino acid and dipeptide composition.
- Five-fold cross-validation was employed for training and testing.
- A web server, Oxypred, was implemented based on the developed modules.
Main Results:
- Prediction modules achieved accuracies of 85.5% (amino acid composition) and 87.8% (dipeptide composition).
- Classification modules reached high accuracies for six oxygen-binding protein classes, with some exceeding 99%.
- The Oxypred web server provides a practical tool for protein analysis.
Conclusions:
- The developed SVM-based approach is effective for predicting and classifying oxygen-binding proteins.
- Oxypred offers a valuable resource for researchers in protein science and bioinformatics.
- The study demonstrates the utility of sequence composition in protein function prediction.
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