Oxypred: Prediction and Classification of Oxygen-Binding Proteins
- Author:
Muthukrishnan S.
1
;
Garg AARTI
;
Raghava G.P.S.
Author Information
1. Institute of Microbial Technology, Sector 39-A, Chandigarh 160036, India
- Keywords:
oxygen-binding proteins;
SVM modules;
hemoglobin;
web server;
prediction
- From:
Genomics, Proteomics & Bioinformatics
2007;2(3):250-252
- CountryChina
- Language:Chinese
-
Abstract:
This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).
