Oxypred: prediction and classification of oxygen-binding proteins.
This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying 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, hemocyanin, 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/).
Muthukrishnan
S
S
Institute of Microbial Technology, Sector 39-A, Chandigarh 160036, India.
Garg
Aarti
A
Raghava
G P S
GP
D000596:Amino Acids / Q000032:analysis; D000818:Animals; D001185:Artificial Intelligence; D030562:Databases, Protein; D006420:Hemeproteins / Q000737:chemistry* / Q000145:classification* / Q000378:metabolism; D006801:Humans; D020407:Internet; D010100:Oxygen / Q000378:metabolism
D000596:Amino Acids; D006420:Hemeproteins; D010100:Oxygen