Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules

Selvaraj Muthukrishna, Munish Puri

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Objectives: The arrival of free oxygen on the globe, aerobic life is becoming possible. However, it has become very clear that the oxygen binding proteins are widespread in the biosphere and are found in all groups of organisms, including prokaryotes, eukaryotes as well as in fungi, plants, and animals. The exponential growth and availability of fresh annotated protein sequences in the databases motivated us to develop an improved version of "Oxypred" for identifying oxygen-binding proteins. Results: In this study, we have proposed a method for identifying oxy-proteins with two different sequence similarity cutoffs 50 and 90%. A different amino acid composition based Support Vector Machines models was developed, including the evolutionary profiles in the form position-specific scoring matrix (PSSM). The fivefold cross-validation techniques were applied to evaluate the prediction performance. Also, we compared with existing methods, which shows nearly 97% recognition, but, our newly developed models were able to recognize almost 99.99 and 100% in both oxy-50 and 90% similarity models respectively. Our result shows that our approaches are faster and achieve a better prediction performance over the existing methods. The web-server Oxypred2 was developed for an alternative method for identifying oxy-proteins with more additional modules including PSSM, available at http://bioinfo.imtech.res.in/servers/muthu/oxypred2/home.html.

Original languageEnglish
Article number290
Number of pages8
JournalBMC Research Notes
Issue number1
Publication statusPublished - 11 May 2018


  • Confusion matrix
  • Erythrocruorin
  • Hemerythrin
  • Hemocyanin
  • Hemoglobin
  • Leghemoglobin
  • Myoglobin
  • Oxygen binding proteins
  • ROC Analysis
  • Support Vector Machines


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