Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules.
BMC Res Notes
; 11(1): 290, 2018 May 11.
Article
em En
| MEDLINE
| ID: mdl-29751818
ABSTRACT
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 .Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Oxigênio
/
Hemocianinas
/
Proteínas de Transporte
/
Evolução Biológica
/
Máquina de Vetores de Suporte
/
Hemeproteínas
/
Hemeritrina
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
BMC Res Notes
Ano de publicação:
2018
Tipo de documento:
Article