Pseudo amino acid composition improves antifreeze protein prediction
- Published
- Accepted
- Subject Areas
- Computational Biology
- Keywords
- Convergent evolution, Support Vector Machines, Ten-fold cross-validation, Sequence order effect, Pseudo amino acid composition
- Copyright
- © 2014 Mondal et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Pseudo amino acid composition improves antifreeze protein prediction. PeerJ PrePrints 2:e224v1 https://doi.org/10.7287/peerj.preprints.224v1
Abstract
Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area did not cover introduction of sequence order information, known to represent important properties of various proteins and protein systems for prediction of their attributes. In this study, the effect of Chou's pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youden’s Index (Sensitivity + Specificity -1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.