Motif analysis and identification of antifreeze protein sequences

DOI

10.2991/iccsee.2013.236How to use a DOI?

Keywords

motif, AFPs, physicochemical characteristics, SVM, amino acid composition

Abstract

Antifreeze proteins (AFPs), which are also known as thermal hysteresis proteins, are ice-binding proteins. AFPs can adsorb to ice crystal surface and inhibit the growth of ice crystals in solution. But the interaction between AFPs and ice crystal is not known completely. Analyzing physicochemical characteristics of AFPs sequences is very significant to understand the ice-protein interaction. Through the analysis of the sequence motif by MEME, hydrophobic amino acids shown blue are most. According to the hydropathy, acid-base property, the chemical structure of the R group of amino acid and the polarity of the amino acids, the amino acids are respectively divided into 6 groups, 3 groups, 6 groups, 4 groups. In this study, based on the n-Peptide compositions and these physicochemical characteristics, an algorithm of Support Vector Machine (SVM) is proposed for predicting antifreeze proteins. The best results of the jackknife test show that the sensitivity, the specificity, the overall identification accuracy and the Mcc value are 93.14%, 96.08%, 94.62% and 0.8927, respectively. The hydropathy and the chemical structure of the R group of amino acid are important physicochemical characteristics for identifying AFPs.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huan Wen
AU  - Jun-Jie Liu
AU  - Qian-Zhong Li
PY  - 2013/03
DA  - 2013/03
TI  - Motif analysis and identification of antifreeze protein sequences
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 937
EP  - 940
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.236
DO  - 10.2991/iccsee.2013.236
ID  - Wen2013/03
ER  -