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iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features
Author(s) -
Islam Md Mofijul,
Saha Sanjay,
Rahman Md Mahmudur,
Shatabda Swakkhar,
Farid Dewan Md,
Dehzangi Abdollah
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25511
Subject(s) - glycation , support vector machine , classifier (uml) , computational biology , lysine , pseudo amino acid composition , artificial intelligence , computer science , feature selection , biochemistry , protein secondary structure , chemistry , peptide sequence , benchmark (surveying) , amino acid , pattern recognition (psychology) , biology , receptor , geodesy , dipeptide , gene , geography
Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly-SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly-SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.