iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features |
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Authors: | Md Mofijul Islam Sanjay Saha Md Mahmudur Rahman Swakkhar Shatabda Dewan Md Farid Abdollah Dehzangi |
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Affiliation: | 1. Department of CSEUniversity of Dhaka, Dhaka, Bangladesh;2. Department of CSEUnited International University, Dhaka, Bangladesh;3. Department of Computer Science, Morgan State University, Baltimore, MD, USA |
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Abstract: | 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/ . |
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Keywords: | classification evolutionary features feature selection protein glycation structural features |
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