文章基本信息

标题：Residue Adjacency Matrix Based Feature Engineering for Predicting Cysteine Reactivity in Proteins
本地全文：下载
作者：Norman John Mapes Jr. ; Christopher Rodriguez ; Pradeep Chowriappa 等
期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2019
卷号：17
页码：90-100
DOI：10.1016/j.csbj.2018.12.005
出版社：Computational and Structural Biotechnology Journal
摘要：Free radicals that form from reactive species of nitrogen and oxygen can react dangerously with cellular components and are involved with the pathogenesis of diabetes, cancer, Parkinson's, and heart disease. Cysteine amino acids, due to their reactive nature, are prone to oxidation by these free radicals. Determining which cysteines oxidize within proteins is crucial to our understanding of these chronic diseases. Wet lab techniques, like differential alkylation, to determine which cysteines oxidize are often expensive and time-consuming. We utilize machine learning as a fast and inexpensive approach to identifying cysteines with oxidative capabilities. We created the original features RAMmod and RAMseq for use in classification. We also incorporated well-known features such as PROPKA, SASA, PSS, and PSSM. Our algorithm requires only the protein sequence to operate; however, we do use template matching by MODELLER to acquire 3D coordinates for additional feature extraction. There was a mean improvement of RAM over N6C by 22.04% MCC. It was statistically significant with a p-value of 0.015. RAM provided a significant increase over PSSM with a p-value of 0.040 and an average 70.09% improvement MCC.
关键词：RAM residue adjacency matrix ; Cysteine reactivity ; Oxidative stress ; Response pathways ; Free radicals ; Position specific scoring matrix ; PSSM