Prediction and Analysis of Protein Hydroxyproline and Hydroxylysine

Abstract

BackgroundHydroxylation is an important post-translational modification and closely related to various diseases. Besides the biotechnology experiments, in silico prediction methods are alternative ways to identify the potential hydroxylation sites.Methodology/Principal FindingsIn this study, we developed a novel sequence-based method for identifying the two main types of hydroxylation sites – hydroxyproline and hydroxylysine. First, feature selection was made on three kinds of features consisting of amino acid indices (AAindex) which includes various physicochemical properties and biochemical properties of amino acids, Position-Specific Scoring Matrices (PSSM) which represent evolution information of amino acids and structural disorder of amino acids in the sliding window with length of 13 amino acids, then the prediction model were built using incremental feature selection method. As a result, the prediction accuracies are 76.0% and 82.1%, evaluated by jackknife cross-validation on the hydroxyproline dataset and hydroxylysine dataset, respectively. Feature analysis suggested that physicochemical properties and biochemical properties and evolution information of amino acids contribute much to the identification of the protein hydroxylation sites, while structural disorder had little relation to protein hydroxylation. It was also found that the amino acid adjacent to the hydroxylation site tends to exert more influence than other sites on hydroxylation determination.Conclusions/SignificanceThese findings may provide useful insights for exploiting the mechanisms of hydroxylation.

MPTM: A tool for mining protein post-translational modifications from literature

“…5 Moreover, hydroxylation is an important PTM and is closely related to various diseases. 6 A better understanding of the role of hydroxylation can help inform the development of drug for diseases. 7 At the same time, the latest study shows that myristoylation plays an important role in human immune response 8 and abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell.…”

“…34 MPTM has the following advantages: (1) it covers 11 common PTMs (phosphorylation, methylation, glycosylation, acetylation, amidation, hydroxylation, myristoylation, sulfation, GPI-anchor, disul¯de and ubiquitination). MPTM is the¯rst web server that provides literature mining service for hydroxylation, myristoylation and GPI-anchor due to their important biological signi¯cance [6][7][8][9][10][11] ; (2) by using an e±cient relation extraction method based on dependency parse trees 35 and a heuristic algorithm, it has good performance in both substrate and modi¯cation site detection; (3) it extracts not only basic PTM information including substrates and modi¯cation sites, but also comprehensive PTM-related information such as enzymes, Gene Ontology (GO) terms, organisms, diseases and crosstalk from literature. The web server is also accompanied by a precompiled collection of PTM information named MPTMDB with 435,433 PTM records, which enables versatile visualization of PTMs, substrates and interacting enzymes.…”

Prediction of ketoacyl synthase family using reduced amino acid alphabets

“…Among these three methods, the jackknife test is deemed as the most objective and rigorous [12] and can always yield a unique outcome, as demonstrated by a penetrating analysis in a recent comprehensive review [11]; it has been widely and increasingly adopted [20,28,30,32,35,36,43]. Accordingly, the jackknife test was used to examine the performance of the model proposed in this study.…”

RF-Hydroxysite: a random forest based predictor for hydroxylation sites

“…Recently, three studies focused on the development of computational methods for hydroxylation site prediction have been reported. In the first study, Hu et al combined support vector machines (SVMs) with position-specific scoring matrices (PSSM) and physicochemical properties to predict sites of hydroxylation in sliding windows of size of 9 [3]. The accuracies achieved using this approach were 76% and 82.1% for HyP and HyK, respectively.…”