An analytical study on the identification of N-linked glycosylation sites using machine learning model.

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment.

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

Prediction of N-linked glycosylation sites using position relative features and statistical moments.

Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen's recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew's correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.