Mahmood Muhammad Khalid, Ehsan Asma, Khan Yaser Daanial, Chou Kuo-Chen
1Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA.
Curr Genomics. 2020 Nov;21(7):536-545. doi: 10.2174/1389202921999200831142629.
Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification.
The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called "iHyd-LysSite (EPSV)" (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. technique is an alternative approach to identify hydroxylysine sites in proteins.
The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%.
The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.
羟基化是细胞功能中最重要的翻译后修饰（PTM）之一，与多种疾病相关。赖氨酸位点经化学修饰添加一个羟基（OH）后会产生羟赖氨酸。
本研究中用于识别羟赖氨酸位点的方法基于强大的数学和统计方法，该方法纳入了蛋白质序列中每个对象的序列顺序效应和组成。这个预测器被称为“iHyd-LysSite (EPSV)”（通过提取增强位置和序列变异技术识别羟赖氨酸位点）。通过实验方法预测羟赖氨酸位点既困难、费力又成本高昂。该技术是识别蛋白质中羟赖氨酸位点的一种替代方法。
实验结果要求预测模型应具有高灵敏度和特异性值，且必须更准确。为了验证目的，进行了自一致性、独立、10折交叉验证和留一法测试。这些测试使用三种著名的分类器——神经网络（NN）、随机森林（RF）和支持向量机（SVM），并具有苛刻的预测率。总体预测结果比以前的预测器获得的结果异常优越。所提出的模型在NN、RF和SVM分类器系统中贡献了出色的预测率。使用所有这些分类器进行留一法测试的灵敏度和特异性结果分别为96.08%、94.99%、98.16%和97.52%、98.52%、80.95%。
所提出工具获得的结果表明，该方法可能以比现有方法更好的预测率满足未来对羟赖氨酸位点的需求。
