Abstract Protein phosphorylation is an emerging post-translational modification, which critically involved in the intracellular process of the human body by controlling diverse functions ranging from cell growth to metabolism. The existing experimental methods for identifying phosphorylated proteins are overpriced and resource-intensive; thus, it is necessary to develop a fast and accurate computational method to address the problem. Here we report a novel predictor HPhosPPred, a phosphorylated protein prediction method that is incorporating highly discriminative evolutionary and physicochemical information conserved in protein primary motifs, namely pseudo-position specific scoring matrix, the auto-covariance transformation of the position-specific scoring matrix and normalized moreau-broto auto-correlation. Further, to boost up the generalization capability of HPhosPPred, we used local fisher discriminant analysis as a dominant feature selection strategy for eliminating redundant and noise patterns from the extracted features. Finally, the optimized features feed to support vector machine with radial basis function kernel to predict phosphorylated proteins. As evident from the results, the proposed method achieved promising performance with an accuracy of 80.68%, sensitivity of 84.63%, specificity of 73.67%, and Matthew’s correlation coefficient of 0.581 using rigorous leave-one-out-cross-validation test and 10-fold cross-validation test. The empirical outcomes demonstrate that the developed model outperformed the existing state-of-the-art methods. Furthermore, our analysis reveals that the proposed tool can help detect unseen phosphorylated proteins in particular and proteomics research in general. The source code and dataset are publicly available at https://github.com/saeed344/HPhosPPred .

中文翻译：

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通过 LFDA 从进化谱和理化特性中提取多视角判别特征来预测人类磷酸化蛋白

摘要 蛋白质磷酸化是一种新兴的翻译后修饰，通过控制从细胞生长到代谢的多种功能，在人体细胞内过程中发挥重要作用。现有的鉴定磷酸化蛋白的实验方法价格过高且资源密集；因此，有必要开发一种快速准确的计算方法来解决这个问题。在这里，我们报告了一种新的预测因子 HPhosPPred，这是一种磷酸化蛋白质预测方法，它结合了蛋白质主要基序中保守的高度区分进化和物理化学信息，即假位置特定评分矩阵、位置特定评分矩阵的自协方差变换和归一化moreau-broto 自相关。更多，为了提高 HPhosPPred 的泛化能力，我们使用局部 Fisher 判别分析作为主要特征选择策略，从提取的特征中消除冗余和噪声模式。最后，优化的特征输入到具有径向基函数核的支持向量机以预测磷酸化蛋白质。从结果中可以明显看出，所提出的方法使用严格的留一法交叉验证测试和 10- 交叉验证测试获得了有希望的性能，准确度为 80.68%，灵敏度为 84.63%，特异性为 73.67%，马修相关系数为 0.581。折叠交叉验证测试。实证结果表明，开发的模型优于现有的最先进方法。此外，我们的分析表明，所提出的工具可以帮助检测特别是看不见的磷酸化蛋白质和一般的蛋白质组学研究。源代码和数据集可在 https://github.com/saeed344/HPhosPPred 公开获得。