BACKGROUND The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. RESULTS We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. CONCLUSIONS The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.

中文翻译：

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Bigram-PGK：使用位置特定评分矩阵的bigram概率技术进行磷酸甘油酯化预测。

背景技术称为翻译后修饰（PTM）的生物学过程是修饰蛋白质组的条件，其影响正常细胞生物学，并因此影响发病机理。近年来已经发现了许多PTM，赖氨酸磷酸甘油化是相当新的发展之一。即使在后基因组时代对大量蛋白质进行了测序，由于诸如成本，时间消耗和实验工作效率低下等因素，磷酸甘油基化的鉴定仍然是一个巨大的挑战。为了克服这个问题，已经出现了计算技术以准确地识别磷酸甘油基化的赖氨酸残基。但是，到目前为止提出的计算技术都存在局限性，无法正确预测这种共价修饰。结果我们在本文中提出了一种新的预测因子，称为Bigram-PGK，它利用氨基酸的进化信息来尝试预测磷酸甘油基化位点。为此，使用了包含实验标记位点的基准数据集，并根据蛋白质序列中氨基酸的位置特定评分矩阵计算了两字组的分布。据报道，这项工作的统计指标分别为0.9642、0.8973、0.8253、0.9193、0.8330和0.9306，例如敏感性，特异性，精密度，准确性，Mathews相关系数和ROC曲线下面积。结论基于进化信息和支持向量机分类器的特征，提出了一种预测器，与现有的预测因子相比，已显示出有效预测磷​​酸甘油基化和非磷酸甘油基化赖氨酸残基的巨大潜力。这项工作的数据和软件可以从https://github.com/abelavit/Bigram-PGK获得。