Bioinformatics-based prediction of protease substrates can help to elucidate regulatory proteolytic pathways that control a broad range of biological processes such as apoptosis and blood coagulation. The majority of published predictive models are position weight matrices (PWM) reflecting specificity of proteases toward target sequence. These models are typically derived from experimental data on positions of hydrolyzed peptide bonds and show a reasonable predictive power. New emerging techniques that not only register the cleavage position but also measure catalytic efficiency of proteolysis are expected to improve the quality of predictions or at least substantially reduce the number of tested substrates required for confident predictions. The main goal of this study was to develop new prediction models based on such data and to estimate the performance of the constructed models. We used data on catalytic efficiency of proteolysis measured for eight major human matrix metalloproteinases to construct predictive models of protease specificity using a variety of regression analysis techniques. The obtained results suggest that efficiency-based (quantitative) models show a comparable performance with conventional PWM-based algorithms, while less training data are required. The derived list of candidate cleavage sites in human secreted proteins may serve as a starting point for experimental analysis.

中文翻译：

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基于定量蛋白酶活性分析数据的蛋白酶特异性预测模型。

基于生物信息学的蛋白酶底物预测可以帮助阐明调节蛋白水解途径，该途径控制着广泛的生物过程，例如细胞凋亡和凝血。大多数已发布的预测模型是位置权重矩阵（PWM），反映了蛋白酶对靶序列的特异性。这些模型通常来自水解肽键位置的实验数据，并显示出合理的预测能力。预期不仅记录切割位置而且测量蛋白水解的催化效率的新出现的技术将改善预测的质量，或至少基本上减少信心确定的预测所需的受试底物的数量。这项研究的主要目的是基于这些数据开发新的预测模型，并估计所构建模型的性能。我们使用关于八种主要人类基质金属蛋白酶的蛋白水解催化效率的数据，使用多种回归分析技术来构建蛋白酶特异性的预测模型。获得的结果表明，基于效率的（定量）模型显示出与常规基于PWM的算法可比的性能，同时所需的训练数据更少。人分泌蛋白中候选切割位点的衍生列表可作为实验分析的起点。我们使用关于八种主要人类基质金属蛋白酶的蛋白水解催化效率的数据，使用多种回归分析技术构建了蛋白酶特异性的预测模型。获得的结果表明，基于效率的（定量）模型显示出与常规基于PWM的算法可比的性能，同时所需的训练数据更少。人分泌蛋白中候选切割位点的衍生列表可作为实验分析的起点。我们使用关于八种主要人类基质金属蛋白酶的蛋白水解催化效率的数据，使用多种回归分析技术构建了蛋白酶特异性的预测模型。获得的结果表明，基于效率的（定量）模型显示出与常规基于PWM的算法可比的性能，同时所需的训练数据更少。人分泌蛋白中候选切割位点的衍生列表可作为实验分析的起点。