School of Mathematics, Shandong University, Jinan, 250100, China; School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541000, China.
The State Key Laboratory of Microbial Technology, Shandong University, Jinan, 250100, China.
Biochem Biophys Res Commun. 2019 Nov 19;519(4):714-720. doi: 10.1016/j.bbrc.2019.09.056. Epub 2019 Sep 20.
Proteases play critical roles in a wide variety of fundamental biological functions, and numerous protease inhibitors have been developed to treat various diseases including cancer. A wide range of experimental and computational methods have been developed to investigate the specificity and catalytic mechanisms of proteases. However, these methods only focused on the preferences of a single position around a cleavage site in a substrate, rarely on the compositionality of the subsites. We present new methods to quantify the specificity of proteases by considering the combinatorial patterns of amino acid residuals of cleavage sites in substrates. By incorporating the preference at positions, we modeled three types of favorable combinations of residues in cleavage sites. Moreover, by constructing a relationship weight matrix of residues between two positions, we can easily identify unfavorable combinations of residues at the positions. Applying these methods to a set of known cleavage sites of proteases, we revealed numerous favorable and unfavorable residues in cooperative positions in the protease cleavage sites. The results can help understand the specificity and catalytic mechanisms of proteases. To our knowledge, this is the first study that quantifies unfavorable combinations of amino acids between two sites. Furthermore, this method is not limited to the study of proteases and cleavage sites, and can be generalized to uncover the relationships of residues at meaningful sites in other proteins.
蛋白酶在广泛的基本生物学功能中发挥着关键作用，已经开发出许多蛋白酶抑制剂来治疗各种疾病，包括癌症。已经开发出广泛的实验和计算方法来研究蛋白酶的特异性和催化机制。然而，这些方法仅关注于底物切割位点周围单个位置的偏好，很少关注亚位点的组成性。我们提出了新的方法来通过考虑底物切割位点中氨基酸残基的组合模式来量化蛋白酶的特异性。通过整合位置的偏好，我们在切割位点中建模了三种有利的残基组合类型。此外，通过构建两个位置之间的残基关系权重矩阵，我们可以轻松识别位置处残基的不利组合。将这些方法应用于一组已知的蛋白酶切割位点，我们揭示了蛋白酶切割位点中协同位置处大量有利和不利的残基。这些结果有助于理解蛋白酶的特异性和催化机制。据我们所知，这是首次定量研究两个位置之间氨基酸的不利组合的研究。此外，该方法不仅限于蛋白酶和切割位点的研究，还可以推广到揭示其他蛋白质中有意义的位点处残基的关系。
