Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.
蛋白酶是水解目标底物蛋白质的两个特定氨基酸残基之间的肽键的酶。由蛋白酶控制的蛋白质特异性水解在蛋白质的降解和循环中起着关键作用，这对于各种生理过程必不可少。因此，解决蛋白酶的底物识别问题，对于准确理解蛋白酶的功能和生理作用，以及治疗靶点识别和药物适用性具有重要意义。因此，基于序列信息和结构信息预测底物裂解的生物信息学方法有着巨大的需求。在本研究中，我们开发了一种新的生物信息学方法Procleave来预测蛋白酶的特异性底物及其裂解位点。这一方法考考虑了序列和三维结构信息，利用LOWESS数据平滑优化方法将已知裂解位点的结构特征用离散值表示，所有结构参数值采用最佳近似值，在此基础上结合了蛋白质序列和氨基酸化学组特征，编码进基于条件随机场（CRF）的计算框架中。大量的基准测试和独立测试实验结果表明，Procleave的预测性能优越，能够正确识别案例研究中的大多数裂解位点。此外，我们应用Procleave对包含17628个蛋白质结构的全人类结构蛋白质组进行蛋白组预测，识别出了一些新的潜在的底物及其对应的裂解位点。Procleave webserver可在http://Procleave.erc.monash.edu/免费访问。





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