Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Bioinformatics. 2010 Mar 15;26(6):752-60. doi: 10.1093/bioinformatics/btq043. Epub 2010 Feb 3.
The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates.
In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship.
http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/
jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au
Supplementary data are available at Bioinformatics online.
半胱氨酸蛋白酶家族的胱天蛋白酶在细胞程序性死亡、分化、增殖、坏死和炎症等关键生物过程中发挥着重要作用。半胱天冬酶底物的完整库有待全面表征。因此，对半胱天冬酶底物切割位点的系统计算筛选研究可能有助于深入了解半胱天冬酶的底物特异性，并进一步促进潜在新底物的发现。
在本文中，我们开发了一种预测经典（即遵循 P(1)Asp）和非典型半胱天冬酶切割位点的方法（称为 Cascleave）。当使用局部序列衍生的图谱时，Cascleave 成功预测了 82.2%的已知底物切割位点，马修斯相关系数（MCC）为 0.667。我们发现，通过纳入预测溶剂可及性以及切割序列是否位于最有可能天然无结构的区域等信息，可以进一步提高预测性能。发现新型双谱贝叶斯特征显著提高了预测性能，总体准确率为 87.6%，MCC 为 0.747，这比主要依赖氨基酸序列的已发表方法具有更高的准确性。预计 Cascleave 将成为预测半胱天冬酶新底物切割位点的有力工具，并为未知的半胱天冬酶-底物相互作用关系提供新的见解。
http://sunflower.kuicr.kyoto-u.ac.jp/approximately sjn/Cascleave/
jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au
补充数据可在 Bioinformatics 在线获取。
