Computational and experimental tool for cancer protein

描述(由申请人提供)： 在主要的序列到功能范式中，三维结构是蛋白质功能的必备前提。尽管在文献中可以找到100多个反例，但对与非折叠(无序)蛋白质相关的功能的概括大多被忽略了。将我们专有的生物信息学软件PONDR应用于一组全面的癌基因，发现这些蛋白质可能比其他类型的蛋白质含有更多的无序。这一结果，再加上紊乱对许多蛋白质功能至关重要的迹象，要求在研究人类疾病时明确考虑紊乱。因此，针对癌症的数据挖掘将产生“癌症DisProt”，它包含无序/有序与蛋白质功能的相关性，被提出。 无序/有序预测和现有的结构知识将与癌症相关蛋白的功能相关联，并与局部相互作用网络相结合，为癌症相关研究提供互动资源工具。配套的方法手册将有助于将有序/无序知识整合到新的实验中。 癌症DisProt将成为一种有用的生物信息学工具，用于对整个基因组进行功能注释。当用研究反例蛋白质的方法加以补充时，它将为设计开发癌症治疗或药物发现的新方法提供基础。

DESCRIPTION (provided by applicant): In the predominant sequence-to-function paradigm, 3-D structure is an obligatory prerequisite for protein function. Even though over 100 counterexamples can be found in literature, generalization of the functions associated with nonfolded (disordered) protein has been mostly ignored. Application of our proprietary bioinformatics software, PONDR, to a comprehensive set of oncogenes revealed that these proteins are likely to contain significantly more disorder than other protein types. This result, combined with indications that disorder is crucial to many protein functions, demand that disorder be considered explicitly in the study of human disease. Thus, cancer-specific data mining will produce "Cancer DisProt," containing correlations of disorder/order with protein function, is proposed. Disorder/order predictions and existing structural knowledge will be correlated with functions of cancer-associated proteins and augmented with local interaction networks to provide an interactive resource tool for cancer-related research. A companion methods manual will facilitate the integration of order/disorder knowledge into novel experiments. Cancer DisProt will be a useful bioinformatics tool for functional annotation of entire genomes. When augmented with methods for studying counter-example proteins, it will provide the basis for design of novel approaches to development of cancer treatments or drug discovery.