Computational and experimental tool for cancer protein

描述（由申请人提供）： 在占主导地位的序列功能范式中，3-D结构是蛋白质功能的必要先决条件。尽管在文献中可以找到100多个反例，但与非折叠（无序）蛋白质相关的功能的推广大多被忽略了。我们专有的生物信息学软件，PONDR，一套全面的癌基因的应用程序显示，这些蛋白质很可能包含显着更多的障碍比其他蛋白质类型。这一结果，结合迹象表明，障碍是至关重要的许多蛋白质的功能，要求在人类疾病的研究中明确考虑的障碍。因此，癌症特异性数据挖掘将产生“癌症DisProt”，其中包含与蛋白质功能的无序/有序相关性。 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，并与局部相互作用网络增强，为癌症相关研究提供交互式资源工具。一个同伴的方法手册将促进整合有序/无序知识到新的实验。 Cancer DisProt将是一个有用的生物信息学工具，用于整个基因组的功能注释。当增加了研究反例蛋白质的方法时，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.