Improve predictions of structure and function by PredictProtein

描述（由申请人提供）： 总结：美国超过25，000名研究人员和其他120个国家的50，000多名研究人员利用PredictProtein（PP）互联网服务器通过同源转移和蛋白质结构和功能的从头预测来分析蛋白质。在这里，我们提出了技术和科学的解决方案，将提高PP及其扩展门户META-PP的功能。许多技术更改对用户来说仍然是隐藏的，并且需要增加这些服务器的可维护性、可伸缩性和可移植性。新的图形用户界面是一个建议的解决方案，将明显影响服务。科学解决方案解决了与结构和功能预测有关的两个相关任务。第一是预测突变的影响。我们建议开发新的基于机器学习的方法来区分影响结构、功能或没有明显表型的突变。我们的最终方法将被应用于我们在哥伦比亚的实验同事的SNP数据的筛选，以及在公共数据库中SNP效应的预测。第二个主要任务是识别天然非结构化区域及其功能分类。孤立地不采用规则结构的蛋白质正日益成为一个重要的研究领域;它们可能提供从原核生物到真核生物的复杂性进化的关键。我们建议开发一种基于机器学习的识别这类重要分子的特征的方法。我们还计划通过预测蛋白质内部的相互作用密度，从一个非常不同的角度来解决这个问题。由此产生的新工具将允许对这些分子的作用进行蛋白质组范围的分析。所有方法都将通过PP提供。 相关性：关于蛋白质结构的信息为蛋白质分析和基因组注释增加了一个完整的维度。这种添加通常对于推断功能是必不可少的，即使对于天然非结构化蛋白质也是如此。PredictProtein服务器在进化、结构和功能的结合和开发方面是独一无二的;成千上万的理论、实验和临床研究从中受益。本文提出的研究的长期目标是提高我们使用氨基酸取代的进化记录的能力，即最终理解氨基酸“语言”。短期目标是解决与人类疾病密切相关的两项任务，即区分沉默突变和重要突变，以及将非结构化蛋白质映射到网络和疾病上。

DESCRIPTION (provided by applicant): SUMMARY: Over 25,000 researchers in the US and over 50,000 in 120 other countries have exploited the PredictProtein (PP) Internet server to analyze proteins by homology-transfer and by eye novo predictions of protein structure and function. Here, we propose technical and scientific solutions that will improve the functionality of PP and its extension portal META-PP. Many technical changes will remain hidden to users and are required to increase the maintainability, scalability, and portability of these servers. New Graphical User Interfaces are one proposed solution that will visibly impact the service. The scientific solutions address two related tasks pertaining to the prediction of structure and function. The first is to predict the effect of mutations. We propose the development of novel machine learning-based methods to distinguish between mutations that affect structure, function, or have no apparent phenotype. Our final method will be applied to the screening of SNP data from our experimental colleagues at Columbia, as well as to the prediction of SNP effects in public databases. The second major task is the identification of natively unstructured regions and their functional classification. Proteins that do not adopt regular structures in isolation are increasingly becoming an important research area; they may provide a key to the evolution of complexity from prokaryotes to eukaryotes. We propose the development of a machine learning-based identification of features specific to this important class of molecules. We also plan to attack the problem from a very different angle by using predictions of interaction densities inside proteins. The resulting novel tools will allow a proteome-wide analysis of the role of these molecules. All methods will be made available through PP. RELEVANCE: Information about protein structure adds an entire dimension to protein analysis and genome annotation. This addition is often essential to infer function even for natively unstructured proteins. The PredictProtein server is unique in its combination and exploitation of evolution, structure, and function; many thousands of theoretical, experimental, and clinical researches have benefited from this. The long-term goal of the research proposed here is to improve our ability to use the evolutionary record of amino acid substitutions, i.e. to ultimately understand the amino acid "language". The short-term goal is to address two tasks that are closely related to human diseases, namely the distinction between silent and important mutations and the mapping of unstructured proteins onto networks and diseases.