Building motif lexicons

描述（由申请人提供）：随着众多基因组的完整测序和蛋白质组的注释，生物学的下一个主要挑战之一是了解编码蛋白质的功能和整合（NIH路线图重点领域）。破译蛋白质的功能是一个非常耗时和昂贵的过程，因为具有确定功能的蛋白质比例低得不成比例。将已建立的功能外推到新蛋白质上的一种方法是预测短基序。短基序针对蛋白质进行翻译后修饰，运输到细胞室，并与其他蛋白质或分子结合。我们的跨学科团队已经建立了Minimotif Miner (MnM)，这是一个简短的motif数据库和平台独立的网络工具，可以识别蛋白质查询中的motif共识序列，从而识别潜在的新蛋白质功能（http://mnm.engr.uconn.edu/）。MnM还可用于提出关于特定突变如何引起人类疾病的新假设，并为开发治疗药物、抗生素、杀虫剂和抗病毒药物确定假定的靶点。尽管使用了MnM和其他基序资源，但功能基序的预测仍然存在两个主要限制，我们在本建议中解决了这两个问题。1)为了减少基序的假阳性预测，我们创造了一种新的语言，允许我们考虑基序的三维结构守恒。对于每个motif，我们将通过结合实验数据和蛋白质数据库中motif结构的数据来构建特定的motif定义。我们也将确定序列排列，可以形成观察基序结构，通过使用分子动力学模拟。2)为了构建更全面的motif数据库，我们将使用人工智能对PubMed进行挖掘。专家系统将使用自动文献筛选、文档摘要和motif识别效率评分从PubMed中提取大部分已知motif。解决这些限制将大大提高短基序预测的效用。

DESCRIPTION (provided by applicant): With the complete sequencing of numerous genomes and the annotation of proteomes, one of the next major challenges in biology is to understand the functions and integration of the encoded proteins (a NIH Roadmap area of emphasis). Deciphering protein function is a very time consuming, expensive process, as reflected by the disproportionately low percentage of proteins with well-established functions. One approach for extrapolating established functions to new proteins is to predict short motifs. Short motifs target proteins for post-translational modification, trafficking to cellular compartments, and binding to other proteins or molecules. Our cross-disciplinary team has built Minimotif Miner (MnM), a short motif database and platform- independent web-tool that identifies motif consensus sequences in protein queries and thus potential new protein functions (http://mnm.engr.uconn.edu/). MnM can also be used to develop new hypotheses of how specific mutations cause human disease and to identify putative targets for the development of therapeutic drugs, antibiotics, insecticides, and antiviral agents. Despite the utility of MnM and other motif resources, prediction of functional motifs still has two major limitations, which we address in this proposal. 1) To reduce the false-positive prediction of motifs, we have created a new language that allows us to consider the 3- dimensional structural conservation of motifs. For each motif, we will build specific motif definitions by combining experimental data with data from motif structures in the Protein Data Bank. We will also determine the sequence permutations that can form the observed motif structure by using molecular dynamic simulations. 2) To build a more comprehensive motif database we will use artificial intelligence to mine PubMed. The expert system will use automated literature screening, document summarization, and motif identification efficiency score to extract the majority of known motifs from PubMed. Addressing these limitations will vastly increase the utility of short motif prediction.