Computational methods for structural-functional studies of protein

蛋白质结构功能研究的计算方法

• 批准号: 8761661
• 负责人: Nick V. Grishin
• 金额: $ 28.83万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761661
• 关键词: Achievement; Affect; Algorithms; Amino Acid Sequence; Amino Acids; Atlases; Bioinformatics; Biological; Blast Cell; Characteristics; Clinical; Collaborations; Communities; Computational Biology; Computer Analysis; Computing Methodologies; DNA Sequence; Data; Databases; Detection; Development; Disease; Drug Design; Educational process of instructing; Effectiveness; Experimental Designs; Funding; Future; Genetic Polymorphism; Grant; Homologous Gene; Homology Modeling; Human; Imagery; Interdisciplinary Study; Internet; Intuition; Knowledge; Libraries; Literature; Manuals; Maps; Methods; Modeling; Molecular; Mutation; Online Mendelian Inheritance In Man; Pattern; Peptide Sequence Determination; Phenotype; Phylogenetic Analysis; Positioning Attribute; Property; Protein Analysis; Protein Family; Protein Sequence Analysis; Proteins; PubMed; Published Comment; Recording of previous events; Research; Resources; Rest; Scoring Method; Sequence Alignment; Sequence Analysis; Sequence Homologs; Site; Solutions; Statistical Algorithm; Structure; Techniques; Thinking; Update; Work; base; disease phenotype; experience; improved; insight; method development; programs; protein function; public health relevance; research study; sound; statistics; three dimensional structure; three-dimensional modeling; tool; web interface; web page; Achievement; Affect; Algorithms; Amino Acid Sequence; Amino Acids; Atlases; Bioinformatics; Biological; Blast Cell; Characteristics; Clinical; Collaborations; Communities; Computational Biology; Computer Analysis; Computing Methodologies; DNA Sequence; Data; Databases; Detection; Development; Disease; Drug Design; Educational process of instructing; Effectiveness; Experimental Designs; Funding; Future; Genetic Polymorphism; Grant; Homologous Gene; Homology Modeling; Human; Imagery; Interdisciplinary Study; Internet; Intuition; Knowledge; Libraries; Literature; Manuals; Maps; Methods; Modeling; Molecular; Mutation; Online Mendelian Inheritance In Man; Pattern; Peptide Sequence Determination; Phenotype; Phylogenetic Analysis; Positioning Attribute; Property; Protein Analysis; Protein Family; Protein Sequence Analysis; Proteins; PubMed; Published Comment; Recording of previous events; Research; Resources; Rest; Scoring Method; Sequence Alignment; Sequence Analysis; Sequence Homologs; Site; Solutions; Statistical Algorithm; Structure; Techniques; Thinking; Update; Work; base; disease phenotype; experience; improved; insight; method development; programs; protein function; public health relevance; research study; sound; statistics; three dimensional structure; three-dimensional modeling; tool; web interface; web page

DESCRIPTION (provided by applicant): Computational analysis of proteins is an essential shortcut to random experimentation. Multiple sequence alignments (MSAs) reveal evolutionary history of a protein family, govern predictions of 3D structures and functions and guide experimental design. Accuracy of these alignments is critical for the accuracy of conclusions from their analysis. With the finding from the previous round of the grant, we significantly advanced the power of sequence similarity search and improved the accuracy of MSA. Using these techniques, we aided biological discoveries in dozens of collaborations with experimentalists, analyzed medically important protein families and implemented a number of public web-servers. For the next funding period we propose to: 1) Build on our advances to perfect homology search and multiple sequence alignment. Sequence profile search will be improved by more sound statistics and by averaging scores over predicted homologs of found hits. Sequence alignment will be corrected in regions that interact less closely with the rest of the protein and segments that require large adjustments. 2) Maintain, improve and integrate our protein sequence analysis servers. During the first funding period of the grant, in addition to improving our sequence search and alignment web-servers, we developed three new servers for predicting a number of characteristics for a protein sequence, finding literature about a protein and visualizing relationships between proteins as networks, and compiled a searchable database of clinical mutations. We will integrate these servers into a single sequence analysis "stop", augmented with other information, such as expression patterns, protein interactions, human polymorphism and known diseases. 3) Develop an Atlas of clinical mutations in proteins, freely available for browsing and download without login requirements. Each out of 25,000 known mutations will have a dedicated web-page with mutation's characteristics and predictions about its negative effects.

描述（由申请人提供）：蛋白质的计算分析是随机实验的必不可少的快捷方式。多个序列比对（MSA）揭示了蛋白质家族的进化历史，控制了3D结构和功能的预测以及指导实验设计。这些比对的准确性对于从其分析中得出结论的准确性至关重要。随着上一轮赠款的发现，我们显着提高了序列相似性搜索的力量，并提高了MSA的准确性。使用这些技术，我们在与实验者的数十次合作中协助了生物学发现，分析了医学上重要的蛋白质家族并实施了许多公共网络服务器。在下一个资金期间，我们建议：1）基于我们的进步，以完美的同源性搜索和多个序列对齐。序列概况搜索将通过更多的声音统计数据和平均得分来改善发现的命中的预测同源物。在与其他需要大量调整的蛋白质和片段相互作用的区域中，序列比对将得到校正。 2）维护，改善和整合我们的蛋白质序列分析服务器。在赠款的第一个资金期间，除了改善我们的序列搜索和对齐网络服务器外，我们开发了三个新的服务器，用于预测蛋白质序列的许多特征，找到有关蛋白质的文献，并可视化蛋白质作为网络之间的关系，并编译了临床突变的搜索数据库。我们将将这些服务器整合到单个序列分析“停止”中，并与其他信息相加，例如表达模式，蛋白质相互作用，人类多态性和已知疾病。 3）在蛋白质中开发一个临床突变的地图集，可自由浏览并下载而无需登录要求。 25,000个已知突变中的每一个都将具有专用的网页页面，并具有有关其负面影响的突变特征和预测。