Collaborative Research: ABI Development: Algorithms and Software for Discovery of Non-sequential Protein Structure Similarities

合作研究：ABI 开发：用于发现非序列蛋白质结构相似性的算法和软件

• 批准号: 1062371
• 负责人: Mona Singh
• 金额: $ 20万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062371&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; Algorithms

The University of Illinois at Chicago and Princeton University are awarded collaborative grants to develop efficient and scalable computational methods for comparing protein structures. Protein sequences and structures are being determined at an increasingly rapid rate. To date, there are more than 1000 sequenced genomes with several thousand more in progress and over 50,000 three-dimensional structures in the Protein Data Bank. Whether considering proteins at the level of sequence or structure, comparing or aligning two proteins is the fundamental technique for uncovering principles of protein structure, function and evolution. While the vast majority of research efforts have focused on sequence comparisons, since protein structures are generally better conserved than protein sequences, identifying structural similarity between proteins can yield valuable clues to protein function and can be used to classify proteins, analyze their evolutionary histories and even to help predict protein interactions. Though considerable advances have been made in recent years in comparing protein structures, key difficulties include detecting shared, conserved structures between proteins where the individual structural elements are in different orderings on the two sequences. This project will develop innovative methods to enable discovery of sequence-order-independent substructure similarity with a long-term goal of doing a large-scale comparison over all protein structures. The research team will formulate precise theoretical problems, design efficient algorithms for them and implement and test the resulting algorithms to test accuracy and efficiency issues. The final software for comparing protein structures will be released to the scientific community and is expected to provide a significant and demonstrable impact on further research in structural bioinformatics.Scientifically, the methodologies to be developed for substructure comparison are general and will have broader impacts beyond structural proteomics and bioinformatics. For example, a biomedical application of the proposed project lies in guiding protein engineering and rational drug design via a systematic identification of all such substructures and their underlying sequences. The project will involve undergraduates and under-represented minority (URM) groups in active research. A central component is to engage URM undergraduate students from the urban UIC campus and involve them in summer research at Princeton with the goal of possible recruitment into Princeton's graduate program in Quantitative and Computational Biology. Additionally, the PIs are planning course and curriculum development, dissemination of research, mentoring of undergraduate and graduate students, outreach and community involvement.The outcomes of the project will be made available through the websites of all the investigators: http://www.cs.uic.edu/~dasgupta http://gila.bioengr.uic.edu/lab http://www.cs.princeton.edu/~mona

位于芝加哥的伊利诺伊大学和普林斯顿大学获得了合作赠款，以开发用于比较蛋白质结构的高效和可扩展的计算方法。 蛋白质序列和结构的测定速度越来越快。迄今为止，蛋白质数据库中已有1000多个已测序的基因组，还有数千个正在进行中，并有超过50，000个三维结构。无论是在序列还是结构水平上考虑蛋白质，比较或比对两种蛋白质是揭示蛋白质结构，功能和进化原理的基本技术。 虽然绝大多数研究工作都集中在序列比较上，但由于蛋白质结构通常比蛋白质序列更保守，因此识别蛋白质之间的结构相似性可以为蛋白质功能提供有价值的线索，并可用于分类蛋白质，分析其进化历史，甚至帮助预测蛋白质相互作用。虽然近年来在比较蛋白质结构方面取得了相当大的进展，但关键的困难包括检测蛋白质之间共享的保守结构，其中单个结构元件在两个序列上处于不同的顺序。该项目将开发创新方法，以发现序列顺序无关的子结构相似性，长期目标是对所有蛋白质结构进行大规模比较。研究团队将制定精确的理论问题，为它们设计高效的算法，并实施和测试由此产生的算法，以测试准确性和效率问题。用于比较蛋白质结构的最终软件将向科学界发布，预计将对结构生物信息学的进一步研究产生重大和明显的影响。从科学角度来看，为子结构比较开发的方法是通用的，将产生结构蛋白质组学和生物信息学以外的更广泛影响。例如，拟议项目的生物医学应用在于通过系统识别所有此类子结构及其潜在序列来指导蛋白质工程和合理的药物设计。该项目将涉及大学生和代表性不足的少数民族（URM）群体的积极研究。 一个核心组成部分是让URM的本科生从城市UIC校园，并让他们在普林斯顿大学的夏季研究与可能的招聘到普林斯顿大学的定量和计算生物学研究生课程的目标。 此外，项目负责人正在规划课程和课程编制、传播研究成果、指导本科生和研究生、外联和社区参与。项目成果将通过所有研究人员的网站公布： http://www.cs.uic.edu/~dasgupta http://gila.bioengr.uic.edu/lab http://www.cs.princeton.edu/~mona