Development of Computational Tools and Experimental Verifications for Protein Design

蛋白质设计计算工具的开发和实验验证

• 批准号: 0639962
• 负责人: Costas Maranas
• 金额: $ 37.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-15 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0639962&HistoricalAwards=false
• 关键词: Development; Computational; Tools; Experimental; Verifications

Development of Computational Tools and Experimental Verifications forProtein DesignCostas D MaranasPennsylvania State UniversityCBET-0639962Through the processes of natural selection and co-option, nature has crafted an astounding array of protein designs with a remarkable repertoire of functions ranging from catalysis to signaling and regulation. A growing list of biotechnology needs for proteins with altered cofactor/substrate specificities, improved/modified functionalities or activities are creating an ever expanding compilation of protein design challenges. Recent advances in the ability to create protein libraries with customized statistics have brought to the forefront the question of what type of mutations and/or recombination events are likely to yield functionally enriched protein libraries. This project seeks to address this challenge through the development of customized computational frameworks to guide protein design. Research will be tightly integrated with a set of experimental studies that will enable one to not only fine-tune the proposed methods but also to quantitatively assess the benefit of using computations. Two separate tracks of computational methods and associated experimental studies will be pursued depending on the presence or absence of detailed structural information. Intellectual merit:The intellectual merit of this project lies in the development of a hierarchy of computational methods and experimental tests for a variety of protein design challenges. Successful completion of this research will lead to new tightly integrated paradigms for protein design where hypotheses generated by the modeling/optimization base are used to guide the experiment and experimental results serve to assess and correct the computational frameworks. The size and complexity of the resulting combinatorial optimization problems will necessitate the development of customized solution procedures and the use of parallel computing architectures. The two Principal Investigators (PIs) have already made significant contributions towards computational and experimental challenges and are uniquely positioned to undertake the proposed challenges. Broader impacts: The broader impact of this activity lies primarily in the introduction of undergraduate students to the scientific research process, mentorship and preparation of a research portfolio to aid in their future industrial or academic endeavors. Specifically, this research will be integrated into Penn State's Intercollegiate Genetically Engineered Machines program (IGEM), aimed at providing undergraduate students and high-school students hands-on exposure to synthetic biology. The research results will be broadly disseminated through journal publications and conference presentations, preparation of elective courses on protein design, and by making available, through the web, all developed software programs, databases and experimental protocols to be used by both the academic and industrial communities. The collaborating labs will enable the PIs to amplify the impact of this activity beyond the specifics of the proposed research.

蛋白质设计计算工具的开发和实验验证Costas D Maranas宾夕法尼亚州立大学CBET-0639962通过自然选择和共同选择的过程，大自然精心设计了一系列令人惊叹的蛋白质设计，具有从催化到信号传导和调节等一系列非凡的功能。越来越多的生物技术对具有改变的辅因子/底物特异性、改进/修改的功能或活性的蛋白质的需求正在创造不断扩大的蛋白质设计挑战。利用定制统计数据创建蛋白质库的能力的最新进展使什么类型的突变和/或重组事件可能产生功能丰富的蛋白质库的问题成为人们关注的焦点。该项目旨在通过开发定制计算框架来指导蛋白质设计来应对这一挑战。研究将与一组实验研究紧密结合，使人们不仅能够微调所提出的方法，而且能够定量评估使用计算的好处。根据详细结构信息的存在或不存在，将进行两个独立的计算方法和相关实验研究。智力价值：该项目的智力价值在于开发了一系列计算方法和针对各种蛋白质设计挑战的实验测试。这项研究的成功完成将带来新的紧密集成的蛋白质设计范例，其中建模/优化基础生成的假设用于指导实验，实验结果用于评估和纠正计算框架。由此产生的组合优化问题的规模和复杂性将需要开发定制的解决方案程序并使用并行计算架构。两位首席研究员 (PI) 已经在计算和实验挑战方面做出了重大贡献，并且在应对所提出的挑战方面处于独特的地位。更广泛的影响：这项活动的更广泛影响主要在于向本科生介绍科学研究过程、指导和准备研究组合，以帮助他们未来的工业或学术努力。具体来说，这项研究将被纳入宾夕法尼亚州立大学的校际基因工程机器计划（IGEM），旨在为本科生和高中生提供合成生物学的实践机会。研究结果将通过期刊出版物和会议演讲、蛋白质设计选修课程的准备以及通过网络提供所有开发的软件程序、数据库和实验方案来广泛传播，以供学术界和工业界使用。合作实验室将使 PI 能够扩大这项活动的影响，使其超出拟议研究的具体范围。