Development of Computational Tools and Experimental Verifications for Protein Design

通过自然选择和协同选择的过程，自然界精心设计了一系列令人震惊的蛋白质设计，这些蛋白质设计具有从催化到信号传导和调节的各种功能。对具有改变的辅因子/底物特异性、改进的/修饰的功能或活性的蛋白质的生物技术需求的不断增长的列表正在创建蛋白质设计挑战的不断扩大的汇编。最近的进展，在创建具有定制的统计数据的蛋白质文库的能力带来了最前沿的问题，什么类型的突变和/或重组事件可能产生功能富集的蛋白质文库。该项目旨在通过开发定制的计算框架来指导蛋白质设计来应对这一挑战。研究将与一系列实验研究紧密结合，这些研究将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。两个独立的轨道的计算方法和相关的实验研究将根据详细的结构信息的存在或不存在而进行。智力价值：该项目的智力价值在于为各种蛋白质设计挑战开发了一系列计算方法和实验测试。这项研究的成功完成将导致新的紧密集成的蛋白质设计范例，其中由建模/优化基础产生的假设用于指导实验，实验结果用于评估和校正计算框架。由此产生的组合优化问题的大小和复杂性将需要定制的解决方案的程序和使用并行计算架构的发展。两位首席研究员（PI）已经对计算和实验挑战做出了重大贡献，并且具有独特的优势来应对拟议的挑战。更广泛的影响：这项活动的更广泛的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，这项研究将被整合到宾夕法尼亚州立大学的校际遗传工程机器计划（IGEM）中，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报告，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，数据库和实验协议，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，超越拟议研究的具体内容。

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.