Development of Computational Tools and Experimental Verifications for Protein Design

为蛋白质设计开发计算工具和实验验证宾夕法尼亚州立大学Costas D Maranas CBET-0639962通过自然选择和共同选择的过程，大自然精心制作了一系列令人惊叹的蛋白质设计，具有从催化到信号和调节的一系列非凡的功能。对辅因子/底物特性改变、改进/修改的功能或活动的蛋白质的生物技术需求越来越多，这产生了越来越多的蛋白质设计挑战汇编。在创建具有定制统计数据的蛋白质文库的能力方面的最新进展已经将什么类型的突变和/或重组事件可能产生功能丰富的蛋白质文库的问题提上了议事日程。该项目寻求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，这将使人们不仅能够微调拟议的方法，而且还能够定量评估使用计算的好处。将根据是否有详细的结构信息，对计算方法和相关的实验研究进行两个不同的轨道。智力价值：这个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为蛋白质设计带来新的紧密集成的范式，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修正计算框架。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程序和使用并行计算体系结构。两名首席调查员已经为应对计算和实验挑战作出了重大贡献，并处于独特的地位，可以应对拟议的挑战。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、指导和准备研究文件，以帮助他们未来的工业或学术努力。具体地说，这项研究将被整合到宾夕法尼亚州立大学的校际基因工程机器计划(IGEM)中，该计划旨在为本科生和高中生提供对合成生物学的亲身接触。研究成果将通过期刊出版物和会议报告、准备蛋白质设计选修课以及通过网络提供所有开发的供学术界和工业界使用的软件程序、数据库和实验方案来广泛传播。合作实验室将使PI能够扩大这一活动的影响，超出拟议研究的细节。

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.