IIBR: Development of enhanced computational protein design methods using a metaanalysis of enzyme dynamics

蛋白质是一种生物分子，它执行对生命至关重要的细胞过程。近年来，蛋白质内部的协同运动（动力学）与蛋白质的功能直接相关，但目前对这种关系的理解还不完全。为了进一步阐明蛋白质动力学和功能之间的联系，将使用最近开发的计算方法首先识别并随后对蛋白质中直接负责定义支撑其功能的运动的相互作用网络进行分类。有关这些网络的信息将被组织在一个数据库中，该数据库将免费提供给对研究蛋白质功能或合理地设计具有所需功能的蛋白质感兴趣的研究人员。这个数据库的潜在效用将直接通过使用其中包含的信息来改进蛋白质工程的努力来测试。使用该数据库生成的增强蛋白将通过实验表征，以努力验证并随后改进其中包含的信息。这不仅会导致对蛋白质动力学和蛋白质功能之间关系的更深层次的理解，而且还会在无数生物技术应用中找到直接的用途，包括开发新的基于蛋白质的药物，增强农产品或生产新的功能性生物材料。通过使用流行的在线蛋白质折叠游戏FoldIt，增强对蛋白质动力学和功能之间关系的理解也可以很容易地纳入教育材料，该游戏将用于寻求激发高中教育水平对STEM领域的兴趣的推广活动。提出的研究的主要目标是：1)更好地阐明蛋白质动力学与功能之间的复杂关系；2)开发新的计算蛋白质设计算法，使从头开始合理设计功能蛋白质。我们目前对蛋白质序列、结构和功能之间关系的理解很大程度上得益于这些信息在公共数据库中的聚集和传播。虽然蛋白质的相关运动与其功能之间的直接联系已经建立，但目前还没有蛋白质动力学的数据库，我们认为这严重限制了我们合理设计功能蛋白质的能力。我们将通过生成已知功能蛋白质动态特征的第一个大规模数据库来解决这一挑战，然后将这些数据聚集在酶功能类内和跨酶功能类。我们的发现将被纳入罗塞塔蛋白质设计软件，以开发基于动态的计算蛋白质设计方法，该方法随后将用于设计蛋白质内部的新动态网络。这将直接测试我们对蛋白质结构、动力学和功能之间关系的理解。我们设计的蛋白质的实验表征将有助于改进我们的计算方法，并将进一步增强我们对动力学和蛋白质功能之间相互作用的理解。本研究的长期目标将是利用本研究的经验教训，产生尖端的计算蛋白质设计工具，可用于研究酶的功能，提高现有设计酶的催化效率，并使具有所需活性的人工酶的重新设计成为可能。该项目的结果将在https://sms.asu.edu/jeremy_mills和http://ozkanlab.physics.asu.edu/research.html.This上报告。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

Proteins are biomolecules that carry out the cellular processes that are fundamental to life. It has recently become well-established that concerted motions (dynamics) within proteins are directly correlated to their function, but an incomplete understanding of this relationship currently exists. To further elucidate the link between protein dynamics and function, recently developed computational methods will be used to first identify and subsequently classify networks of interactions in proteins that are directly responsible for defining the motions that underpin their functions. Information regarding these networks will be organized in a database that will be made freely available to researchers interested in both studying protein function or rationally engineering proteins that possess desired functions. The potential utility of this database will then be directly tested by using the information contained therein to improve protein engineering efforts. Enhanced proteins generated using this database will be experimentally characterized in an effort to both validate and subsequently improve the information contained therein. This will not only lead to a deeper understanding of the relationship between protein dynamics and protein function but will also find immediate use in myriad biotechnological applications including the development of new protein-based drugs, enhanced agricultural products or the production of new, functional biomaterials. An enhanced understanding of the relationship between protein dynamics and function can also readily be incorporated into educational materials using a popular online protein folding game, FoldIt, which will be used in outreach activities that seek to inspire interest in STEM fields at the high school educational level.The primary goals of the proposed research are 1) to better elucidate the complex relationship between protein dynamics and function and 2) to develop new computational protein design algorithms that enable the rational design of functional proteins from scratch. Our current understanding of the relationship between protein sequences, their structures and functions has benefitted substantially from the aggregation and dissemination of this information in publicly accessible databases. Although a direct link between correlated motions in proteins and the functions they carry out is well-established, no database of protein dynamics currently exists, which we believe severely limits our ability rationally engineer functional proteins. We will address this challenge by generating the first large-scale database of the dynamic signatures of proteins of known function and then cluster these data both within and across enzyme functional classes. Our findings will be incorporated into the Rosetta protein design software to develop dynamics-based computational protein design methods that will subsequently be used to engineer new dynamic networks within proteins. This will serve as a direct test of our understanding of the relationship between protein structure, dynamics and function. Experimental characterization of our designed proteins will serve to improve our computational methods and will further enhance our understanding of the interplay between dynamics and protein function. The long-term goal of this study will be to use the lessons learned in this study to generate cutting edge computational protein design tools that could be used to study enzyme function, enhance the catalytic efficiencies of existing designed enzymes and enable the de novo design of artificial enzymes with desired activities.Results of this project will be reported at https://sms.asu.edu/jeremy_mills and http://ozkanlab.physics.asu.edu/research.html.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.