CAREER: FAST methods for protein folding and design

职务名称：职业：蛋白质是分子机器，在很大程度上负责从消化食物到构建细胞新成分的各种过程。许多蛋白质能够自发地从延伸的链折叠成紧凑的功能机器。一旦折叠，蛋白质就会继续进行与其稳定性和功能相关的运动。理解这些运动的功能相关性仍然极具挑战性，因为很难在原子尺度上观察运动，并提供必要的结构细节来将这些运动与蛋白质的功能联系起来。该项目的目标是1）开发用于模拟这些蛋白质运动的强大算法，2）应用这些算法来理解蛋白质如何折叠，以及3）将这些算法与生化实验结合联合收割机来设计比天然蛋白质更稳定的蛋白质。这项研究的完成将为未来的努力奠定基础，以了解蛋白质运动在细胞通信等过程中的作用，并设计用于生物燃料合成等应用的蛋白质。与这些研究目标相一致，PI将开发一个Python编程靴子训练营，教授生物学学生分析自己数据所需的基本编程技能，为更复杂的计算和实验集成提供起点，并在STEM领域开辟新的就业机会。该项目将从使用专用硬件创建的模拟数据集中识别蛋白质自由能景观的一般特性，并利用它们为商品硬件的类似研究提供支持。这项工作将指导的假设，利用优化理论的思想，探索/开发权衡将允许有效的构象搜索。根据初步分析，PI的实验室已经开始开发一种新的算法原型，称为特定性状的波动放大，或FAST。进一步开发该算法，展示其功能，并将其传播到更广泛的科学界，将为理解和设计蛋白质构象系综奠定基础。具体目标包括：1）开发特定性状波动放大（FAST）算法，以有效地探索蛋白质的构象空间，2）测试FAST是否可以折叠蛋白质，和3）测试FAST是否可以揭示设计稳定蛋白质而不干扰其功能的机会。该项目由生物科学理事会分子和细胞生物科学部分子生物物理学小组共同资助以及数学和物理科学理事会物理部的生命系统物理学计划。

Title: CAREER: FAST methods for protein folding and designProteins are molecular machines that are largely responsible for processes as varied as digestion of food to building new components of cells. Many proteins are capable of spontaneously folding from an extended chain into compact, functional machines. Once folded, proteins continue to undergo motions that are related to their stability and function. Understanding the functional relevance of these motions remains extremely challenging because it is difficult to observe movement on the atomic scale and provide the necessary structural detail to connect these motions with a protein's function. The objectives of this project are 1) to develop powerful algorithms for simulating these protein motions, 2) apply these algorithms to understand how proteins fold, and 3) to combine these algorithms with biochemical experiments to design proteins that are more stable than their natural counterparts. Completion of this research will lay the foundation for future efforts to understand the role of protein motions in processes like cellular communications and to design proteins for applications such as the synthesis of biofuels. In concert with these research objectives, the PI will develop a python programming boot camp to teach students in biology the basic programming skills required to analyze their own data, providing a starting point for more sophisticated integration of computation and experiments and opening new job opportunities in the STEM fields. This project will identify general properties of free energy landscapes of proteins from simulation datasets created with specialized hardware and leverage them to empower similar studies with commodity hardware. This work will be guided by the hypothesis that leveraging ideas from optimization theory regarding exploration/exploitation tradeoffs will allow efficient conformational searches. Based on preliminary analyses, the PI's lab has already begun to prototype a new algorithm, referred to as fluctuation amplification of specific traits, or FAST. Further developing this algorithm, demonstrating its power, and disseminating it to the broader scientific community will lay a foundation for understanding and designing protein's conformational ensembles. Specific goals include: 1) develop the fluctuation amplification of specific traits (FAST) algorithm to efficiently explore a protein's conformational space, 2) test whether FAST can fold proteins, and 3) test whether FAST can reveal opportunities for designing stabilized proteins without perturbing their functions.This project is jointly funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Physics of Living Systems Program in the Division of Physics in the Directorate of Mathematical and Physical Sciences.