From Protein Structure Predictions to Dynamics

亚利桑那州立大学的Matthias Heyden教授获得了化学学部化学理论、模型和计算方法（CTMC）项目的奖励，以开发表征和预测蛋白质集体动力学的新方法。目标是在基于结构的蛋白质设计过程中包括动力学及其对蛋白质功能的影响，并消除目前设计高效酶催化剂的限制。这有望使定制酶的设计，催化新的反应和取代能源和成本密集型的过程中有价值的化合物的合成。关键的发展将是在全原子计算机模拟中捕捉蛋白质非谐波低频运动的一系列新算法。这个项目的计算性质被进一步用于为在线本科生开发可以远程进行的研究机会。长期以来，蛋白质结构一直被认为是蛋白质功能的关键决定因素，例如酶催化特定化学反应的能力。然而，越来越多的证据表明，结构本身不足以解释许多酶的催化效率，它们的活性依赖于多种不同构象的共存以及它们之间的转变。虽然基于序列的蛋白质结构预测的准确性最近有了显着的改进，并且存在解决设计折叠成预先选择的结构基序的氨基酸序列的逆向问题的策略，但我们目前预测蛋白质功能动力学的能力是有限的，即使一旦结构已知。全原子分子动力学模拟提供了一个具有足够微观细节的模型，但是与构象波动采样相关的计算成本限制了它们在少数系统中的应用。另一方面，粗粒度模型和非弹性网络模型计算效率高，但缺乏微观细节。在这里，我们提出了一套新的方法，从时间尺度上的全原子分子动力学模拟的波动中提取易受大规模运动影响的集体蛋白质自由度，这些方法可用于高通量模拟。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Professor Matthias Heyden at Arizona State University is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division for the development of new methods to characterize and predict collective dynamics in proteins. The goal is to include dynamics and its effect on protein function in structure-based protein design processes and eliminate current limitations in the design of efficient enzyme catalysts. This promises to enable the design of tailored enzymes that catalyze novel reactions and replace energy and cost intensive processes in the synthesis of valuable compounds. The key development will be a series of new algorithms that capture anharmonic low-frequency motions of proteins in all-atom computer simulations. The computational nature of this project is further used to developed research opportunities for online undergraduate students that can be carried out remotely.Protein structure has long been regarded as the key determinant of protein function, for example, the ability of enzymes to catalyze a specific chemical reaction. However, an increasing body of evidence shows that structure alone is insufficient to explain the catalytic efficiency of many enzymes, whose activity instead relies on the co-existence of multiple distinct conformations and the transitions between them. While the accuracy of sequence-based protein structure predictions has seen dramatic recent improvements and strategies exist to tackle the inverse problem of designing amino acid sequences that fold into pre-selected structural motifs, our current ability to predict the functional dynamics of protein is limited even once the structure is known. All-atom molecular dynamics simulations provide a model with sufficient microscopic detail, but the computational costs associated with the sampling of conformational fluctuations limits their application to a small number of systems. On the other hand, coarse-grained models and inelastic network models are computationally efficient but lack microscopic detail. Here, we propose a new set of methods to extract collective protein degrees of freedom susceptible to large-scale motion from fluctuations in all-atom molecular dynamics simulations on timescales that are accessible to high-throughput simulations.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.