Determinants of Folding Mechanism in Small Proteins

描述(申请人提供)：大多数蛋白质在进化上针对功能进行了优化，但不是为了折叠。因此，了解蛋白质的折叠机制将有助于我们设计在不改变其功能的情况下优化折叠的蛋白质。此外，错误折叠和聚集是囊性纤维化、阿尔茨海默病和其他淀粉样变性以及II型糖尿病等致命疾病的基础。蛋白质折叠的预测框架，特别是在非自然状态及其序列测定，将极大地影响折叠相关疾病的生物医学研究。我们将通过对一些快速折叠蛋白质的动力学和热力学的研究来开发和验证这种蛋白质折叠的预测框架。我们打算从全原子细节上了解这些蛋白质折叠机制的决定因素，并通过广泛的实验比较来验证我们的理解。显性溶剂和隐式溶剂中的全原子分子力力场将被用来表征非固有态和折叠路径。 1)我们计划：(A)研究序列和拓扑在决定两个含锌指基序的工程蛋白FSD1和PDA8D折叠速度和折叠途径中的相互作用；(B)通过突变扰乱它们的折叠路径，以达到该家族的亚微秒折叠速度。2)我们将调查：(A)是什么使基于拓扑学预测的蛋白质A的速率正确，但递增的同源结构域的预测速率偏离了40倍；(B)是什么使蛋白质A对突变如此敏感--单个突变可以将其速率改变10倍或更高；(C)是什么使递增的同源结构域在折叠速率几乎没有变化的情况下耐受序列的剧烈变化。我们将从计算上探索它们折叠过程的许多方面，包括变性状态、过渡态、中间态(如果有的话)以及通向自然态的途径，以了解这些差异。

DESCRIPTION (provided by applicant): Most proteins are evolutionarily optimized for function, but not for folding. Thus, understanding protein folding mechanisms will help us design proteins that are optimized for folding without altering their functions. In addition, misfolding and aggregation underlie fatal diseases such as cystic fibrosis, Alzheimer's disease and other amyloidoses, and type-II diabetes. A predictive framework for protein folding, particularly in the nonnative states and its sequence determination, will greatly impact biomedical research of folding-related diseases. We will develop and validate such a predictive framework for protein folding through the studies of kinetics and thermodynamics of a few fast-folding proteins. We intend to understand the determinants of folding mechanisms for these proteins at all-atom details and validate our understanding by comparing with experiment extensively. An all-atom molecular mechanics force field in both explicit solvent and in implicit solvent will be used to characterize the nonnative states and the folding pathways. 1) We plan to: (a) investigate the interplay of sequence and topology in the determination of the folding rates and pathways for two engineered proteins with Zn-finger motif: FSD1 and PDA8D; (b) perturb their folding pathways by mutation to achieve sub-microsecond folding rates in this family. 2) We will investigate: (a) what makes the predicted rate of protein A based on topology correct, but the predicted rate of Engrailed Homeodomain off by a factor of 40; (b) what makes protein A so sensitive to mutation -- a single mutation can change its rate by a factor of 10 or higher; (c) what makes Engrailed Homeodomain tolerate drastic changes in sequence with little change in folding rates. We will computationally probe many aspects of their folding processes, the denatured states, the transition states, the intermediate states if any, and the pathways towards native states to understand the differences.