Toward experimental quality protein structures: a synergistic approach

描述(申请人提供)：理解蛋白质折叠和错误折叠的机制并准确预测蛋白质结构是科学界面临的两个重要挑战。对导致天然和非天然状态形成的分子事件的详细了解是全面阐明蛋白质折叠机制的基础。随着具有代表性的蛋白质序列家族的高通量结构表征的快速发展，迫切需要高精度的计算方法，能够可靠地产生接近实验质量的结构模型，以利用结构基因组学的投资。这种方法的出现将使蛋白质结构的准确建模成为可能，这将对包括生物技术、制药工业、药物发现和一般生命科学在内的一系列领域产生重大影响。段和周建议将这两个小组的优势与互补的专业知识结合起来，开发蛋白质结构建模和优化的计算方法，最终目标是产生高度准确和可靠的蛋白质结构预测方法，其准确度与实验技术相当。目的1：段和周在第一个具体目标中提出了一种新的构象采样方法，用于蛋白质结构的精细化。一种最近开发的“生长到适合”的方法将被利用和进一步发展，以使能够从大量的预期蛋白质结构中准确地识别接近自然的结构。这些方法的进一步发展将有助于结构的改进，这将有助于改进结构，使其与实验技术获得的结构具有相当的准确性。目的2：段和周建议开发有效的自由能(计分)函数，用于精确的蛋白质结构的全原子建模。这种新的评分函数是基于集成基于知识的统计势和基于全原子物理的力场的协同概念。此外，与统计势的比较将允许对力场参数和溶剂化模型进行关键评估。目的3：段和周建议以FSD1、蛋白G和蛋白L及其各自拓扑结构不同的突变体为模型系统，研究蛋白质天然结构拓扑在蛋白质折叠中的作用；研究三级结构形成对二级结构的依赖。与实验的比较，包括对我们模型预测能力的直接测试，将是我们研究的一个组成部分，并将有助于对该方法的密切审查。 与公共健康相关：为了理解生命的基本规则，细胞是如何工作的，有必要知道蛋白质结构，这些结构对于理解它们是如何工作至关重要的。这一建议的动机是需要开发计算方法来基于初级序列可靠地预测蛋白质结构。由于蛋白质结构在药物发现中也非常有用，拟议中的工作对人类健康的一个潜在影响是在新疗法的开发领域。

DESCRIPTION (provided by applicant): Understanding the mechanisms of protein folding and misfolding and accurately predicting protein structures are two of the important challenges facing the scientific community. Detailed knowledge of the molecular events leading to the formation of both native and non-native states are the basis for a full elucidation of the protein folding mechanisms. With the rapid progress facilitated by the high-throughput structural characterization of representative protein sequence families, an essential need is the highly accurate computational methods that can reliably generate near-experimental quality structural models for capitalizing on the investment in the structural genomics. Availability of such methods would enable accurate modeling of protein structures which would have significant impact on a range of fields including biotechnology, pharmaceutical industry, drug discovery, and life sciences in general. Duan and Zhou propose to combine the strengths of the groups with complementary expertise to develop computational methods for protein structure modeling and refinement with the ultimate goal to produce highly accurate and reliable methods for protein structure prediction that have comparable accuracy to experimental techniques. Aim 1: Duan and Zhou propose to develop novel conformational sampling method for protein structure refinement in the first specific aim. A recently developed "Grow-to-Fit" method will be utilized and further developed to enable accurate identification of the near-native structures from a large ensemble of perspective protein structures. Further development of the methods will facilitate structural refinement which will help to improve the structures to be comparably accurate as those obtained from experimental techniques. Aim 2: Duan and Zhou propose to develop effective free energy (scoring) functions for accurate all-atom modeling of protein structures. This novel scoring function is based on the synergistic concept of integrating both knowledge-based statistical potential and the all-atom physics-based force field. Furthermore, comparison to the statistical potential will allow critical assessment of the force field parameters and solvation models. Aim 3: Duan and Zhou propose to examine the roles of protein native structure topology in protein folding using FSD1, Protein G and Protein L and their respective topologically distinct mutants as the model systems; to study the dependence of tertiary structure formation on secondary structures. Comparison with experiments, including direct tests on the predictive ability of our model will be an integral part of our study and will be instrumental for a close scrutiny on the approach. PUBLIC HEALTH RELEVANCE: To understand the basic rules of life, how cell works, it is necessary to know the protein structures that are critically important to understand how they work. This proposal is motivated by the need to develop computational method to reliably predict protein structures based on the primary sequence. Because protein structures are also enormously useful in drug discovery, a potential impact of the proposed work in human health is in the area of development of novel therapeutics.