Computer Simulation Theory of Globular Protein Dynamics

描述（由申请人提供）：该项目解决了广泛的相关生物学问题，利用基于结构的方法进行生化功能预测：蛋白质结构和功能预测方法的改进将导致更好的药物先导选择和鉴定药物副作用的靶蛋白相互作用的方法。同样，由于DNA-蛋白质相互作用对包括转录在内的多种生物过程至关重要，因此将开发更好地预测DNA结合蛋白与DNA复合物的身份和结构的方法。为了阐明亚细胞过程的定性行为，将进行示意图分子模拟。一个关键的观点是观察到在进化距离较远的蛋白质中，存在保守的结构和功能特征，可用于配体和蛋白质的排序和改进；值得注意的是，这种保守性甚至在蛋白质侧链和配体官能团（如羟基）的水平上也存在。为实现这些目标，提出了五个具体目标：TASSER将得到改进和扩展，以在原子细节上处理模型。2. 利用多结构域全蛋白的结构保守性，改进多结构域全蛋白的结构预测。3. 改进的方法配体排序/鉴定脱靶蛋白的蛋白质组将被开发。4. 预测DNA序列依赖性和蛋白质结合DNA的四级结构将得到改进。5. 为了探索流体动力学相互作用（对细胞内分子扩散很重要）和细胞内拥挤的作用，将进行亚细胞过程的理想布朗动力学模拟，如代谢途径、肌动蛋白组装机制、肌凝蛋白沿着肌动蛋白丝运动机制以及与转录相关的蛋白质动力学方面。在每一个阶段，都要做细致全面的对标；通常，这将是随后的应用开发的方法，以蛋白质组。所有工具和数据库将作为网络服务提供；所有软件都可以在本地服务器上下载使用。每个目标都是一个更全面的目标的一部分，以增加我们对亚细胞过程和生化功能的理解，这将有助于加速药物发现，并提供一般的生化见解。

DESCRIPTION (provided by applicant): This project addresses a broad spectrum of interrelated biological problems that exploit structure based approaches to biochemical function prediction: Proposed improvements in protein structure and function prediction methodologies will lead to better approaches for drug lead selection and identification of of-target protein interactions responsible for drug side effects. Similarly, since DNA-protein interactions are crucial to a variety of biological processes including transcription, methods that better predict the identity and structure of DNA binding proteins in complex with DNA will be developed. Towards elucidation of the qualitative behavior of subcellular processes, schematic molecular simulations will be undertaken. A key idea is the observation that across evolutionarily distant proteins, there are conserved structural and functional features that can be used for ligand and protein ranking and refinement; remarkably, this conservation holds even at the level of protein side chain and ligand functional groups (e.g. hydroxyls). To realize these objectives, five Specific Aims are proposed: 1. TASSER will be improved and extended to treat models at atomic detail. 2. Exploiting structural conservation across multiple-domain holo-proteins, multidomain protein structure prediction will be improved. 3. Improved approaches for ligand ranking/identification of off-target proteins in proteomes will be developed. 4. Prediction of the DNA sequence dependence and quaternary structure of protein-bound DNA will be improved. 5. To explore the roles of hydrodynamic interactions (shown to be important for intracellular molecular diffusion) and crowding in intracellular dynamics, idealized Brownian Dynamics simulations of subcellular processes such as metabolic pathways, the mechanism of actin assembly, the mechanism of myosin motion along actin filaments, and aspects of protein dynamics associated with transcription will be undertaken. At every stage, careful and comprehensive benchmarking will be done; often, this will be followed by application of the developed methodology to proteomes. All tools and databases will be provided as web services; all software will be downloadable for use on local servers. Each Aim is part of a more comprehensive objective to increase our understanding of subcellular processes and biochemical function that will help accelerate drug discovery as well as provide general biochemical insights.