Computer Simulation Theory of Globular Protein Dynamics

描述（由申请人提供）：该项目解决了利用基于结构的方法进行生化功能预测的广泛的相互关联的生物学问题：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物选择和鉴定负责药物副作用的靶蛋白相互作用的方法。类似地，由于DNA-蛋白质相互作用对包括转录在内的各种生物过程至关重要，因此将开发更好地预测与DNA复合的DNA结合蛋白的身份和结构的方法。为了阐明亚细胞过程的定性行为，将进行示意性分子模拟。一个关键的想法是观察到，在进化上遥远的蛋白质中，有保守的结构和功能特征可以用于配体和蛋白质的排名和细化;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（例如羟基）的水平上也保持不变。为了实现这些目标，提出了五个具体目标：1。TASSER将得到改进和扩展，以处理原子细节的模型。2.利用多结构域全蛋白质的结构保守性，可以提高多结构域蛋白质结构预测的精度。3.将开发用于蛋白质组中脱靶蛋白的配体排序/鉴定的改进方法。4. DNA序列依赖性和蛋白质结合DNA的四级结构的预测将得到改进。5.为了探索流体动力学相互作用（被证明是重要的细胞内分子扩散）和拥挤的细胞内动力学的作用，理想化的布朗动力学模拟的亚细胞过程，如代谢途径，肌动蛋白组装的机制，肌球蛋白运动的机制沿着肌动蛋白丝，和方面的蛋白质动力学与转录将进行。在每一个阶段，将进行仔细和全面的基准测试;通常，这将是随后将开发的方法应用于蛋白质组。所有工具和数据库都将作为网络服务提供;所有软件都可下载，供本地服务器使用。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.