Predicting protein flexibility and stability

预测蛋白质的灵活性和稳定性

• 批准号: 7369816
• 负责人: Donald JACOBS
• 金额: $ 37.35万
• 依托单位: UNIVERSITY OF NORTH CAROLINA CHARLOTTE
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7369816
• 关键词: Accounting; Algorithms; Alzheimer' s Disease; Attention; Biochemistry; Bioinformatics; Biomedical Research; Biophysics; California; Collaborations; Complement component C1s; Computational Biology; Computer software; Condition; Data Set; Databases; Development; Diffusion; Disease; Disruption; Electrostatics; Entropy; Enzymes; Equilibrium; Evolution; Figs - dietary; Financial compensation; Free Energy; Generic Drugs; Glass; Goals; Grant; Graph; Gray unit of radiation dose; Growth; Heating; Homology Modeling; Hydration status; Hydrogen Bonding; Hydrophobic Interactions; Individual; Ionic Strengths; Joints; Large-Scale Sequencing; Lead; Ligand Binding; Liquid substance; Literature; Measures; Mechanics; Mediating; Methodology; Minority; Modeling; Molecular; Motion; Muscle Rigidity; Nature; Numbers; Online Systems; Orthologous Gene; Outcome; Pathway interactions; Pharmacology; Physical Chemistry; Physics; Pliability; Probability; Process; Protein Family; Proteins; Range; Reaction; Reproducibility; Research; Research Personnel; Research Support; S06 grant; Scheme; Science; Score; Site; Solutions; Solvents; Specific qualifier value; Structure; Structure-Activity Relationship; System; Techniques; Temperature; Testing; Thermodynamics; Time; Training; Underrepresented Minority; Universities; Work; aqueous; base; catalyst; comparative; computerized tools; concept; design; enthalpy; experience; improved; insight; mathematical algorithm; molecular recognition; novel; professor; programs; protein folding; protein function; protein structure function; prototype; success; theories; three dimensional structure; tool; two-dimensional

A grand challenge of biophysics is to understand protein folding, stability, flexibility, and function in terms of structure and solvent condition. A novel Distance Constraint Model (DCM) is employed to accurately predict protein stability in aqueous solution under specified thermodynamic conditions (i.e. temperature, pH, ionic strength, etc) from known three-dimensional structure. This project builds upon prior success of the PI in developing efficient rigidity-graph algorithms to identify flexible and rigid regions in proteins modeled as a fixed constraint topology, and development of the DCM. The DCM is based on the hypothesis that network rigidity is an underlying mechanism for enthalpy-entropy compensation, yielding a mathematically precise algorithm to account for non-additivity in free energy decompositions. A proof of concept, minimal DCM, will be extended in this project to include explictit modeling of essential entropy-compensation mechanisms that include (a) hydration, (b) hydrophobic interactions, (c) electrostatics interactions, with (d) a residue-specific parameterization. These extensions will allow prediction of protein stability in mixed solvent conditions, and bring the DCM closer to a fully transferable parameterization. However, parameter transferability is not a requirement of this proposed work, as the utility of our minimal DCM has been firmly established. The first outcome.of this work will be the release of a fast computational tool that harmoniously quantifies stabilitiy and flexibility in practical computing times necessary for protien design applications. For example, local- details of protein flexibility are quantified to identify correlated atomic motions important for induced fit of ligand binding and allosteric conformational changes. Synergistic application of the DCM with protein family evolutionary descriptions will provide key insight into familial variability of Quantified Stability/Flexibility Relationships (QSFR). The second outcome will be a public accessible QSFR database providing users wide access to DCM results and analysis tools will give users a practical means to better understand protein function in realistic computing times needed for the post-geonomic era.

生物物理学的巨大挑战是了解蛋白质折叠，稳定性，灵活性和功能 结构和溶剂状况。采用新颖的距离约束模型（DCM）来准确预测 在指定的热力学条件下水溶液中的蛋白质稳定性（即温度，pH，离子 强度等来自已知的三维结构。该项目以PI在先前的成功为基础 开发有效的刚性图形算法，以识别建模为一种的蛋白质中的柔性和刚性区域 固定约束拓扑和DCM的开发。 DCM基于网络的假设 刚性是焓 - 内部补偿的潜在机制，从数学上精确地产生 算法在自由能分解中说明了非增强性。概念证明，最小DCM将 在该项目中延长，包括对基本熵补偿机制的解释建模 包括（a）水合，（b）疏水相互作用，（c）静电相互作用，（d）残基特异性 参数化。这些扩展将允许在混合溶剂条件下预测蛋白质稳定性，并且 使DCM更靠近完全可传递的参数化。但是，参数可传递性不是 这项提议的工作的要求，因为我们最小的DCM的效用已牢固确定。第一个 结果。这项工作将是快速计算工具的发布，该工具和谐地量化了稳定性 和灵活性在蛋白质设计应用所需的实用计算时间中。例如，本地 - 量化了蛋白质灵活性的详细信息，以确定对诱导拟合至关重要的相关原子运动 配体结合和变构构象变化。 DCM与蛋白质家族的协同应用 进化描述将为量化稳定性/灵活性的家族变异性提供关键的见解 关系（QSFR）。第二个结果将是为用户提供的公共访问QSFR数据库 广泛访问DCM结果和分析工具将为用户提供一种实用手段，以更好地了解蛋白质 在后时代所需的现实计算时间中的功能。