Predicting protein flexibility and stability

预测蛋白质的灵活性和稳定性

• 批准号: 7185134
• 负责人: Donald JACOBS
• 金额: $ 37.91万
• 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/7185134
• 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

DESCRIPTION (provided by applicant): 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 stability 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.

描述（申请人提供）：生物物理学的一个巨大挑战是了解蛋白质折叠、稳定性、灵活性和结构和溶剂条件方面的功能。采用一种新的距离约束模型（DCM）从已知的三维结构准确预测蛋白质在特定热力学条件（即温度、pH、离子强度等）下在水溶液中的稳定性。该项目建立在PI先前成功开发高效的刚性图算法以识别蛋白质中的柔性和刚性区域的基础上，该蛋白质建模为固定约束拓扑结构，并开发DCM。DCM是基于网络刚性是熵补偿的基本机制的假设，产生数学上精确的算法来解释自由能分解中的非加性。概念证明，最小DCM，将在这个项目中扩展到包括显式建模的基本熵补偿机制，包括（a）水化，（B）疏水相互作用，（c）静电相互作用，（d）残留物的具体参数化。这些扩展将允许在混合溶剂条件下预测蛋白质稳定性，并使DCM更接近完全可转移的参数化。然而，参数的可转移性是不是一个要求，这项工作提出的，因为我们的最小DCM的效用已经牢固地建立。这项工作的第一个成果将是发布一个快速的计算工具，和谐地量化的稳定性和灵活性，在实际计算时间所需的蛋白质设计应用程序。例如，蛋白质柔性的局部细节被量化以鉴定对于配体结合和变构构象变化的诱导拟合重要的相关原子运动。DCM与蛋白质家族进化描述的协同应用将为量化稳定性/灵活性关系（QSFR）的家族变异性提供关键的见解。第二个成果将是一个公众可访问的QSFR数据库，为用户提供广泛的访问DCM结果和分析工具，将为用户提供一个实用的手段，以更好地了解蛋白质的功能，在现实的计算时间所需的后地质时代。