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（由申请人提供）：生物物理学的一个重大挑战是在结构和溶剂条件下理解蛋白质的折叠、稳定性、灵活性和功能。采用一种新的距离约束模型（DCM），从已知的三维结构出发，在特定的热力学条件下（即温度、pH、离子强度等）准确预测水溶液中蛋白质的稳定性。该项目建立在PI先前成功开发有效的刚性图算法的基础上，该算法用于识别作为固定约束拓扑模型的蛋白质中的柔性和刚性区域，以及DCM的开发。DCM基于网络刚性是焓-熵补偿的潜在机制的假设，产生了一个数学上精确的算法来解释自由能分解中的非可加性。概念证明，最小DCM，将在本项目中扩展，包括基本熵补偿机制的明确建模，包括(A)水合作用，(b)疏水相互作用，(c)静电相互作用，(d)残留物特定参数化。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更接近于完全可转移的参数化。然而，参数可转移性并不是这项工作的要求，因为我们的最小DCM的效用已经牢固地建立起来了。这项工作的第一个成果将是发布一种快速计算工具，该工具可以在实际计算时间内和谐地量化蛋白质设计应用所需的稳定性和灵活性。例如，蛋白质柔韧性的局部细节被量化，以确定对配体结合和变构构象变化的诱导配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见解。第二个结果将是一个公共访问的QSFR数据库，为用户提供广泛访问DCM结果和分析工具，将为用户提供一种实用的方法，以更好地了解后地理时代所需的现实计算时代的蛋白质功能。