Predicting protein flexibility and stability

生物物理学的一个重大挑战是了解蛋白质的折叠，稳定性，灵活性和功能， 结构和溶剂条件。采用一种新的距离约束模型（DCM）， 在特定的热力学条件（即温度、pH、离子浓度）下蛋白质在水溶液中的稳定性 强度等）。该项目建立在PI先前成功的基础上， 开发有效的刚性图算法，以识别蛋白质中的柔性和刚性区域， 固定约束拓扑，DCM的发展。DCM基于这样的假设： 刚性是熵补偿的基本机制，产生数学上精确的 算法来解释自由能分解中的非加和性。概念验证，最小DCM，将 在这个项目中扩展到包括基本熵补偿机制的显式建模， 包括（a）水合作用，（B）疏水相互作用，（c）静电相互作用，以及（d）残基特异性 参数化这些延伸将允许预测蛋白质在混合溶剂条件下的稳定性， 使DCM更接近于完全可转移的参数化。但是，参数可传递性不是一个 这项工作的要求，因为我们的最小DCM的效用已经牢固确立。第一 这项工作的成果将是发布一个快速的计算工具，和谐地量化稳定性 以及蛋白质设计应用所需的实际计算时间的灵活性。例如，本地- 蛋白质柔性的细节被量化，以识别对诱导的拟合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 进化描述将提供对量化稳定性/灵活性的家族变异性的关键见解 关系（QSFR）。第二个成果将是一个公众可访问的QSFR数据库， DCM结果和分析工具的广泛使用将为用户提供更好地了解蛋白质的实用方法 在后地球经济时代所需的现实计算时代发挥作用。

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.