Predicting protein flexibility and stability

生物物理学的一个重大挑战是从以下方面理解蛋白质的折叠、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)进行精确预测 特定热力学条件(即温度、pH、离子)下蛋白质在水溶液中的稳定性 强度等)来自已知的三维结构。该项目建立在国际和平研究所在 开发有效的刚性图算法来识别蛋白质中的柔性和刚性区域 固定的约束拓扑，以及DCM的发展。DCM的基础是假设网络 刚性是焓-熵补偿的一种基本机制，在数学上产生了精确的 在自由能分解中考虑非加性的算法。概念证明，最小的DCM，将 在本项目中进行扩展，以包括对基本的熵补偿机制的显式建模，该机制 包括(A)水合作用，(B)疏水相互作用，(C)静电相互作用，(D)特定残基 参数化。这些扩展将允许预测蛋白质在混合溶剂条件下的稳定性，以及 使DCM更接近完全可转移的参数化。但是，参数可传递性不是 这项拟议工作的需要，因为我们的最低限度的DCM的效用已经牢固地确立。第一 这项工作的结果将是一种快速计算工具的发布，该工具可以协调地量化稳定性 以及在实际计算时间内的灵活性是Protien设计应用程序所必需的。例如，本地- 蛋白质柔韧性的细节被量化，以确定对诱导拟合度重要的相关原子运动 配体结合和变构构象变化。扩张型心肌病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洞察 关系(QSFR)。第二个结果将是一个公众可访问的QSFR数据库，为用户提供 广泛获取DCM结果和分析工具将为用户提供更好地了解蛋白质的实用手段 在后地质学时代所需的现实计算时代中的作用。

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.