Side Chain Driven Refinement of Protein Structure

侧链驱动的蛋白质结构精化

• 批准号: 8138246
• 负责人: JERRY W TSAI
• 金额: $ 9.9万
• 依托单位: UNIVERSITY OF THE PACIFIC-STOCKTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8138246
• 关键词: Address; Algorithms; Area; CASP7 gene; Cereals; DNA Sequence Rearrangement; Data; Dependence; Development; Educational workshop; Elements; Environment; Family; Genomics; Goals; High Performance Computing; Homologous Gene; Methods; Modeling; Molecular Conformation; Movement; National Institute of General Medical Sciences; Nature; Outcome; Positioning Attribute; Principal Investigator; Process; Protein Conformation; Proteins; Relative (related person); Research; Research Personnel; Side; Staging; Structure; Techniques; Torsion; Variant; Vertebral column; Work; Workplace; base; comparative; computing resources; defined contribution; improved; innovation; molecular dynamics; novel; programs; protein structure; protein structure function; statistics; structural genomics; success

DESCRIPTION (provided by applicant): We propose to develop a more accurate refinement algorithm that addresses the major goals from the RFA on High Accuracy Protein Structure Modeling. Better refinement of the starting template toward the native structure is a primary step in improving the predictions from close as well as remote homologs. At this level of structure prediction, where the conformational space is limited to a single fold family, sequence-specific differences in tertiary structure determine the perturbations necessary to refine a template toward its native structure. However, current refinement methods are dominated by random searches of local backbone conformations and only consider tertiary structure (such as side-chain packing) indirectly as an outcome to these main-chain movements. As supported by the data in the Preliminary Results section, this proposal is based on the hypothesis that a more accurate refinement method needs to be driven by tertiary structure. Therefore, the major goal of this proposal is to statistically model and apply more exact descriptions of the variation in tertiary structure to improve protein structure refinement in comparative modeling. In particular, our analysis will more clearly define the contributions to protein conformation from multi-bodied, tertiary interactions versus those determined by the linear protein backbone. As a new investigator, this proposal continues my group's long-term objective of discovering the determinants of protein structure and function, and we have assembled a collaborative, multi-disciplinary team of computational biochemists and statisticians with expertise in development of algorithms modeling protein structure, Bayesian non-parametric techniques, and high performance computing. With our computational resources and environment, we will complete the following objectives framed in the three stages of our refinement algorithm. First, we will create conformationally "relaxed" starting structures that will have a higher likelihood of reaching the native state. Secondly, we will use our relative packing group construct to develop a side-chain centric, refinement move set. This move set will be incorporated into a structure build-up routine based on distance geometry. Lastly, we will derive selection algorithms that will identify near native models. By emphasizing that sequence specific variation in tertiary structure determines a protein's backbone, the proposed research represents a subtle but innovative shift in perspective to protein refinement of comparative models.

描述（由申请人提供）：我们建议开发一种更准确的完善算法，该算法以高精度蛋白质结构建模来解决RFA的主要目标。更好地改进了启动模板朝天然结构进行改进，这是改善关闭和远程同源物的预测的主要步骤。在这种结构预测的水平上，构象空间仅限于单个折叠族，而序列特异性差异确定了将模板朝向其天然结构所需的扰动。但是，当前的精炼方法由对局部主链构象的随机搜索进行主导，并且仅将三级结构（例如侧链堆积）间接地视为这些主链运动的结果。正如初步结果部分中的数据所支持的那样，该提案基于以下假设：更准确的改进方法需要由第三级结构驱动。因此，该提案的主要目标是在统计上建模并应用更精确的三级结构变化描述，以改善比较模型中的蛋白质结构的细化。特别是，我们的分析将更清楚地定义了对蛋白质构象的贡献，该蛋白质构象与线性蛋白主链确定的分析相互作用与蛋白质相互作用。作为一名新的研究者，该建议继续我小组的长期目标，即发现蛋白质结构和功能的决定因素，我们组装了一个合作的，多学科的计算生物化学家和统计学家的团队，并在算法开发蛋白质结构的发展方面具有专业知识，对蛋白质结构进行了建模，贝叶斯非参与技术和高性能计算。借助我们的计算资源和环境，我们将在精炼算法的三个阶段完成以下目标。首先，我们将创建构象的“放松”起始结构，这些结构将具有更高的可能性达到本地状态的可能性。其次，我们将使用我们的相对包装组构造来开发以侧链为中心的精致运动集。此移动集将根据距离几何形状合并到结构堆积程序中。最后，我们将得出选择识别本机模型的选择算法。通过强调第三级结构的序列特异性变化决定了蛋白质的主链，提出的研究代表了对比较模型的蛋白质完善的微妙但创新的转变。

项目成果

Understanding the general packing rearrangements required for successful template based modeling of protein structure from a CASP experiment.

• DOI: 10.1016/j.compbiolchem.2012.10.008
• 发表时间: 2013-02
• 期刊: COMPUTATIONAL BIOLOGY AND CHEMISTRY
• 影响因子: 3.1
• 作者: Day, Ryan;Joo, Hyun;Chavan, Archana C.;Lennox, Kristin P.;Chen, Y. Ann;Dahl, David B.;Vannucci, Marina;Tsai, Jerry W.
• 通讯作者: Tsai, Jerry W.

Near-native protein loop sampling using nonparametric density estimation accommodating sparcity.

• DOI: 10.1371/journal.pcbi.1002234
• 发表时间: 2011-10
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Joo H;Chavan AG;Day R;Lennox KP;Sukhanov P;Dahl DB;Vannucci M;Tsai J
• 通讯作者: Tsai J

Relative packing groups in template-based structure prediction: cooperative effects of true positive constraints.

基于模板的结构预测中的相对包装组：真正约束的协同效应。

• DOI: 10.1089/cmb.2010.0078
• 发表时间: 2011
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Day,Ryan;Qu,Xiaotao;Swanson,Rosemarie;Bohannan,Zach;Bliss,Robert;Tsai,Jerry
• 通讯作者: Tsai,Jerry