Ab Initio Structure Prediction of Polypeptides

描述（由申请人提供）：本项目的总体目标是提高我们对单链多肽从头计算结构预测的理解。基于我们在以下方面的最新进展：（1）溶剂化寡肽的自由能计算（Klepeis和Floudas，1999）;（B）通过alphaBB方法的确定性全局优化（Ketiman等人，1998 a，B; Floudas，2000）及其在溶剂化寡肽中的应用（Klepeis等，1998; Klepeis和Floudas，1999 b）;和（c）通过稀疏NMR限制的寡肽结构细化（Klepeis等，1999），所提出的研究旨在从氨基酸序列确定多肽的二级和三级结构，而不使用可从数据库获得的信息。我们计划重点实现以下目标： （一） 研究了一种新的方法来识别的螺旋片段的单体多肽，这是基于熵和自由能的计算重叠的五肽，和处理的静电通过非线性Poisson-Boltzmann方程。 （B） 研究一种新的方法，基于混合整数优化框架，用于确定反平行，平行和多侧β折叠，以及二硫桥的鉴定。 （c）第（1）款 研究一种基于确定性全局优化和扭转角动力学的新方法，用于在存在稀疏约束的情况下对多肽进行结构细化，这些约束来自NmR实验或通过（a）和（B）中提出的方法提供的二级结构信息。 （d）其他事项 在（a）、（B）和（c）的基础上，探索一种新的单链多肽从头算结构预测框架。 （五） 开发（a）、（B）、（c）和（d）的分布式计算算法，将其应用于多肽，并开发用于多肽三维结构的从头预测的工具。

DESCRIPTION (provided by applicant): The overall aim of this project is to improve our understanding of the ab initio structure prediction of single chain polypeptides. Based upon our recent advances in (1) free energy calculations of solvated oligopeptines (Klepeis and Floudas, 1999); (b) deterministic global optimization through the alphaBB approach (Adjiman et al., 1998a,b; Floudas, 2000) and its application to solvated oligopeptides (Klepeis et aL, 1998; Klepeis and Floudas, 1999b); and (c) structure refinement of oligepeptides through sparse NMR restraints (Klepeis et al., 1999), the proposed research is directed at determining the secondary and tertiary structure of polypeptides from the sequence of amino acids without using information available from the databases. We plan to focus on the following objectives: (a) Study a new approach for the identification of the helical segments in monomeric polypeptides that is based on entropic and free energy calculations of overlapping pentapeptides, and treatment of the electrostatics through the nonlinear Poisson-Boltzmann equation. (b) Investigate a novel approach, based upon a mixed-integer optimization framework, for the determination of antiparallel, parallel and multistanded beta sheets, as well as the identification of the disulfide bridges. (c) Study a new approach, based on deterministic global optimization and torsion angle dynamics, for the structure refinement of polypeptides in the presence of sparse restraints that arise either from NmR experiments or through secondary structure information provided by approaches such as those proposed in (a) and (b). (d) Investigate a novel framework for the ab initio structure prediction of single chain polypep tides based on the advances in (a), (b) and (c). (e) Develop distributed computing algorithms for (a), (b), (c) and (d), apply them to polypeptides, and develop tools for the ab initio prediction of three-dimensional structures of polypeptides.