ENERGY SMOOTHING METHODS FOR CONFORMATIONAL SEARCH

生物聚合物的计算机建模面临的双重挑战是在能量学描述中实现化学准确性，并充分搜索大型柔性系统的构象空间。 本计画提出改进及扩展势能平滑技术在构象搜寻中的应用。 目前的搜索方法，如模拟退火，是至关重要的结构生物学正在进行的研究。 搜索问题可以在很大程度上克服采样的数学变换的势能表面比原来的表面更平滑。 在这个项目中，来自Scheraga在扩散方程方法上的工作和Straub在高斯密度退火上的工作的想法形成了新的势能平滑搜索（PSS）算法的基础，在构象搜索应用中表现出更大的收敛范围。 推导出粗粒化、光滑化的能量函数和溶剂化模型的解析表达式。 更好的局部搜索启发式算法和各向异性原子概率分布的结合应该会产生优于广泛使用的退火协议的上级组合方法。 对于许多系统，PSS方法可以在单个确定性计算中产生全局能量最小值。 一个相关的算法，构象扫描，发现了一组结构，代表了独特的低能量盆地，跨越有机分子，肽和小蛋白质的潜在表面。 这些平滑算法将被应用于构象搜索和结构优化，包装的小分子晶体，灵活的配体对接到蛋白质结合位点，和分子构象的表征和聚类的问题。重点将放在螺旋跨膜蛋白，如血型糖蛋白，细菌视紫红质，视紫红质和G蛋白偶联受体家族的发展模式。跨膜螺旋之间的相互作用是许多完整膜蛋白的结构和功能的中心决定因素。 充分了解这些系统需要原子分辨率的结构知识，这是很难通过结晶NMR方法获得。 PSS方法代表了一个适当的和有效的搜索模式，这些生物学上重要的系统建模。

The twin challenges in computer modeling of biopolymers are achieving chemical accuracy in the description of energetics, and adequately searching conformational space for large flexible systems. This project proposes to improve and extend the application of potential energy smoothing techniques for conformational search. Current search methods, such as simulated annealing, are critical to much of the ongoing research in structural biology. The search problem can be largely overcome by sampling on mathematically transformed potential energy surfaces which are smoother than the original surfaces. In this project, ideas derived from work by Scheraga on the Diffusion Equation Method and Straub s work on Gaussian Density Annealing form the basis of new Potential Smoothing Search (PSS) algorithms exhibiting a much larger range of convergence in conformational search applications. Analytical expressions for course-grained, smoothable energy functions and solvation models are derived. Incorporation of better local search heuristics and anisotropic atomic probability distributions should result in a combined method superior to widely used annealing protocols. For many systems, PSS methods can produce the global energy minimum in a single deterministic calculation. A related algorithm, Conformational Scanning, finds sets of structures representing unique low energy basins spanning the potential surfaces of organic molecules, peptides and small proteins. These smoothing algorithms will be applied to problems in conformational search and structure refinement, packing of small molecule crystals, flexible ligand docking to protein binding sites, and the characterization and clustering of molecular conformations. Emphasis will be placed on developing models for helical transmembrane proteins such as glycophorin, bacteriorhodopsin, rhodopsin and the family of G-protein coupled receptors. Interactions between transmembrane helices are central determinants of structure and function for many integral membrane proteins. Full understanding of these systems requires atomic resolution structural knowledge which is difficult to obtain via crystallographic of NMR methods. PSS methods represent an appropriate and efficient search paradigm for modeling these biologically important systems.