Development and Application of Generalized Ensemble Algorithms for Protein-Folding

乌尔里希·汉斯曼是由理论与计算化学和生物物理学项目共同资助的，旨在开发一种通用的系综方法，用于探索蛋白质中发现的崎岖能量景观。新的和改进的算法将被开发用于对蛋白质进行分子模拟，并用于预测多达大约50个氨基酸的小肽的三维结构。这些算法在蛋白质折叠的热力学研究以及研究观察到的结构转变方面应该会被证明是有用的。PI已经以甲硫氨酸脑啡肽为基准对新算法进行了大量测试，现在将把应用扩展到更大的系统，包括变色龙肽、蛋白G和对应于人类甲状旁腺激素前34个氨基酸的多肽片段。对蛋白质折叠方式的基本了解是当今时代的重大挑战之一。由于描述蛋白质构象空间的势能面的高维性和粗糙性，使得对这一复杂问题的理论理解变得困难。挑战是利用目前可用的计算机技术在这个复杂的表面上找到一个全局最小值。汉斯曼的研究提供了探索这片崎岖地形的新方法，这将使全球最低点更容易定位。

Ulrich Hansmann is cofunded with a grant from the Theoretical and Computational Chemistry and the Biophysics Program to develop of a generalized ensemble method for exploring the rugged energy landscapes found in proteins. New and improved algorithms will be developed for performing molecular simulations on proteins, and for the prediction of three dimensional structure for small peptides up to approximately 50 amino acids. The algorithms should prove useful in studies of thermodynamics of protein folding, and for investigating observed structural transitions. The PI has performed substantial testing of the new algorithms using Met-enkephalin as a benchmark, and will now extend the applications to larger systems including the chameleon peptide, Protein G, and a peptide fragment corresponding to the first 34 amino acids of human parathyroid hormone.A fundamental understanding of the way in which proteins fold is one of the grand challenges of current times. A theoretical understanding of this complex problem is made difficult by the high dimensionality and the ruggedness of the potential energy surface describing the protein conformational space. The challenge is to find a global minimum on this complex surface using currently available computer technology. Hansmann's research offers new ways of exploring this rugged landscape that will make the global minimum easier to locate.