Development and Application of Generalized Ensemble Algorithms for Protein-Folding

乌尔里希·汉斯曼（Ulrich Hansmann）与理论与计算化学和生物物理项目共同资助，开发一种用于探索蛋白质中崎岖能量景观的广义集合方法。新的和改进的算法将被开发用于对蛋白质进行分子模拟，并用于预测小肽的三维结构，最多可达50个氨基酸。这些算法在研究蛋白质折叠的热力学和研究观察到的结构转变方面应该是有用的。PI已经使用Met-enkephalin作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、蛋白质G和与人类甲状旁腺激素的前34个氨基酸相对应的肽片段。对蛋白质折叠方式的基本理解是当今时代的重大挑战之一。描述蛋白质构象空间的势能表面的高维性和粗大性使得对这一复杂问题的理论理解变得困难。挑战在于利用现有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，这将使全球最小值更容易定位。

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.