Novel Approaches to Empirical Force Field Models in Molecular Modeling via Multidimensional Scaling

通过多维尺度建立分子建模中的经验力场模型的新方法

• 批准号: 0713812
• 负责人: Robert Lewis
• 金额: $ 13.38万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713812&HistoricalAwards=false
• 关键词: Novel; Approaches; Empirical; Force; Field

The project focuses on new computational methods for the determination of biomolecular structure, which is crucial to our understanding of how biomolecules such as proteins perform the functions they do. This functional understanding, in turn, figures in the development of techniques for disease diagnosis and treatment. A widely used approach to determining the structure of biomolecules is the minimization of the molecule''s potential energy, computed using a force-field model. The potential energy accounts for such features as the stretching of bonds between atoms, the deformation of bond angles, and the interactions of non-bonded atoms (e.g., electrostatic forces). Of interest are molecular configurations that have very low potential energies, since biomolecules fold themselves into shapes that tend to minimize the potential energy: Nature is an optimizer. One seeks physically meaningful molecular configurations by applying computational techniques to systematically vary the locations of the atoms to reduce the potential energy. A serious difficulty encountered when applying computational algorithms to energy minimization arises when the search needs to move through configurations with high potential energies on the way to configurations with low potential energies. For instance, moving atoms close past one another increases the potential energy due to repulsive forces. Minimization algorithms have difficulty dealing with this situation since they cannot be sure in advance that allowing increases in potential energy will ultimately lead to configurations with lower energy. As a consequence, algorithms may halt at structures that minimize the potential energy only among nearby configurations, but not overall, and are not of physical interest. This project explores a new approach to address this difficulty. We pose the problem in terms of the interatomic distances, rather than the locations of the atoms, rewriting the potential energy in terms of these distances. Roughly speaking, our approach corresponds to adding fictitious copies of each atom, one for each of the other atoms. Of course, we must also add conditions that ensure that all the fictitious copies of an atom ultimately coalesce into a single atom. However, we use conditions that can be relaxed at intermediate steps of the energy minimization process and are only enforced as we approach a solution. This approach benefits from adding a large number of extra dimensions to the search space. As an analogy, imagine a search for the lowest point in North America, Death Valley, starting from Chicago. As purely earthbound voyagers we might be fooled into stopping at a local low point at the foot of the Rockies or around the Great Salt Lake, just as optimization algorithms might halt at local energy minimizers. But if we relax the requirement of traveling by land and allow travel by air, then we can pass over local minimizers on the ground to arrive at the desired destination. Preliminary tests have shown that our approach greatly improves the behavior of the more notoriously troublesome energy terms. This project will investigate its use for molecular structure determination and will examine extensions to other applications such as drug docking and finding transition mechanisms for chemical reactions.

该项目重点关注用于确定生物分子结构的新计算方法，这对于我们了解蛋白质等生物分子如何执行其功能至关重要。 这种功能性理解反过来又体现在疾病诊断和治疗技术的发展中。 确定生物分子结构的一种广泛使用的方法是使用力场模型计算分子势能的最小化。 势能解释了诸如原子之间的键的拉伸、键角的变形和非键合原子的相互作用（例如，静电力）。 感兴趣的是具有非常低势能的分子构型，因为生物分子将自己折叠成倾向于使势能最小化的形状：自然是一个优化器。 人们通过应用计算技术来系统地改变原子的位置以降低势能，从而寻求物理上有意义的分子构型。 一个严重的困难时遇到的应用计算算法的能量最小化时出现的搜索需要移动通过配置的方式与低势能的高势能的配置。 例如，移动的原子彼此靠近，由于排斥力而增加了势能。 最小化算法很难处理这种情况，因为它们不能预先确定允许势能的增加最终会导致具有较低能量的配置。 因此，算法可能会停止在最小化势能的结构，只有在附近的配置，但不是整体，并没有物理利益。 这个项目探索了一种新的方法来解决这个难题。 我们用原子间的距离而不是原子的位置来提出问题，用这些距离来重写势能。 粗略地说，我们的方法对应于添加每个原子的虚拟副本，每个其他原子一个。 当然，我们还必须加上一些条件，以确保原子的所有虚拟副本最终都能合并成一个原子。 然而，我们使用的条件，可以放松在能量最小化过程的中间步骤，只有当我们接近一个解决方案时才强制执行。 这种方法的好处是向搜索空间添加了大量额外的维度。 打个比方，想象一下从芝加哥出发，寻找北美的最低点死亡谷。 作为纯粹的地球旅行者，我们可能会被愚弄，停在落基山脉脚下或大盐湖周围的局部低点，就像优化算法可能会停在局部能量最小化点一样。 但是如果我们放宽对陆路旅行的要求，允许乘飞机旅行，那么我们就可以越过地面上的局部极小点到达期望的目的地。 初步测试表明，我们的方法大大改善了更臭名昭著的麻烦的能量项的行为。 该项目将研究其用于分子结构测定，并将研究扩展到其他应用，如药物对接和寻找化学反应的过渡机制。