CONFORMATIONAL ANALYSIS BY ENERGY EMBEDDING

通过能量嵌入进行构象分析

• 批准号: 3292163
• 负责人: GORDON M CRIPPEN
• 金额: $ 9.17万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1985
• 资助国家: 美国
• 起止时间: 1985-11-01 至 1993-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3292163
• 关键词: chemical bond; chemical models; computer simulation; conformation; intermolecular interaction; molecular energy level; protein structure; thermodynamics

Most molecules are free to assume a variety of conformations by rotating about single bonds, and which conformations they prefer can have a great influence on their properties. For example, enzymes are active as catalysts and subject to biochemical controls on their activity when the polypeptide chain is correctly folded in space (the native state) and inactive when incorrectly folded. Conformational analysis has been very successful in treating molecules with few degrees of freedom by approximating the free energy as a function of conformation, and then locating regions of conformation space having relatively low energy. For molecules as large or larger than small peptide hormones, however, there are an astronomical number of local energy minima scattered throughout a conformation space of very high dimensionality, and only a vanishingly small fraction of these have low enough energy to be physically significant. A thorough search would require an amount of computer time that increases exponentially with the size of the molecule such that a decapeptide is well beyond the reach of any foreseeable computers. It does us little good to sequence the entire genome of a virus (or eventually the human genome) if we are unable to predict the folding of the corresponding proteins and hence their function. Similarly genetic engineering needs to know what alterations will improve a protein's properties, such as increasing its thermal stability or changing an enzyme's specificity. Energy embedding is a technique we have pioneered for sidestepping this problem entirely by treating the molecule in the computer as if it existed in many more than three dimensions. Our long term goal is to apply energy embedding to the prediction of the low-resolution global folding of proteins. We are learning that successful predictions are guided entirely by a potential function that may have numerous local minima, but must prefer the native conformation in a global sense. Thus developing a suitable potential is our top priority, and we have invented a systematic method for carrying this out, based on linear programming. Since most tests of molecular mechanics potential functions examine their properties only in the neighborhood of experimentally determined conformations energy embedding Is a unique tool for validating their global character. Therefore another short term goal is to examine their properties only in the neighborhood of experimentally determined conformations, energy embedding is a unique tool for validating their global character. Therefore another short term goal is to examine the global predictive ability of standard potential functions, such as AMBER and MM2, on small molecules. A third immediate task is to vectorize our computer programs in order to make larger molecules feasible subjects of study.

大多数分子可以自由地呈现各种构象 围绕单键旋转，以及它们喜欢的构象 会对它们的性质有很大的影响。例如, 酶作为催化剂很活跃，并受生化控制。 当多肽链正确折叠时，它们的活性 在空间中(原生状态)，不正确折叠时处于非活动状态。 构象分析在治疗 几个自由度的分子通过近似自由 能量作为构象的函数，然后定位区域 能量相对较低的构象空间。对于分子AS 然而，大或大于小的多肽荷尔蒙，有一个 分散在整个宇宙中的局部能量极小值的天文数 高维的构象空间，只有一个 其中极小的一部分具有足够低的能量 身体上意义重大。一次彻底的搜查需要 的大小呈指数增长的计算机时间 十肽是一种分子，它远远超出了任何 可预见的计算机。它对我们没有什么好处排序 病毒的整个基因组(或最终人类基因组)，如果我们是 无法预测相应蛋白质的折叠和 因此，它们起到了作用。同样，基因工程需要知道 哪些变化会改善蛋白质的性质，例如 提高其热稳定性或改变酶的 专一性。能量嵌入是我们首创的一项技术 通过处理分子中的分子来完全回避这个问题 计算机，就好像它存在于更多的三维空间中。我们的 长期目标是将能量嵌入应用于预测 蛋白质的低分辨率全球折叠。我们正在学习 成功的预测完全是由一种潜在的 函数，该函数可能有许多局部极小值，但必须优先选择 全球意义上的原生构象。从而开发出一种合适的 潜力是我们的首要任务，我们已经发明了一种系统的 基于线性规划的实现这一点的方法。自.以来 大多数分子力学势函数的测试都检验了它们的 属性仅位于实验确定的 构象能量嵌入是一种独特的验证工具 他们的全球性特征。因此，另一个短期目标是 只在附近检查他们的财产 实验确定的构象，能量嵌入是一种 独特的工具，用于验证它们的全球特征。因此 另一个短期目标是检查全球预测能力 标准势函数，如琥珀和MM2，在小的 分子。第三项迫在眉睫的任务是将我们的计算机矢量化 为了使更大的分子成为可行的课题的计划 学习。