CONFORMATIONAL ANALYSIS BY ENERGY EMBEDDING

大多数分子可以自由地呈现各种构象， 围绕单键旋转，以及它们喜欢哪种构象 会对它们的性能产生很大的影响。 比如说， 酶作为催化剂是有活性的，并受生物化学控制 当多肽链正确折叠时， 在空间中（自然状态），并且在不正确折叠时不活动。 构象分析已经非常成功地治疗了 分子与几个自由度，通过近似的自由 能量作为构象的函数，然后定位 构象空间具有相对低的能量。 对于分子， 大或大于小肽激素，然而， 天文数字的局部能量极小值分散在整个 非常高维的构象空间，只有一个 其中极小的一部分具有足够低的能量 物理意义。 彻底搜查需要 的计算机时间，以指数方式增加的大小， 分子，使得十肽远远超出了任何分子的能力范围 可预见的计算机 对我们来说， 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， 因此也是其职能。 同样地，基因工程需要知道 什么样的改变会改善蛋白质的特性，例如 增加其热稳定性或改变酶的 的特异性能量嵌入是我们开创的一项技术 完全避开了这个问题， 计算机就好像它存在于三维空间之外。 我们 长期目标是将能量嵌入应用于预测 蛋白质的低分辨率全局折叠。 我们正在学习 成功的预测完全是由一个潜在的 函数可能有许多局部最小值，但必须首选 整体意义上的天然构象。从而开发出一种合适的 潜力是我们的首要任务，我们已经发明了一个系统的 实现这一点的方法，基于线性规划。 以来 大多数分子力学势函数的测试都是考察它们的 只有在实验确定的附近的属性 构象能量嵌入是一种独特的工具， 其全球性特征。 另一个短期目标是 只在附近检查它们的属性 实验确定的构象，能量嵌入是一个 这是一个独特的工具来验证他们的全球性。 因此 另一个短期目标是检验全球预测能力， 标准势函数，如AMBER和MM2，在小 分子。 第三个紧迫的任务是矢量化我们的计算机 计划，以使更大的分子可行的主题， study.

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.