Requimte, Faculdade de Ciências do Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.
Acc Chem Res. 2008 Jun;41(6):689-98. doi: 10.1021/ar7001045.
Computational methodologies are playing increasingly important roles in elucidating and presenting the complete and detailed mechanisms of enzymatic reactions because of their capacity to determine and characterize intermediates and transition states from both structural and energetics points of view, independent of their reduced lifetimes and without interfering with the natural reactional flux. These features are turning the field into an active and interesting area of research, involving a diverse range of studies, mostly directed at understanding the ways in which enzymes function under certain circumstances and predicting how they will behave under others. The accuracy of the computational data obtained for a given mechanistic hypothesis depends essentially on three mutually exclusive factors: the accuracy of the Hamiltonian of the reaction mechanism, consideration of the modulating aspect of the enzyme's structure in the energetics of the active center, and consideration of the enzyme's conformational fluctuations and dynamics. Although, unfortunately, it is impossible at present to optimize these crucial factors simultaneously, the success of any enzymatic mechanistic study depends on the level of equilibrium achieved among them. Different authors adopt different solutions, and this Account summarizes the most favored, with emphasis placed on our own preferences. Another crucial aspect in computational enzymatic catalysis is the model used in the calculations. Our aim is to build the simplest model that captures the essence of the catalytic power of an enzyme, allowing us to apply the highest possible theoretical level and minimize accidental errors. The choice is, however, far from obvious, ranging from simple models containing tens of atoms up to models of full enzymes plus solvent. Many factors underlie the choice of an appropriate model; here, examples are presented of very different modeling strategies that have been employed to obtain meaningful results. One particular case study, that of enzyme ribonucleotide reductase (RNR), a radical enzyme that catalyzes the reduction of ribonucleotides into deoxyribonucleotides, is one of the examples illustrating how the successive increase of the system's size does not dramatically change the thermodynamics and kinetics of the reaction. The values obtained and presented speak for themselves in that the only ones that are distinctly different are those calculated using an exceedingly small model, which omitted the amino acids that establish hydrogen bonds with the reactive unit of the substrate. This Account also describes our computational analysis of the mechanism of farnesyltransferase, a heterodimeric zinc metalloenzyme that is currently one of the most fascinating targets in cancer research. We focus on the present methodologies that we have been using, our models and understanding of the problem, and the accuracy of results and associated problems within this area of research.
计算方法在阐明和呈现酶反应的完整和详细机制方面发挥着越来越重要的作用,因为它们能够从结构和能量学的角度确定和描述中间体和过渡态,而不受其寿命缩短的影响,并且不会干扰自然反应流。这些特性使得该领域成为一个活跃且有趣的研究领域,涉及到各种研究,主要旨在了解酶在某些情况下的工作方式,并预测它们在其他情况下的行为。计算数据的准确性对于给定的机制假设取决于三个相互排斥的因素:反应机制哈密顿量的准确性、考虑酶结构在活性中心能量学中的调节方面,以及考虑酶的构象波动和动力学。虽然目前不可能同时优化这些关键因素,但任何酶促机制研究的成功都取决于它们之间达到的平衡水平。不同的作者采用不同的解决方案,本综述总结了最受欢迎的解决方案,并强调了我们自己的偏好。计算酶催化中的另一个关键方面是计算中使用的模型。我们的目标是构建最简单的模型,捕捉酶催化能力的本质,允许我们应用尽可能高的理论水平并最小化偶然误差。然而,选择远非显而易见,从包含数十个原子的简单模型到包含酶和溶剂的完整模型不等。选择受到许多因素的影响;这里介绍了不同建模策略的示例,这些策略已被用于获得有意义的结果。一个特定的案例研究,即酶核苷酸还原酶(RNR),一种自由基酶,催化核苷酸还原为脱氧核苷酸,是一个例子,说明随着系统尺寸的连续增加,反应的热力学和动力学不会发生显著变化。所获得和呈现的值不言而喻,唯一明显不同的值是使用一个非常小的模型计算的值,该模型省略了与底物反应单元建立氢键的氨基酸。本综述还描述了我们对法呢基转移酶(一种异二聚体锌金属酶)机制的计算分析,法呢基转移酶是癌症研究中最迷人的靶标之一。我们专注于我们一直在使用的当前方法、我们的模型和对问题的理解,以及该研究领域内结果的准确性和相关问题。