中国酶学基础研究四十年回顾

20世纪70年代以来,我国学者做出了许多出色的、有关酶学的基础研究工作.在纪念《生物化学与生物物理进展》创刊40周年之际,作者选择性介绍了我国科学家在酶学领域所取得部分有代表性、系统性的研究成果.     

酶的分子设计、改造与工程应用

酶工程的研究已经发展到分子水平 ,在体外通过基因工程、化学、物理等手段改造酶分子结构与功能 ,大幅提高了酶分子的进化效率和催化效率 ,生产有价值的非天然酶。对酶工程学若干“热点”和前沿课题的研究、应用进行了概述 ,分析了国际上酶工程研究及应用技术、手段、方法 ,包括体外分子进化、核酶和抗体酶的设计、酶分子的定向固定化技术、酶蛋白分子的化学修饰、融合酶、人工合成及模拟酶等技术 ,并展望了酶工程的技术进步和应用的新进展。     

分子酶工程学研究进展

酶工程的研究已经发展到分子水平,通过基因操作,已实现了许多酶的克隆和表达。定点突变成为研究酶结构与功能的常规手段,并被广泛用于改善酶的性能。体外分子进化方法则大幅提高了酶分子的进化效率,并有可能发展新功能酶。融合蛋白技术的发展使构建新型多功能融合酶成为可能。这里对分子酶工程学的研究与发展情况进行了综述。      

采用渗透交联固定化方法提高细菌内酶转化率的研究

采用渗透交联方法处理固定化棒状杆菌,并利用细胞内酶顺式环氧琥珀酸水解酶催化生产L（＋）酒石酸。确定了最佳渗透交联固定化条件：多乙烯多胺浓度1．25％,作用30min;戊二醛浓度0．75％,作用15min,固定化细胞内酶转化的最适条件,底物浓度为0．5－1．0mol/L;pH8.0;温度37－42℃。结果发现,经渗透交联处理的固定化细胞酶活力比未处理的提高5倍多。而且固定化细胞的颗粒更度提高10倍多,保存期也有较大提高。     

分子模拟方法及其在分子生物学中的应用

常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。常用的分子模拟方法有 :量子力学法、分子力学方法、蒙特卡洛法和分子动力学法。四种方法各有优势 ,共同成为分子模拟的组成部分。综述了分子模拟法在分子生物学中的应用 ,最后介绍了分子模拟的发展方向 ,并预测了其未来的发展趋势。     

工业酶蛋白研究的重要进展

作为生物催化剂的核心,工业酶的研究在近年来取得了许多重要的进展,特别是具有重要意义的新酶发现、酶家族研究、酶的分子改造等领域出现了许多突破性的进展。     

用分子模拟方法研究HIV-1整合酶突变体的耐药性机理

二酮酸类化合物(DKAs)是目前最有前景的HIV-1整合酶(integrase, IN)抑制剂．为了解DKAs引起的多种耐药株共有的耐药性机理,选择3种S-1360引起的IN耐药突变体,用分子对接和分子动力学模拟,研究了野生型和突变型IN与S-1360的结合模式,基于该结合模式探讨了3种耐药突变体所共有的耐药性机理．结果表明：在突变体中,S-1360结合到耐药突变IN核心区中的位置靠近功能loop 3区却远离与 DNA结合的关键残基,结合位置不同导致S-1360的抑制作用部分丧失;残基138到166区域的柔性对IN发挥生物学功能很重要,S-1360能与DNA结合的关键残基N155及K159形成氢键,这2个氢键作用降低了该区域的柔性,突变体中无类似氢键,因而该区域柔性增高;在突变体中,S-1360的苯环远离病毒DNA结合区,不能阻止病毒DNA末端暴露给宿主DNA;T66I突变导致残基Ⅰ的长侧链占据IN的活性口袋,阻止抑制剂以与野生型中相同的方式结合到活性中心,这均是产生抗药性的重要原因．这些模拟结果与实验结果吻合,可为抗IN的抑制剂设计和改造提供帮助．     

蓖麻毒素A链突变体(MRTA)的分子设计

利用同源模建的方法,借助分子力学优化、分子动力学模拟退火设计构建了删除部分氨基酸序列的蓖麻毒素Ａ链突变体（ＭＲＴＡ）。采用泊松—玻尔兹曼方程对比分析了蓖麻毒素Ａ链（ＲＴＡ）与ＭＲＴＡ表观静电势分布,研究ＲＴＡ与ＭＲＴＡ蛋白表面静电性质;通过半经验量子化学ＡＭ１与分子力学结合方法探讨ＲＴＡ与ＭＲＴＡ功能域氨基酸前线分子轨道性质、能级分布,从理论上预测ＭＲＴＡ功能活性     

酶化学模拟研究进展

以主－客体化学和超分子化学为理论，介绍了各种仿酶模型（如金属卟啉、大环配体、线性和大环聚醚、膜体系、环糊精、抗体、聚全物等）及其模拟酶的研究进展。     

生物大分子的自由能计算方法

在本文中,我们总结了计算生物物理中广泛使用的经典的自由能计算方法,主要可以归纳为三大类:第一类是基于过渡态理论,利用路径系综得到自由能的方法;第二类方法采用一些经验势来补偿自由能表面达到均匀采样的目标;最后一种是将整个转换径分段计算,然后拼接完成得到完整自由能的方法.通过详细介绍自由能计算的重要性和实施细节,希望能够让...     

Computational modeling of carbohydrate processing enzymes reactions

Carbohydrate processing enzymes are of biocatalytic interest. Glycoside hydrolases and the recently discovered lytic polysaccharide monooxygenase for their use in biomass degradation to obtain biofuels or valued chemical entities. Glycosyltransferases or engineered glycosidases and phosphorylases for the synthesis of carbohydrates and glycosylated products. Quantum mechanics-molecular mechanics (QM/MM) methods are highly contributing to establish their different chemical reaction mechanisms. Other computational methods are also used to study enzyme conformational changes, ligand pathways, and processivity, e.g. for processive glycosidases like cellobiohydrolases. There is still a long road to travel to fully understand the role of conformational dynamics in enzyme activity and also to disclose the variety of reaction mechanisms these enzymes employ. Additionally, computational tools for enzyme engineering are beginning to be applied to evaluate substrate specificity or aid in the design of new biocatalysts with increased thermostability or tailored activity, a growing field where molecular modeling is finding its way.     

工业蛋白质构效关系的计算生物学解析

工业催化用酶已经成为现代生物制造技术的核心"芯片"。不断设计和研发新型高效的酶催化剂是发展工业生物技术的关键。工业催化剂创新设计的科学基础是对酶与底物的相互作用、结构与功能关系及其调控机制的深入剖析。随着生物信息学和智能计算技术的发展,可以通过计算的方法解析酶的催化反应机理,进而对其结构的特定区域进行理性重构,实现酶催化性能的定向设计与改造,促进其工业应用。聚焦工业酶结构-功能关系解析的计算模拟和理性设计,已成为工业酶高效创制改造不可或缺的关键技术。本文就各种计算方法和设计策略以及未来发展趋势进行简要介绍和讨论。     

Overview of simulation studies on the enzymatic activity and conformational dynamics of the GTPase Ras

Over the last 40 years, we have learnt a great deal about the Ras onco-proteins. These intracellular molecular switches are essential for the function of a variety of physiological processes, including signal transduction cascades responsible for cell growth and proliferation. Molecular simulations and free energy calculations have played an essential role in elucidating the conformational dynamics and energetics underlying the GTP hydrolysis reaction catalysed by Ras. Here we present an overview of the main lessons from molecular simulations on the GTPase reaction and conformational dynamics of this important anti-cancer drug target. In the first part, we summarise insights from quantum mechanical and combined quantum mechanical/molecular mechanical simulations as well as other free energy methods and highlight consensus viewpoints as well as remaining controversies. The second part provides a very brief overview of new insights emerging from large-scale molecular dynamics simulations. We conclude with a perspective regarding future studies of Ras where computational approaches will likely play an active role.     

Sensitivity of molecular dynamics simulations to the choice of the X-ray structure used to model an enzymatic reaction

A subject of great practical importance that has not received much attention is the question of the sensitivity of molecular dynamics simulations to the initial X-ray structure used to set up the calculation. We have found two cases in which seemingly similar structures lead to quite different results, and in this article we present a detailed analysis of these cases. The first case is acyl-CoA dehydrogenase, and the chief difference of the two structures is attributed to a slight shift in a backbone carbonyl that causes a key residue (the proton-abstracting base) to be in a bad conformation for reaction. The second case is xylose isomerase, and the chief difference of the two structures appears to be the ligand sphere of a Mg2+ metal cofactor that plays an active role in catalysis.     

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.     

Molecular modeling of green fluorescent protein: structural effects of chromophore deprotonation

Molecular dynamics (MD) simulations were carried out to study the conformational rearrangement induced by deprotonation of the fluorescent chromophore in GFP, as well as the associated changes in the hydrogen-bonding network. For both the structures with either a neutral or an anionic chromophore, it was found that the beta-barrel was stable and rigid, and the conformation of the chromophore was consistent with the available x-ray structure. The conformational change in Thr203 due to deprotonation was also found to be consistent with the three-state isomerization model. Although GFP is highly fluorescent, denatured-GFP is nonfluorescent, indicating that the environment of the protein plays an important role in its fluorescence behavior. Our MD simulations, which explore the effect of the protein shell on the conformation of the chromophore, find the flexibility of the central chromophore to be significantly restricted due to the rigid nature of the protein shell. The hydrogen-bonding between the chromophore and neighboring residues was also shown to contribute to the chromophore rigidity. In addition to the MD studies, quantum mechanics/molecular mechanics (QM/MM) ONIOM calculations were carried out to investigate the effect of the beta-barrel on the internal rotation in the chromophore. Along with providing quantitative values for torsional rotation barriers about the bridging bond in the chromophore, the ONIOM calculations also validate our MD force field parameters.     

Escherichia coli glutaminyl-tRNA synthetase is electrostatically optimized for binding of its cognate substrates

Natural evolution has resulted in protein molecules displaying a wide range of binding properties that include extremes of affinity and specificity. A detailed understanding of the principles underlying protein structure-function relationships, particularly with respect to binding properties, would greatly enhance molecular engineering and ligand design studies. Here, we have analyzed the interactions of an aminoacyl-tRNA synthetase for which strong evolutionary pressure has enforced high specificity for substrate binding and catalysis. Electrostatic interactions have been identified as one efficient mechanism for enhancing binding specificity; as such, the effects of charged and polar groups were the focus of this study. The binding of glutaminyl-tRNA synthetase from Escherichia coli to several ligands, including the natural substrates, was analyzed. The electrostatic complementarity of the enzyme to its ligands was assessed using measures derived from affinity optimization theory. The results were independent of the details of the calculational parameters, including the value used for the protein dielectric constant. Glutamine and ATP, two of the natural ligands, were found to be extremely complementary to their binding sites, particularly in regions seen to make electrostatic interactions in the structure. These data suggest that the optimization of electrostatic interactions has played an important role in guiding the evolution of this enzyme. The results also show that the enzyme is able to effectively select for high affinity and specificity for the same chemical moieties both in the context of smaller substrates, and in that of a larger reactive intermediate. The regions of greatest non-complementarity between the enzyme and ligands are the portions of the ligand that make few polar contacts with the binding site, as well as the sites of chemical reaction, where overly strong electrostatic binding interactions with the substrate could hinder catalysis. The results also suggest that the negative charge on the phosphorus center of glutaminyl-adenylate plays an important role in the tight binding of this intermediate, and thus that adenylate analogs that preserve the negative charge in this region may bind substantially tighter than analogs where this group is replaced with a neutral group, such as the sulfamoyl family, which can make similar hydrogen bonds but is uncharged.     

Refined models of New Delhi metallo-beta-lactamase-1 with inhibitors: an QM/MM modeling study

New Delhi metallo-beta-lactamase 1 (NDM-1) has been identified as a potential target for the treatment of multi-drug resistance bacterial infections. We used molecular docking, normal MD, SIE, QM/MM MD simulations, QM/MM GBSA binding free energy, and QM/MM GBSA alanine-scanning mutagenesis techniques to investigate interactions of the NDM-1 with 11 inhibitors (Tigecycline, BAL30072, D-captopril, Penicillin G, Ampicillin, Carbenicillin, Cephalexin, Cefaclor, Nitrocefin, Meropenem, and Imipenem). From our normal MD and QM/MM simulations, the correlation coefficients between the predicted binding free energies and experimental values are .88 and .93, respectively. Then simulations, which combined QM/MM/GBSA and alanine-scanning mutagenesis techniques, were performed and our results show that two residues (Lys211 and His250) have the strongest impact on the binding affinities of the 11 NDM-1/inhibitors. Therefore, our approach theoretically suggests that the two residues (Lys211 and His250) are responsible for the selectivity of NDM-1 associated inhibitors.     

Computational studies of ligand diffusion in globins: I. Leghemoglobin

The thermally assisted diffusion of a small ligand (carbon monoxide) through a protein matrix (lupine leghemoglobin) is investigated computationally. The diffusion paths are calculated by a variant of the time-dependent Hartree approximation which we call LES (locally enhanced sampling). The variant which was recently introduced by Elber and Karplus is based on the classical TDSCF approximation of Gerber et al. The simulation enables more significant search for diffusion pathways than was possible before. This is done by increasing the number of ligand trajectories using a single trajectory for the protein. We compare qualitatively diffusion rates in leghemoglobin and in myoglobin. The calculation shows that the diffusion in leghemoglobin is much faster than the diffusion in myoglobin, in agreement with experiment. The gate in leghemoglobin is opened by fluctuations at a close contact between the B/C and the G helices. The most relevant fluctuation is the rigid shift of the C helix with respect to the G helix. This path is not observed in a comparable calculation for myoglobin. This finding is rationalized by the lack of the D helix in leghemoglobin and a significantly more flexible CE loop. Supporting experimental evidence for the importance of the CE loop in leghemoglobin can be found in the kinetics studies of Gibson et al.