用离散-连续模型计算NH2-,NH3和NH4+的溶剂化自由能

通过理论计算推测NH2-,NH3和NH4+在水溶液第一溶剂化层中与之直接作用的水分子分别为2,4和4个,并采用离散-连续模型计算了NH2-,NH3,NH3和NH4+在水溶液中的溶剂化自由能.结果表明,由于离散-连续模型在从头算水平考虑了溶质分子与第一溶剂化层溶剂分子之间的作用,能更准确地描述溶剂化作用.此外,采用更加符合溶液中真实情况的溶剂化构型,能得到更准确的溶剂化性质.     

基于分子表面的水化自由能预测方法

报导了一种基于加权原子表面的水合自由能预测（SAWSA）.对于不同原子类型的溶剂化参数，其参数化分为三个步骤：首先用SMARTS 语言确定不同的原子类型；然后计算每个原子的溶剂可及化表面；最后用遗传算法来优化不同原子类型的溶剂化参数.采用该模型，计算了18个蛋白质分子的水合自由能，预测结果和PB/SA的计算结果呈现了很好的线性关系（r=0.99).计算表明，SAWSA模型对有机小分子和生物大分子都具有很好的预测能力.     

水及有机溶剂中离子溶剂化焓的理论计算

离子溶剂化热力学的理论研究是一项令人感兴趣的工作.在Born理论的基础上人们先后提出了多种较详细的计算离子溶剂化热力学量的模型或公式,并对离子在不同溶剂中的溶剂化自由能进行了理论计算.本文从离子-溶剂间的相互作用力出发,分别考虑了离子溶剂化过程中造腔作用、静电吸引、静电排斥及非静电相互作用对离子溶剂化焓的贡献,得到了一个具有一定意义的、计算离子溶剂化焓的理论公式。     

固载化酶催化合成多肽的研究进展

评述了溶剂、载体性质与固载化方法、pH值、底物浓度以及反应温度等因素对固载化酶催化合成肽反应的影响,并概括了固载化酶催化合成生物活性肽、寡肽等方面的研究与应用.     

溶剂化效应对6-亚甲基环戊二烯酮与HCN的反应机理影响的理论研究

在B3LYP/6-311+G**计算水平上, 采用导体极化连续模型研究了溶剂化效应对6-亚甲基环戊二烯酮与HCN反应生成主要产物b类酸的反应机理的影响. 计算结果表明, 在溶剂中的反应机理与在气相中的反应机理一致. 溶剂化效应使反应路径中各驻点的自由能降低, 稳定化了各物质. 溶液中的活化自由能与气相相比也有所降低, 反应更容易发生, 其中CC进攻方式的活化自由能降低得更多.     

非平衡溶剂效应的约束平衡理论及在电子转移中的应用

基于经典热力学约束平衡方法,采用新非平衡溶剂化理论研究了Np O+2-Np O2+2体系电子转移反应的溶剂重组能,采用限制密度泛函理论实现电荷定域,用积分方程可极化连续介质模型获得水溶液中极化电荷.计算结果表明,新双球模型和数值解都给出了一致的溶剂重组能理论计算值.     

二氯二氰基苯醌及其阴离子自交换电子转移反应的理论研究

基于非平衡溶剂化能的约束平衡方法和溶剂重组能的新表达式, 实现了电子转移反应溶剂重组能的数值解, 研究了二氯二氰基苯醌(DDQ)及其阴离子体系DDQ-之间的自交换电子转移反应. 考虑了DDQ与DDQ-分子以平行方式形成受体-给体络合物时的两种构型. 引入线性反应坐标, 计算了该反应在不同溶剂中的溶剂重组能. 基于两态变分模型得到了反应的电子耦合矩阵元. 根据电子转移动力学模型, 计算了该自交换电子转移反应的速率常数.     

单原子离子溶剂化的研究 Ⅱ. 离子溶剂化吉布斯自由能的理论计算

本文采用单层结构模型的思想, 吸取Born模型的优点, 提出一种新的处理离子与第一溶剂化层溶剂分子间相互作用的方法。所得到的离子溶剂化吉布斯自由能的计算公式考虑了离子-溶剂相互作用能、离子内能和溶剂分子间相互作用能的贡献。对水、DMF和PC中各种类型的离子的计算与实验值符合得比较好。     

5-氟胞嘧啶互变异构的密度泛函理论计算

采用BH-HLYP/6-311＋G**方法对10种气相和水相中可能存在的5-氟胞嘧啶互变异构体进行了几何全优化, 并计算出它们的总能量和吉布斯自由能. Onsager反应场溶剂模型用于水相的计算. 计算结果表明, 5-氟胞嘧啶在气相中主要以烯醇式-氨基式形式存在, 在水相中主要以酮式-氨基式形式存在. 溶剂化自由能与异构体的气相偶极矩存在相关性．进一步求得互变异构化以及构象异构化和顺反异构化的过渡态, 探讨异构化过程中的几何结构和能量的变化.     

基于原子表面的蛋白质水合自由能预测模型

提出了一种计算蛋白质水合自由能的简化模型（SAWSA 2）.模型把蛋白质分子中的原子分为20种不同的原子类型,通过每类原子的溶剂可及化表面以及相应的溶剂化参数,就可以得到分子的水合自由能.不同原子类型的溶剂化参数通过110个蛋白质分子水合自由能拟合得到,水合自由能的标准值采用了基于求解Possion-Boltzmann方程（PB）以及分子表面计算（SA） 相结合的方法.采用得到的模型,预测了20个蛋白质分子的水合自由能,预测值的相对值和绝对值都能和PB/SA的计算值很好地吻合,大大优于两种已报导的水合自由能模型.     

Blind prediction of SAMPL4 cucurbit[7]uril binding affinities with the mining minima method

Accurate methods for predicting protein–ligand binding affinities are of central interest to computer-aided drug design for hit identification and lead optimization. Here, we used the mining minima (M2) method to predict cucurbit[7]uril binding affinities from the SAMPL4 blind prediction challenge. We tested two different energy models, an empirical classical force field, CHARMm with VCharge charges, and the Poisson–Boltzmann surface area solvation model; and a semiempirical quantum mechanical (QM) Hamiltonian, PM6-DH+, coupled with the COSMO solvation model and a surface area term for nonpolar solvation free energy. Binding affinities based on the classical force field correlated strongly with the experiments with a correlation coefficient (R²) of 0.74. On the other hand, binding affinities based on the QM energy model correlated poorly with experiments (R² = 0.24), due largely to two major outliers. As we used extensive conformational search methods, these results point to possible inaccuracies in the PM6-DH+ energy model or the COSMO solvation model. Furthermore, the different binding free energy components, solute energy, solvation free energy, and configurational entropy showed significant deviations between the classical M2 and quantum M2 calculations. Comparison of different classical M2 free energy components to experiments show that the change in the total energy, i.e. the solute energy plus the solvation free energy, is the key driving force for binding, with a reasonable correlation to experiment (R² = 0.56); however, accounting for configurational entropy further improves the correlation.     

Free energy perturbation calculations on models of active sites: Applications to adenosine deaminase inhibitors

Free energy perturbation calculations were performed to determine the free energy of binding associated with the presence of perhaps an unusual hydroxyl group in the transition state analog of nebularine, an inhibitor of the enzyme adenosine deaminase. The presence of a single hydroxyl group in this inhibitor has been found to contribute ?9.8 kcal/mol to the free energy of binding, with a 10⁸-fold increase in the binding affinity by the enzyme. In this work, we calculate the difference in solvation free energy for the 1,6-dihydropurine complex versus that of the 6-hydroxyl-1,6-dihydropurine complex to determine if this marked increase in binding affinity is attributed to an unusually hydrophobic hydroxyl group. The calculated ΔG associated for the solvation free energy is ?11.8 kcal/mol. This large change in the solvation free energy suggests that this hydroxyl is instead unusually hydrophilic and that the difference in free energy of interaction for the two inhibitors to the enzyme must be at least ca. 20 kcal/mol. Although the crystal structure for adenosine deaminase is currently not known, we attempt to mimic the nature of the active site by constructing models which simulate the enzyme-inhibitor complex. We present a first attempt at determining the change in free energy of binding for a system in which structural data for the enzyme is incomplete. To do this, we construct what we believe is a minimal model of the binding between adenosine deaminase and an inhibitor. The active site is simulated as a single charged carboxyl group which can form a hydrogen bond with the hydroxyl group of the analog. Two different carboxyl anion models are used. In the first model, the association is modeled between an acetic acid anion and the modified inhibitor. The second model consists of a hydrophobic amino acid pocket with an interior Glu residue in the active site. From these models we calculate the change in free energy of association and the overall change in free energy of binding. We calculate the free energies of interaction both in the absence and presence of water. We conclude from this that the presence of a single suitably placed-CO^?₂ group probably cannot explain the binding effect of the-OH group and that additional interactions will be found in the adenosine deaminase active site.     

Comparative study of free energies of solvation of phenylimidazole inhibitors of cytochrome P450cam by free energy simulation,AMSOL, and Poisson Boltzmann methods

Free energies of solvation of phenylimidazole inhibitors of cytochrome P450cam were determined using (1) free energy simulation, (2) AMSOL-SM2 semiempirical methods, and (3) Poisson-Boltzmann methods. The goals of this study were threefold: (1) to compare the results obtained from the three different methods, (2) to investigate the effect of inclusion of intraperturbed group interactions on free energy simulation estimates of solvation free energy differences, and (3) to investigate to what extent differences in free energies of solvation among three of these inhibitors could account for observed differences in their enzyme binding free energies. In general, relative solvation free energies obtained from the free energy simulations and AMSOL-SM2 methods give comparable results (i.e., the same rank ordering and similar quantitative results, differing significantly from results obtained using Poisson-Boltzmann methods). The free energy simulation studies suggest that the neglect of intraperturbed group interactions had little effect on rank order of free energies of solvation of the polar phenylimidazoles. The relative desolvation free energies of the three inhibitors of P450cam—1-phenylimidazole (1-PI), 2-phenylimidazole (2-PI), and 4-phenylimidazole (4-PI)—with known enzyme bound X-ray structures parallel that of their known binding affinities and could account for most of the differences in the free energies of binding of these three inhibitors to P450cam. The origin of the difference of the free energies of solution of these three inhibitors is primarily the additional interaction between solvent and N(SINGLE BOND)H group in the imidazole ring of 2- and 4-phenylimidazole that is absent in the 1-phenylimidazole isomer. This hypothesis is substantiated by a second comparison of the relative solvation free energies of 4-phenylimidazole with its methylated derivative, 3-methyl-4-phenylimidazole, also lacking an N(SINGLE BOND)H group. © 1996 by John Wiley & Sons, Inc.     

The Binding Energy Distribution Analysis Method (BEDAM) for the Estimation of Protein-Ligand Binding Affinities

The Binding Energy Distribution Analysis Method (BEDAM) for the computation of receptor-ligand standard binding free energies with implicit solvation is presented. The method is based on a well established statistical mechanics theory of molecular association. It is shown that, in the context of implicit solvation, the theory is homologous to the test particle method of solvation thermodynamics with the solute-solvent potential represented by the effective binding energy of the protein-ligand complex. Accordingly, in BEDAM the binding constant is computed by means of a weighted integral of the probability distribution of the binding energy obtained in the canonical ensemble in which the ligand is positioned in the binding site but the receptor and the ligand interact only with the solvent continuum. It is shown that the binding energy distribution encodes all of the physical effects of binding. The balance between binding enthalpy and entropy is seen in our formalism as a balance between favorable and unfavorable binding modes which are coupled through the normalization of the binding energy distribution function. An efficient computational protocol for the binding energy distribution based on the AGBNP2 implicit solvent model, parallel Hamiltonian replica exchange sampling and histogram reweighting is developed. Applications of the method to a set of known binders and non-binders of the L99A and L99A/M102Q mutants of T4 lysozyme receptor are illustrated. The method is able to discriminate without error binders from non-binders, and the computed standard binding free energies of the binders are found to be in good agreement with experimental measurements. Analysis of the results reveals that the binding affinities of these systems reflect the contributions from multiple conformations spanning a wide range of binding energies.     

Comparative assessment of computational methods for the determination of solvation free energies in alcohol‐based molecules

The determination of differences in solvation free energies between related drug molecules remains an important challenge in computational drug optimization, when fast and accurate calculation of differences in binding free energy are required. In this study, we have evaluated the performance of five commonly used polarized continuum model (PCM) methodologies in the determination of solvation free energies for 53 typical alcohol and alkane small molecules. In addition, the performance of these PCM methods, of a thermodynamic integration (TI) protocol and of the Poisson–Boltzmann (PB) and generalized Born (GB) methods, were tested in the determination of solvation free energies changes for 28 common alkane‐alcohol transformations, by the substitution of an hydrogen atom for a hydroxyl substituent. The results show that the solvation model D (SMD) performs better among the PCM‐based approaches in estimating solvation free energies for alcohol molecules, and solvation free energy changes for alkane‐alcohol transformations, with an average error below 1 kcal/mol for both quantities. However, for the determination of solvation free energy changes on alkane‐alcohol transformation, PB and TI yielded better results. TI was particularly accurate in the treatment of hydroxyl groups additions to aromatic rings (0.53 kcal/mol), a common transformation when optimizing drug‐binding in computer‐aided drug design. © 2013 Wiley Periodicals, Inc.     

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 1. Exploring the parameter space

We present a binding free energy function that consists of force field terms supplemented by solvation terms. We used this function to calibrate the solvation model along with the binding interaction terms in a self-consistent manner. The motivation for this approach was that the solute dielectric-constant dependence of calculated hydration gas-to-water transfer free energies is markedly different from that of binding free energies (J. Comput. Chem. 2003, 24, 954). Hence, we sought to calibrate directly the solvation terms in the context of a binding calculation. The five parameters of the model were systematically scanned to best reproduce the absolute binding free energies for a set of 99 protein-ligand complexes. We obtained a mean unsigned error of 1.29 kcal/mol for the predicted absolute binding affinity in a parameter space that was fairly shallow near the optimum. The lowest errors were obtained with solute dielectric values of Din = 20 or higher and scaling of the intermolecular van der Waals interaction energy by factors ranging from 0.03 to 0.15. The high apparent Din and strong van der Waals scaling may reflect the anticorrelation of the change in solvated potential energy and configurational entropy, that is, enthalpy-entropy compensation in ligand binding (Biophys. J. 2004, 87, 3035-3049). Five variations of preparing the protein-ligand data set were explored in order to examine the effect of energy refinement and the presence of bound water on the calculated results. We find that retaining water in the final protein structure used for calculating the binding free energy is not necessary to obtain good results; that is the continuum solvation model is sufficient. Virtual screening enrichment studies on estrogen receptor and thymidine kinase showed a good ability of the binding free energy function to recover true hits in a collection of decoys.     

Converging free energy estimates: MM-PB(GB)SA studies on the protein-protein complex Ras-Raf

Estimating protein-protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H-Ras/C-Raf1 using the MM-PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas-phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit-solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent-accessible surface area-dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute-solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving the Poisson-Boltzmann equation (PB) instead, the calculated binding affinity strongly depends on the atomic radii set chosen. For three GB models investigated, different absolute deviations from PB energies were found for the unbound proteins and the complex. As an alternative to normal-mode calculations, quasiharmonic analyses have been performed to estimate entropic contributions due to changes of solute flexibility upon binding. However, such entropy estimates do not converge after 10 ns of simulation time, indicating that sampling issues may limit the applicability of this approach. Finally, binding free energies estimated from snapshots of the unbound proteins extracted from the complex trajectory result in an underestimate of binding affinity. This points to the need to exercise caution in applying the computationally cheaper "one-trajectory-alternative" to systems where there may be significant changes in flexibility and structure due to binding. The best estimate for the binding free energy of Ras-Raf obtained in this study of -8.3 kcal mol(-1) is in good agreement with the experimental result of -9.6 kcal mol(-1), however, further probing the transferability of the applied protocol that led to this result is necessary.     

A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models

Docking and pharmacophore screening tools were used to examine the binding of ligands in the active site of thymidine monophosphate kinase of Mycobacterium tuberculosis. Docking analysis of deoxythymidine monophosphate (dTMP) analogues suggests the role of hydrogen bonding and other weak interactions in enzyme selectivity. Water-mediated hydrogen-bond networks and a halogen-bond interaction seem to stabilize the molecular recognition. A pharmacophore model was developed using 20 dTMP analogues. The pharmacophoric features were complementary to the active site residues involved in the ligand recognition. On the basis of these studies, a composite screening model that combines the features from both the docking analysis and the pharmacophore model was developed. The composite model was validated by screening a database spiked with 47 known inhibitors. The model picked up 42 of these, giving an enrichment factor of 17. The validated model was used to successfully screen an in-house database of about 500,000 compounds. Subsequent screening with other filters gave 186 hit molecules.     

Free-energy analysis of the molecular binding into lipid membrane with the method of energy representation

A statistical-mechanical treatment of the molecular binding into lipid membrane is presented in combination with molecular simulation. The membrane solution is viewed as an inhomogeneous, mixed solvent system, and the free energy of solvation of a solute in membrane is computed with a realistic set of potential functions by the method of energy representation. Carbon monoxide, carbon dioxide, benzene, and ethylbenzene are adopted as model solutes to analyze the binding into 1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine (DMPC) membrane. It is shown that the membrane inside is more favorable than bulk water and that the solute distribution is diffuse throughout the membrane inside. The membrane-water partition coefficient is then constructed with the help of the Kirkwood-Buff theory from the solvation free energy obtained separately in the hydrophobic, glycerol, headgroup, and aqueous regions. To discuss the role of repulsive and attractive interactions, the solvation free energy is partitioned into the DMPC and water contributions and the effect of water to stabilize the benzene and ethylbenzene solutes within the membrane is pointed out.     

Virtual screening studies to design potent CDK2-cyclin A inhibitors

The cell division cycle is controlled by cyclin-dependent kinases (CDK), which consist of a catalytic subunit (CDK1-CDK8) and a regulatory subunit (cyclin A-H). Pharmacophore analysis indicates that the best inhibitor model consists of (1) two hydrogen bond acceptors, (2) one hydrogen bond donor, and (3) one hydrophobic feature. The HypoRefine pharmacophore model gave an enrichment factor of 1.31 and goodness of fit score of 0.76. Docking studies were carried out to explore the structural requirements for the CDK2-cyclin A inhibitors and to construct highly predictive models for the design of new inhibitors. Docking studies demonstrate the important role of hydrogen bond and hydrophobic interactions in determining the inhibitor-receptor binding affinity. The validated pharmacophore model is further used for retrieving the most active hits/lead from a virtual library of molecules. Subsequently, docking studies were performed on the hits, and novel series of potent leads were suggested based on the interaction energy between CDK2-cyclin A and the putative inhibitors.