谷氨酰胺结合蛋白的分子动力学模拟和自由能计算

谷氨酰胺结合蛋白(Glutamine-binding protein, GlnBp)是大肠杆菌透性酶系统中一个细胞外液底物专一性结合蛋白, 对于细胞外液中谷氨酰胺(Gln)的运输和传递至关重要. 本文运用分子动力学(Molecular dynamics, MD)模拟采样, 考察了GlnBp关键残基与底物Gln之间的相互作用和GlnBp两条铰链的功能差别; 并采用MM-PBSA方法计算了GlnBp与底物Gln的结合自由能. 结果表明: Ph13, Phe50, Thr118和Ile69与底物Gln的范德华相互作用和Arg75, Thr70, Asp157, Gly68, Lys115, Ala67, His156与底物Gln的静电相互作用是结合Gln的主要推动力; 复合物的铰链区85～89柔性大, 对构象开合提供了结构基础; 而铰链区181～185柔性小, 其作用更多是在功能上把底物Gln限制在口袋中; 自由能预测值与实验值吻合. 本研究很好地解释了GlnBp结构与功能的关系, 为进一步了解GlnBp的开合及转运Gln的机制提供了重要的结构信息.     

An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor

The binding free energy calculation of protein–ligand complexes is necessary for research into virus–host interactions and the relevant applications in drug discovery. However, many current computational methods of such calculations are either inefficient or inaccurate in practice. Utilizing implicit solvent models in the molecular mechanics generalized Born surface area (MM/GBSA) framework allows for efficient calculations without significant loss of accuracy. Here, GBNSR6, a new flavor of the generalized Born model, is employed in the MM/GBSA framework for measuring the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. A computational protocol is developed based on the widely studied Ras–Raf complex, which has similar binding free energy to SARS-CoV-2/ACE2. Two options for representing the dielectric boundary of the complexes are evaluated: one based on the standard Bondi radii and the other based on a newly developed set of atomic radii (OPT1), optimized specifically for protein–ligand binding. Predictions based on the two radii sets provide upper and lower bounds on the experimental references: −14.7(ΔGbindBondi)<−10.6(ΔGbindExp.)<−4.1(ΔGbindOPT1) kcal/mol. The consensus estimates of the two bounds show quantitative agreement with the experiment values. This work also presents a novel truncation method and computational strategies for efficient entropy calculations with normal mode analysis. Interestingly, it is observed that a significant decrease in the number of snapshots does not affect the accuracy of entropy calculation, while it does lower computation time appreciably. The proposed MM/GBSA protocol can be used to study the binding mechanism of new variants of SARS-CoV-2, as well as other relevant structures.     

评估石杉碱甲杂合体衍生物作为AD症药物活性顺序的简单方法

为了合理化设计、指导具有高活性、低毒性的石杉碱甲杂合体(Huprine X)衍生物的合成, 建立了一个计算结合自由能的新方案, 以实现对该类衍生物活性排列顺序的预测. 该结合自由能由三部分组成: 抑制剂和靶酶的相互作用能; 活性位点残基在结合前后的构象绝对自由能的变化; 抑制剂从稳定构象变为活性构象的自由能增加值. 通过计算已合成的14个杂合体衍生物的结合自由能, 结果显示理论值和实验测定的生物活性值有很好的等级相关性, 其斯皮尔曼相关系数为0.85, 证明了该方法的可行性.     

Comparison of thermodynamic integration and Bennett acceptance ratio for calculating relative protein‐ligand binding free energies

The performances of Bennett's acceptance ratio method and thermodynamic integration (TI) for the calculation of free energy differences in protein simulations are compared. For the latter, the standard trapezoidal rule, Simpson's rule, and Clenshaw‐Curtis integration are used as numerical integration methods. We evaluate the influence of the number and definition of intermediate states on the precision, accuracy, and efficiency of the free energy calculations. Our results show that non‐equidistantly spaced intermediate states are in some cases beneficial for the TI methods. Using several combinations of softness parameters and the λ power dependence, it is shown that these benefits are strongly dependent on the shape of the integrand. Although TI is more user‐friendly due to its simplicity, it was found that Bennett's acceptance ratio method is the more efficient method. It is also the least dependent on the choice of the intermediate states, making it more robust than TI. © 2013 Wiley Periodicals, Inc.     

Oversampling Free Energy Perturbation Simulation in Determination of the Ligand-Binding Free Energy

Determination of the ligand-binding affinity is an extremely interesting problem. Normally, the free energy perturbation (FEP) method provides an appropriate result. However, it is of great interest to improve the accuracy and precision of this method. In this context, temperature replica exchange molecular dynamics implementation of the FEP computational approach, which we call replica exchange free energy perturbation (REP) was proposed. In particular, during REP simulations, the system can easily escape from being trapped in local minima by exchanging configurations with high temperatures, resulting in significant improvement in the accuracy and precision of protein–ligand binding affinity calculations. The distribution of the decoupling free energy was enlarged, and its mean values were decreased. This results in changes in the magnitude of the calculated binding free energies as well as in alteration in the binding mechanism. Moreover, the REP correlation coefficient with respect to experiment ( R^REP = 0.85 ± 0.15 ) is significantly boosted in comparison with the FEP one ( R^FEP = 0.64 ± 0.30 ). Furthermore, the root-mean-square error (RMSE) of REP is also smaller than FEP, RMSE^REP = 4.28 ± 0.69 versus RMSE^FEP = 5.80 ± 1.11 kcal/mol, respectively. © 2019 Wiley Periodicals, Inc.     

In Silico Study towards Repositioning of FDA-Approved Drug Candidates for Anticoronaviral Therapy: Molecular Docking,Molecular Dynamics and Binding Free Energy Calculations

The SARS-CoV-2 targets were evaluated for a set of FDA-approved drugs using a combination of drug repositioning and rigorous computational modeling methodologies such as molecular docking and molecular dynamics (MD) simulations followed by binding free energy calculations. Six FDA-approved drugs including, Ouabain, Digitoxin, Digoxin, Proscillaridin, Salinomycin and Niclosamide with promising anti-SARS-CoV-2 activity were screened in silico against four SARS-CoV-2 proteins—papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), SARS-CoV-2 main protease (Mpro), and adaptor-associated kinase 1 (AAK1)—in an attempt to define their promising targets. The applied computational techniques suggest that all the tested drugs exhibited excellent binding patterns with higher scores and stable complexes compared to the native protein cocrystallized inhibitors. Ouabain was suggested to act as a dual inhibitor for both PLpro and Mpro enzymes, while Digitoxin bonded perfectly to RdRp. In addition, Salinomycin targeted PLpro. Particularly, Niclosamide was found to target AAK1 with greater affinity compared to the reference drug. Our study provides comprehensive molecular-level insights for identifying or designing novel anti-COVID-19 drugs.     

Inhibition Mechanism of Hydroxyproline-like Small Inhibitors to Disorder HIF-VHL Interaction by Molecular Dynamic Simulations and Binding Free Energy Calculations

Protein-protein interactions are vital for a wide range of biological processes. The interactions between the hypoxia-inducible factor and von Hippel Lindau (VHL) are attractive drug targets for ischemic heart disease. In order to disrupt this interaction, the strategy to target VHL binding site using a hydroxyproline-like (pro-like) small molecule has been reported. In this study, we focused on the inhibition mechanism between the pro-like inhibitors and the VHL protein, which were investigated via molecular dynamics simulations and binding free energy calculations. It was found that pro-like inhibitors showed a strong binding affinity toward VHL. Binding free energy calculations and free energy decompositions suggested that the modification of various regions of pro-like inhibitors may provide useful information for future drug design.     

HIV-1蛋白酶与抑制剂作用的结合自由能计算

HIV-1蛋白酶是治疗艾滋病的重要靶标酶之一. 采用分子动力学模拟, 运用MM-PBSA方法计算了HIV-1蛋白酶与三个抑制剂BE4, BE5和BE6的结合自由能, 结果表明抑制剂P₁/ 位置的苄基上双氟原子的不同位置对结合自由能产生不同的影响. 通过能量分解的方法考察了HIV-1蛋白酶的主要残基与三个抑制剂间的相互作用与识别, 结果表明三个抑制剂以相同的作用模式与HIV-1蛋白酶结合, 计算结果与实验结果基本吻合.     

新型二氟甲基磷酸类酪氨酸蛋白磷酸酯酶1B抑制剂的分子动力学模拟和结合自由能计算

通过分子对接建立了一系列含二氟甲基磷酸基团(DFMP)或二氟甲基硫酸基团(DFMS)的抑制剂与酪氨酸蛋白磷酸酯酶1B(PTP1B)的相互作用模式, 并通过1 ns的分子动力学模拟和molecular mechanics/generalized Born surface area (MM/GBSA)方法计算了其结合自由能. 计算获得的结合自由能排序和抑制剂与靶酶间结合能力排序一致; 通过基于主方程的自由能计算方法, 获得了抑制剂与靶酶残基间相互作用的信息, 这些信息显示DFMP/DFMS基团的负电荷中心与PTP1B的221位精氨酸正电荷中心之间的静电相互作用强弱决定了此类抑制剂的活性, 进一步的分析还显示位于DFMP/DFMS基团中的氟原子或其他具有适当原子半径的氢键供体原子会增进此类抑制剂与PTP1B活性位点的结合能力.     

Binding affinity prediction with different force fields: examination of the linear interaction energy method

A systematic study of the linear interaction energy (LIE) method and the possible dependence of its parameterization on the force field and system (receptor binding site) is reported. We have calculated the binding free energy for nine different ligands in complex with P450cam using three different force fields (Amber95, Gromos87, and OPLS-AA). The results from these LIE calculations using our earlier parameterization give relative free energies of binding that agree remarkably well with the experimental data. However, the absolute energies are too positive for all three force fields, and it is clear that an additional constant term (gamma) is required in this case. Out of five examined LIE models, the same one emerges as the best for all three force fields, and this, in fact, corresponds to our earlier one apart from the addition of the constant gamma, which is almost identical for the three force fields. Thus, the present free energy calculations clearly indicate that the coefficients of the LIE method are independent of the force field used. Their relation to solvation free energies is also demonstrated. The only free parameter of the best model is gamma, which is found to depend on the hydrophobicity of the binding site. We also attempt to quantify the binding site hydrophobicity of four different proteins which shows that the ordering of gamma's for these sites reflects the fraction of hydrophobic surface area.     

Structural Insight into the Binding of Cyanovirin-N with the Spike Glycoprotein,Mpro and PLpro of SARS-CoV-2: Protein–Protein Interactions,Dynamics Simulations and Free Energy Calculations

The emergence of COVID-19 continues to pose severe threats to global public health. The pandemic has infected over 171 million people and claimed more than 3.5 million lives to date. We investigated the binding potential of antiviral cyanobacterial proteins including cyanovirin-N, scytovirin and phycocyanin with fundamental proteins involved in attachment and replication of SARS-CoV-2. Cyanovirin-N displayed the highest binding energy scores (−16.8 ± 0.02 kcal/mol, −12.3 ± 0.03 kcal/mol and −13.4 ± 0.02 kcal/mol, respectively) with the spike protein, the main protease (M^pro) and the papainlike protease (PL^pro) of SARS-CoV-2. Cyanovirin-N was observed to interact with the crucial residues involved in the attachment of the human ACE2 receptor. Analysis of the binding affinities calculated employing the molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA) approach revealed that all forms of energy, except the polar solvation energy, favourably contributed to the interactions of cyanovirin-N with the viral proteins. With particular emphasis on cyanovirin-N, the current work presents evidence for the potential inhibition of SARS-CoV-2 by cyanobacterial proteins, and offers the opportunity for in vitro and in vivo experiments to deploy the cyanobacterial proteins as valuable therapeutics against COVID-19.     

Docking,Binding Free Energy Calculations and In Vitro Characterization of Pyrazine Linked 2-Aminobenzamides as Novel Class I Histone Deacetylase (HDAC) Inhibitors

Class I histone deacetylases, HDAC1, HDAC2, and HDAC3, represent potential targets for cancer treatment. However, the development of isoform-selective drugs for these enzymes remains challenging due to their high sequence and structural similarity. In the current study, we applied a computational approach to predict the selectivity profile of developed inhibitors. Molecular docking followed by MD simulation and calculation of binding free energy was performed for a dataset of 2-aminobenzamides comprising 30 previously developed inhibitors. For each HDAC isoform, a significant correlation was found between the binding free energy values and in vitro inhibitory activities. The predictive accuracy and reliability of the best preforming models were assessed on an external test set of newly designed and synthesized inhibitors. The developed binding free-energy models are cost-effective methods and help to reduce the time required to prioritize compounds for further studies.     

Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek

Widely used in global households, fenugreek is well known for its culinary and medicinal uses. The various reported medicinal properties of fenugreek are by virtue of the different natural phytochemicals present in it. Regarded as a promising target, interleukin 2 receptor subunit alpha (IL2Rα) has been shown to influence immune responses. In the present research, using in silico techniques, we have demonstrated the potential IL2Rα binding properties of three polyphenol stilbenes (desoxyrhaponticin, rhaponticin, rhapontigenin) from fenugreek. As the first step, molecular docking was performed to assess the binding potential of the fenugreek phytochemicals with IL2Rα. All three phytochemicals demonstrated interactions with active site residues. To confirm the reliability of our molecular docking results, 100 ns molecular dynamics simulations studies were undertaken. As discerned by the RMSD and RMSF analyses, IL2Rα in complex with the desoxyrhaponticin, rhaponticin, and rhapontigenin indicated stability. The RMSD analysis of the phytochemicals alone also demonstrated no significant structural changes. Based on the stable molecular interactions and comparatively slightly better MM/PBSA binding free energy, rhaponticin seems promising. Additionally, ADMET analysis performed for the stilbenes indicated that all of them obey the ADMET rules. Our computational study thus supports further in vitro IL2Rα binding studies on these stilbenes, especially rhaponticin.     

Hybrid Solvation Model with First Solvation Shell for Calculation of Solvation Free Energy

We present a hybrid solvation model with first solvation shell to calculate solvation free energies. This hybrid model combines the quantum mechanics and molecular mechanics methods with the analytical expression based on the Born solvation model to calculate solvation free energies. Based on calculated free energies of solvation and reaction profiles in gas phase, we set up a unified scheme to predict reaction profiles in solution. The predicted solvation free energies and reaction barriers are compared with experimental results for twenty bimolecular nucleophilic substitution reactions. These comparisons show that our hybrid solvation model can predict reliable solvation free energies and reaction barriers for chemical reactions of small molecules in aqueous solution.     

Deciphering the Selectivity of Bombyx mori Pheromone Binding Protein for Bombykol over Bombykal: A Theoretical Approach

In this article we report calculations dedicated to estimate the selectivity of the Bombyx mori pheromone binding protein towards the two closely related pheromonal components Bombykol and Bombykal. The selectivity is quantified by the binding free‐energy difference, obtained either by the thermodynamic integration or by the MM‐GBSA approach. In the latter, the selectivity is decomposed on a per‐residue basis, which identifies the residues considered crucial for the selectivity of the protein for Bombykol over Bombykal. A discussion on the role of Bombyx mori pheromone binding protein is provided on the basis of these results.     

Binding affinities by alchemical perturbation using QM/MM with a large QM system and polarizable MM model

The most general way to improve the accuracy of binding‐affinity calculations for protein–ligand systems is to use quantum‐mechanical (QM) methods together with rigorous alchemical‐perturbation (AP) methods. We explore this approach by calculating the relative binding free energy of two synthetic disaccharides binding to galectin‐3 at a reasonably high QM level (dispersion‐corrected density functional theory with a triple‐zeta basis set) and with a sufficiently large QM system to include all short‐range interactions with the ligand (744–748 atoms). The rest of the protein is treated as a collection of atomic multipoles (up to quadrupoles) and polarizabilities. Several methods for evaluating the binding free energy from the 3600 QM calculations are investigated in terms of stability and accuracy. In particular, methods using QM calculations only at the endpoints of the transformation are compared with the recently proposed non‐Boltzmann Bennett acceptance ratio (NBB) method that uses QM calculations at several stages of the transformation. Unfortunately, none of the rigorous approaches give sufficient statistical precision. However, a novel approximate method, involving the direct use of QM energies in the Bennett acceptance ratio method, gives similar results as NBB but with better precision, ～3 kJ/mol. The statistical error can be further reduced by performing a greater number of QM calculations. © 2015 Wiley Periodicals, Inc.     

On the Relative Merits of Equilibrium and Non‐Equilibrium Simulations for the Estimation of Free‐Energy Differences

The possibility of estimating equilibrium free‐energy profiles from multiple non‐equilibrium simulations using the fluctuation–dissipation theory or the relation proposed by Jarzynski has attracted much attention. Although the Jarzynski estimator has poor convergence properties for simulations far from equilibrium, corrections have been derived for cases in which the work is Gaussian distributed. Here, we examine the utility of corrections proposed by Gore and collaborators using a simple dissipative system as a test case. The system consists of a single methane‐like particle in explicit water. The Jarzynski equality is used to estimate the change in free energy associated with pulling the methane particle a distance of 3.9 nm at rates ranging from ～0.1 to 100 m s^?1. It is shown that although the corrections proposed by Gore and collaborators have excellent numerical performance, the profiles still converge slowly. Even when the corrections are applied in an ideal case where the work distribution is necessarily Gaussian, performing simulations under quasi‐equilibrium conditions is still most efficient. Furthermore, it is shown that even for a single methane molecule in water, pulling rates as low as 1 m s^?1 can be problematic. The implications of this finding for studies in which small molecules or even large biomolecules are pulled through inhomogeneous environments at similar pulling rates are discussed.     

Meaning and Measurability of Single‐Ion Activities,the Thermodynamic Foundations of pH,and the Gibbs Free Energy for the Transfer of Ions between Dissimilar Materials

Considering the relationship between concentration and vapor pressure (or the relationship between concentration and fugacity) single‐ion activity coefficients are definable in purely thermodynamic terms. The measurement process involves measuring a contact potential between a solution and an external electrode. Contact potentials are measurable by using thermodynamically reversible processes. Extrapolation of an equation to zero concentration and ionic strength enables determination of single‐ion activity coefficients. Single‐ion activities can be defined and measured without using any extra‐thermodynamic assumptions, concepts, or measurements. This method could serve as a gold standard for the validation of extra‐thermodynamic methods for determining single‐ion activities. Furthermore, it places the concept of pH on a thermodynamically solid foundation. Contact potential measurements can also be used to determine the Gibbs free energy for the transfer of ions between dissimilar materials.     

Ultra-Large-Scale Screening of Natural Compounds and Free Energy Calculations Revealed Potential Inhibitors for the Receptor-Binding Domain (RBD) of SARS-CoV-2

The emergence of immune-evading variants of SARS-CoV-2 further aggravated the ongoing pandemic. Despite the deployments of various vaccines, the acquired mutations are capable of escaping both natural and vaccine-induced immune responses. Therefore, further investigation is needed to design a decisive pharmacological treatment that could efficiently block the entry of this virus into cells. Hence, the current study used structure-based methods to target the RBD of the recombinant variant (Deltacron) of SARS-CoV-2, which was used as a model variant. From the virtual drug screenings of various databases, a total of four hits were identified as potential lead molecules. Key residues were blocked by these molecules with favorable structural dynamic features. The binding free energies further validated the potentials of these molecules. The TBE for MNP was calculated to be −32.86 ± 0.10 kcal/mol, for SANC00222 the TBE was −23.41 ± 0.15 kcal/mol, for Liriodenine the TBE was −34.29 ± 0.07 kcal/mol, while for Carviolin the TBE was calculated to be −27.67 ± 0.12 kcal/mol. Moreover, each complex demonstrated distinct internal motion and a free energy profile, indicating a different strategy for the interaction with and inhibition of the RBD. In conclusion, the current study demands further in vivo and in vitro validation for the possible usage of these compounds as potential drugs against SARS-CoV-2 and its variants.     

Comparison of Penta and Tetra-pyridyl Cobalt-based Catalysts for Water Reduction: H2 Production Cycle,Solvent Response and Reduction Free Energy

Understanding water reduction towards H₂ generation is crucial to overcome today's renewable energy obstacles. Previous studies have shown the superior H₂ production performances of Cobalt based penta-pyridyl (CoaPPy) and tetra-pyridyl (CoaTPy) complexes in solution. We investigate H₂ production cycles of CoaPPy and CoaTPy complexes immersed in water solution by means of Ab-initio Molecular Dynamics and Density Functional Theory. We monitor dynamic properties of the systems, solvent response and structural changes occurring in the catalysts, by simulating all intermediate steps of the H₂ production cycle. Reduction free energies and reorganization energies are calculated. Our results show that, following the first electron injection, H₂ production proceeds with the singlet spin state. Following the first electron insertion, we observe a significant rearrangement of the hydrogen bonding network in the first solvation shell. The cobalt center turns out to be more accessible for the surrounding water molecules in the case of CoaTPy at all the intermediate steps, which explains its higher catalytic performance over CoaPPy. Following the first reduction reaction, a larger gain in reduction free energy is estimated for CoaTPy with respect to CoaPPy, with a difference of 0.14 eV, in line with the experiments. For the second reduction, instead, CoaPPy shows more negative reduction potential, by 0.41 eV.