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（英文）

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1ns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.     

PTP1B和1,2-萘醌类抑制剂的分子动力学模拟

采用分子动力学和分子力学相结合的方法 ,研究了一类 1,2 萘醌类抑制剂与酪氨酸蛋白磷酸酯酶PTP1B之间的相互作用模式 .计算得到的抑制剂和靶酶之间的相互作用模式显示范德华相互作用、疏水相互作用以及氢键作用是主要的作用模式 .计算结果还表明抑制剂和PTP1B的相互作用能ΔE越低 ,抑制剂活性越高 .通过计算各种能量对ΔE的贡献 ,以及对复合物结构参数的分析 ,发现抑制剂和受体之间疏水相互作用是造成抑制剂活性差别的主要原因 .这为设计其他非酸类抑制剂提供了信息     

PTPlB和1，2-萘醌类抑制剂的分子动力学模拟

采用分子动力学和分子力学相结合的方法，研究了一类1，2-萘醌类抑制剂与酪氨酸蛋白磷酸酯酶PTP1B之间的相互作用模式．计算得到的抑制剂和靶酶之间的相互作用模式显示范德华相互作用、疏水相互作用以及氢键作用是主要的作用模式．计算结果还表明抑制剂和PTP1B的相互作用能△E越低，抑制剂活性越高．通过计算各种能量对△E的贡献，以及对复合物结构参数的分析，发现抑制剂和受体之间疏水相互作用是造成抑制剂活性差别的主要原因．这为设计其他非酸类抑制剂提供了信息．     

葡萄糖苷酶抑制剂作用机理的分子动力学模拟和自由能计算

含有锍离子的葡萄糖苷酶抑制剂如kotalanol (SK)和它除去磺酸基团后的衍生物(DSK), 是潜在的毒副作用较小的治疗II 型糖尿病的候选药物. α-葡萄糖苷酶抑制活性实验显示, DSK活性比SK略高, 而将二者环上的S原子替换成NH后(分别称为DSN和SN), DSN的活性要比SN高1500倍左右. 本文用分子动力学模拟, 结合自由能计算和自由能分解的方法对上述四个抑制剂的作用机理进行了研究. 研究结果表明活性的巨大差异是由NH基团取代效应和磺酸基团立体效应共同作用的结果, 由于N―C键长比S―C键长短, NH基团取代导致烷基链的翻转, 同时, 磺酸基团限制了链的翻转, 因此改变了抑制剂的结合模式. 计算结果与实验基本一致.本文的研究结果有助于进一步理解含锍离子的葡萄糖苷酶抑制剂的结合机理, 并为设计更有潜力的葡萄糖苷酶抑制剂提供了有价值的信息.     

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.     

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.     

新型含咔唑环酰腙衍生物的合成及Cdc25B/PTP1B抑制活性评价

以咔唑和4-氰基氯化苄为初始原料,经多步反应合成出了一系列新型含咔唑基团的酰腙衍生物6,并利用IR、1H NMR、13CNMR和元素分析对其进行了结构表征.对目标化合物进行了Cdc25B/PTP1B抑制活性评价,结果显示,目标化合物6对Cdc25B/PTP1B均具有较高的抑制活性,其中4-[(咔唑-9-基)甲基]-N'-(2-羟基-1-萘亚甲基)苯甲酰肼(6g)对Cdc25B和PTP1B的抑制活性最高, IC50值分别为(2.16±0.38)和(1.06±0.23)?g/mL.对化合物6g进行分子对接的研究结果表明, 6g能与Cdc25B/PTP1B酶形成稳定的复合物,形成氢键和疏水等相互作用.     

HIV-1整合酶与芳香二酮酸类抑制剂相互作用的分子模拟研究

用分子对接方法研究了一系列芳香二酮酸类抑制剂与HIV-1整合酶的识别及相互作用. 结果表明, 抑制剂结合到整合酶Asp64～Leu68, Thr115～Phe121, Gln148～Lys159和Mg2＋所构成的口袋区, 抑制机理与5CITEP相似. 采用分子动力学模拟和MM/PBSA方法计算了芳香二酮酸类抑制剂与整合酶之间的结合自由能, 计算结果与实验值相吻合, 平均绝对偏差为3.6 kJ/mol, 体系范德华相互作用和溶剂化效应的非极性项是利于形成复合物的主要因素. 相关性分析结果表明, 结合自由能值与疏水相互作用有较强的线性相关(R＝0.61), 基于此, 用多元线性回归方法给出了一个能较强预测芳香二酮酸类抑制剂与HIV-1整合酶的结合自由能预测模型, 为后续基于抑制剂结构的抗HIV-1药物分子设计提供指导.     

表皮生长因子受体和抑制剂之间分子对接的研究

研究了表皮生长因子受体(EGFR)和4-苯胺喹唑啉类抑制剂之间的相互作用模式，表皮生长因子受体的三维结构通过同源蛋白模建的方法得到，而抑制剂和靶酶结合复合物结构则通过分子力学和分子动力学结合的方法计算得到。从模拟结果得到的抑制剂和靶酶之间的相互作用模式表明范德华相互作用、疏水相互作用以及氢键相互作用对抑制剂的活性都有重要的影响，抑制剂的苯胺部分位于活性口袋的底部，能够与受体残基的非极性侧链产生很强的范德华和疏水相互作用，抑制剂双环上的取代基团也能和活性口袋外部的部分残基形成一定的范德华和疏水性相互作用，而抑制剂喹唑啉环上的氮原子能和周围的残基形成较强的氢键相互作用，对抑制剂的活性有较大的影响，计算得到抑制剂和靶酶之间的非键相互作用能以及抑制剂和靶酶之间的相互作用信息能够很好地解释抑制剂活性和结构的关系，为全新抑制剂的设计提供了重要的结构信息。     

EGFR和4-苯胺喹唑啉类抑制剂之间相互作用模式的研究

采用分子动力学和MM/PBSA相结合的方法预测了表皮生长因子受体和4-苯胺喹 啉类抑制剂的相互作用模式。在分子动力学采样的基础上,采用MM/PBSA的方法分 别预测了四种可能结合模式下表皮生长因子受体和4-苯胺喹唑啉类抑制剂间的结合 自由能。在MM/PBSA计算中,受体和抑制剂之间的非键相互作用能采用分子力学 (MM)的方法得到;溶剂效应中极性部分对自由能的贡献通过解Possion- Boltzmanne (PB)方程的方法得到;溶液效应中非极性部分对自由能的贡献则通过 分子表面积计算(SA)的方法得到。计算表明,在四种结合模式下,表皮生长因子受 体和4-苯胺喹唑啉类抑制剂之间的结合自由能有较大的差别。在最佳的相互作用模 式中,抑制剂的苯胺部分位于活性口袋的底部,能够与受体残基的非极性侧链产生 很强的范德华和疏水相互作用。抑制剂喹唑啉环上的N(1)原子能够和Met-769上的 NH形成稳定的氢键,而抑制剂上的N(3)原子则和周围的一个水分子形成氢键。同时 ,抑制剂双环上的取代基团也能和活性口袋外部的部分残基形成一定的范德华和疏 水相互作用。最佳结合模式能够很好地解释已有抑制剂结构和活性间的关系。     

Structural and energetic insight into the interactions between the benzolactam inhibitors and tumor marker HSP90α

The heat shock protein 90α (HSP90α) provides a promising molecular target for cancer therapy. A series of novel benzolactam inhibitors exhibited distinct inhibitory activity for HSP90α. However, the structural basis for the impact of distinct R₁ substituent groups of nine benzolactam inhibitors on HSP90α binding affinities remains unknown. In this study, we carried out molecular docking, molecular dynamics (MD) simulations, and molecular mechanics and generalized Born/surface area (MM–GBSA) binding free energy calculations to address the differences. Molecular docking studies indicated that all nine compounds presented one conformation in the ATP-binding site of HSP90α N-terminal domain. MD simulations and subsequent MM–GBSA calculations revealed that the hydrophobic interactions between all compounds and HSP90α contributed the most to the binding affinity and a good linear correlation was obtained between the calculated and the experimental binding free energies (R = 0.88). The per residue decomposition revealed that the most remarkable differences of residue contributions were found in the residues Ala55, Ile96, and Leu107 defining a hydrophobic pocket for the R₁ group, consistent with the analysis of binding modes. This study may be helpful for the future design of novel HSP90α inhibitors.     

Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.     

Molecular dynamics simulations reveal the allosteric effect of F1174C resistance mutation to ceritinib in ALK-associated lung cancer

Anaplastic lymphoma kinase (ALK) has become as an important target for the treatment of various human cancers, especially non-small-cell lung cancer. A mutation, F1174C, suited in the C-terminal helix αC of ALK and distal from the small-molecule inhibitor ceritinib bound to the ATP-binding site, causes the emergence of drug resistance to ceritinib. However, the detailed mechanism for the allosteric effect of F1174C resistance mutation to ceritinib remains unclear. Here, molecular dynamics (MD) simulations and binding free energy calculations [Molecular Mechanics/Generalized Born Surface Area (MM/GBSA)] were carried out to explore the advent of drug resistance mutation in ALK. MD simulations observed that the exquisite aromatic-aromatic network formed by residues F1098, F1174, F1245, and F1271 in the wild-type ALK-ceritinib complex was disrupted by the F1174C mutation. The resulting mutation allosterically affected the conformational dynamic of P-loop and caused the upward movement of the P-loop from the ATP-binding site, thereby weakening the interaction between ceritinib and the P-loop. The subsequent MM/GBSA binding free energy calculations and decomposition analysis of binding free energy validated this prediction. This study provides mechanistic insight into the allosteric effect of F1174C resistance mutation to ceritinib in ALK and is expected to contribute to design the next-generation of ALK inhibitors.     

Crystal molecular dynamics simulations to speed up MM/PB(GB)SA evaluation of binding free energies of di-mannose deoxy analogs with P51G-m4-Cyanovirin-N

Complexes of two Cyanovirin-N (CVN) mutants, m4-CVN and P51G-m4-CVN, with deoxy di-mannose analogs were employed as models to generate conformational ensembles using explicit water Molecular Dynamics (MD) simulations in solution and in crystal environment. The results were utilized for evaluation of binding free energies with the molecular mechanics Poisson-Boltzmann (or Generalized Born) surface area, MM/PB(GB)SA, methods. The calculations provided the ranking of deoxy di-mannose ligands affinity in agreement with available qualitative experimental evidences. This confirms the importance of the hydrogen-bond network between di-mannose 3'- and 4'-hydroxyl groups and the protein binding site B(M) as a basis of the CVN activity as an effective HIV fusion inhibitor. Comparison of binding free energies averaged over snapshots from the solution and crystal simulations showed high promises in the use of the crystal matrix for acceleration of the conformational ensemble generation, the most time consuming step in MM/PB(GB)SA approach. Correlation between energy values based on solution versus crystal ensembles is 0.95 for both MM/PBSA and MM/GBSA methods.     

Studies of the mechanism of selectivity of protein tyrosine phosphatase 1B (PTP1B) bidentate inhibitors using molecular dynamics simulations and free energy calculations

Bidentate inhibitors of protein tyrosine phosphatase 1B (PTP1B) are considered as a group of ideal inhibitors with high binding potential and high selectivity in treating type II diabetes. In this paper, the binding models of five bidentate inhibitors to PTP1B, TCPTP, and SHP-2 were investigated and compared by using molecular dynamics (MD) simulations and free energy calculations. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complexes are well consistent with the experimental data. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition analysis indicates that the residues ARG24, ARG254, and GLN262 in the second binding site of PTP1B are essential for the high selectivity of inhibitors. Furthermore, the residue PHE182 close to the active site is also important for the selectivity and the binding affinity of the inhibitors. According to our analysis, it can be concluded that in most cases the polarity of the portion of the inhibitor that binds to the second binding site of the protein is positive to the affinity of the inhibitors while negative to the selectivity of the inhibitors. We expect that the information we obtained here can help to develop potential PTP1B inhibitors with more promising specificity.     

Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM‐PBSA and MM‐GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein‐ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy‐minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein‐ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM‐PBSA or MM‐GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations

The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2, or 4) on the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1) MD simulation length has an obvious impact on the predictions, and longer MD simulation is not always necessary to achieve better predictions. (2) The predictions are quite sensitive to the solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface. (3) Conformational entropy often show large fluctuations in MD trajectories, and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.     

Calculations of the free energy of interaction of the c-Fos-c-Jun coiled coil: effects of the solvation model and the inclusion of polarization effects

The leucine zipper region of activator protein-1 (AP-1) comprises the c-Jun and c-Fos proteins and constitutes a well-known coiled coil protein-protein interaction motif. We have used molecular dynamics (MD) simulations in conjunction with the molecular mechanics/Poisson-Boltzmann generalized-Born surface area [MM/PB(GB)SA] methods to predict the free energy of interaction of these proteins. In particular, the influence of the choice of solvation model, protein force field, and water potential on the stability and dynamic properties of the c-Fos-c-Jun complex were investigated. Use of the AMBER polarizable force field ff02 in combination with the polarizable POL3 water potential was found to result in increased stability of the c-Fos-c-Jun complex. MM/PB(GB)SA calculations revealed that MD simulations using the POL3 water potential give the lowest predicted free energies of interaction compared to other nonpolarizable water potentials. In addition, the calculated absolute free energy of binding was predicted to be closest to the experimental value using the MM/GBSA method with independent MD simulation trajectories using the POL3 water potential and the polarizable ff02 force field, while all other binding affinities were overestimated.     

Combining molecular docking and molecular dynamics studies for modelling Staphylococcus aureus MurD inhibitory activity

The ATP-dependent bacterial MurD enzyme catalyses the formation of the peptide bond between cytoplasmic intermediate UDP-N-acetylmuramoyl-L-alanine and D-glutamic acid. This is essential for bacterial cell wall peptidoglycan synthesis in both Gram-positive and Gram-negative bacteria. MurD is recognized as an important target for the development of new antibacterial agents. In the present study we prepared the 3D-stucture of the catalytic pocket of the Staphylococcus aureus MurD enzyme by homology modelling. Extra-precision docking, binding free energy calculation by the MM–GBSA approach and a 40 ns molecular dynamics (MD) simulation of 2-thioxothiazolidin-4-one based inhibitor $1 was carried out to elucidate its inhibition potential for the S. aureus MurD enzyme. Molecular docking results showed that Lys19, Gly147, Tyr148, Lys328, Thr330 and Phe431 residues are responsible for the inhibitor–protein complex stabilization. Binding free energy calculation revealed electrostatic solvation and van der Waals energy components as major contributors for the inhibitor binding. The inhibitor-modelled S. aureus protein complex had a stable conformation in response to the atomic flexibility and interaction, when subjected to MD simulation at 40 ns in aqueous solution. We designed some molecules as potent inhibitors of S. aureus MurD, and to validate the stability of the designed molecule D1-modelled protein complex we performed a 20 ns MD simulation. Results obtained from this study can be utilized for the design of potent S. aureus MurD inhibitors.