雪卡毒素毒性机理的分子对接及分子动力学研究

采用分子对接和分子动力学方法,研究了雪卡毒素与其毒性作用靶点之一钠通道的结合模式,并与钠通道阻滞剂奎尼丁比较.研究结果表明,雪卡毒素、奎尼丁与钠通道作用方式有所不同.分子动力学模拟表明,对接受体-配体复合物体系在2.5 ns的模拟过程中稳定.奎尼丁在钠通道中央与GLU1784,THR1858各形成1个稳定氢键,吡啶环与...     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Protein ligand docking based on empirical method for binding affinity estimation

An empirical protein-ligand binding affinity estimation method, SCORE, was incorporated into a popular docking program, DOCK4. The combined program, ScoreDock, was used to reconstruct the 200 protein-ligand complex structures and found to give good results for the complexes with high binding affinities. A quality assessment method for docking results from ScoreDock was developed based on the whole test set and tested by additionally selected complexes. The method significantly improves the docking accuracy and was shown to be reliable in docking quality assessment. As a docking tool in structural based drug design, ScoreDock can screen out final hits directly based on the predicted negative logarithms of dissociation equilibrium constants of protein-ligand complexes, and can explicitly deal with structure water molecules, as well as metal atoms.     

Unsupervised guided docking of covalently bound ligands

Summary An approach for docking covalently bound ligands in protein enzymes or receptors was implemented in MacDOCK, a similarity-driven docking program based on DOCK 4.0. This approach was tested with a small number of covalent ligand–protein structures, using both native and non-native protein structures. In all cases, MacDOCK was able to generate orientations consistent with the known covalent binding mode of these complexes, with a performance similar to that of other docking programs. This method was also applied to search for known covalent thrombin inhibitors in a medium-sized molecular database (ca. 11,000 compounds). Detection of functional groups suitable for covalent docking was carried out automatically. A significant enrichment in known active molecules in the first 5% of the database was obtained, showing that MacDOCK can be used efficiently for the virtual screening of covalently bound ligands.     

Role of bridging water molecules in GSK3β-inhibitor complexes: insights from QM/MM, MD, and molecular docking studies

The role of water molecules is increasingly gaining interest in drug design, and several studies have highlighted their paramount contributions to the specificity and the affinity of ligand binding. In this study, we employ the two-layer ONIOM-based quantum mechanics/molecular mechanics (QM/MM) calculations, molecular dynamics (MD) simulations, and molecular docking studies to investigate the effect of bridging water molecules at the GSK3β-inhibitors interfaces. The results obtained from the ONIOM geometry optimization and AIM analysis corroborated the presence of bridging water molecules that form hydrogen bonds with protein side chain of Thr138 and/or backbone of Gln185, and mediate interactions with inhibitors in the 10 selected GSK3β-inhibitor complexes. Subsequently, MD simulations carried out on a representative system of 1R0E demonstrated that the bridging water molecule is stable at the GSK3β-inhibitor interface and appears to contribute to the stability of the protein-inhibitor interactions. Furthermore, molecular docking studies of GSK3β-inhibitor complexes indicated that the inhibitors can increase binding affinities and the better docked conformation of inhibitors can be obtained by inclusion of the bridging water molecules, especially for the flexible inhibitors, in docking experiments into individual protein conformations. Our results elucidate the importance of bridging water molecules at the GSK3β-inhibitor interfaces and suggest that they might prove useful in rational drug design.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟, 将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接, 形成非特异性激动和特异性激动的受体-配体复合物. 用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰胆碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns. 将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分, 以top5%富集因子(EF)作为评价依据, 用占诺美林优化后的M1受体模型的EF为8.0, 用乙酰胆碱优化后M1受体模型的EF为6.5, 非复合物的EF为1.5. 说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理, 可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选, 为找到新的选择性M1受体激动剂奠定了基础.     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

Flexible docking under pharmacophore type constraints

FlexX-Pharm, an extended version of the flexible docking tool FlexX, allows the incorporation of information about important characteristics of protein-ligand binding modes into a docking calculation. This information is introduced as a simple set of constraints derived from receptor-based type pharmacophore features.The constraints are determined by selected FlexX interactions and inclusion volumes in the receptor active site. They guide the docking process to produce a set of docking solutions with particular properties. By applying a series of look-ahead checks during the flexible construction of ligand fragments within the active site, FlexX-Pharm determines which partially built docking solutions can potentially obey the constraints. Solutions that will not obey the constraints are deleted as early as possible, often decreasing the calculation time and enabling new docking solutions to emerge. FlexX-Pharm was evaluated on various individual protein-ligand complexes where the top docking solutions generated by FlexX had high root mean square deviations (RMSD) from the experimentally observed binding modes. FlexX-Pharm showed an improvement in the RMSD of the top solutions in most cases, along with a reduction in run time. We also tested FlexX-Pharm as a database screening tool on a small dataset of molecules for three target proteins. In two cases, FlexX-Pharm missed one or two of the active molecules due to the constraints selected. However, in general FlexX-Pharm maintained or improved the enrichment shown with FlexX, while completing the screen in considerably less run time.     

Synthesis theoretical and experimental spectroscopic characterization of isoniazid-benzoic acid drug complexes: Insilico and invitro biological evaluations

New organic charge-transfer molecules were synthesised by salt formation from isoniazid and benzoic acid/aspirin compounds acting as acceptor and donor molecules. The synthesised charge transfer complexes were characterized and structurally confirmed by various instrumental techniques such as UV–visible, FT-IR, powder XRD, and NMR spectroscopic methods. Initially, compounds are studied molecular docking analysis with different kinds of proteins, such as 1HNY.pdb, 1PGG.pdb and 4-COX.pdb. Docking results have been compared with molecular electrostatic potential mapping and Mulliken charge distribution methods. Results show that both complexes IAC and IBC have almost the same binding constant value with 1HNY.pdb. Besides, IBC has a more binding constant than the IAC with inflammatory proteins (1PGG.pdb and 4-COX.pdb). The reactivity of the complexes is explained by the chemical potential and electrophilic index derived by the frontier molecular orbitals using the DFT method. These results show a more electrophilic index of IBC than the IAC indicating, more electron affinity nature of IBC. This is also reflected in the in-vitro biological studies, which shows IAC having better activity in anti-diabetic studies whereas IBC has better activity in anti-inflammatory studies. For the sake of complex ability, all biological and molecular docking experimental results are compared with standard drug molecules.     

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function,efficient optimization,and multithreading

AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed‐up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔG_bind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.     

Cε3-Cε4蛋白寡核苷酸适配子结合位点的预测与筛选

采用NPDock程序对Cε3-Cε4蛋白与其核酸适配子A1的结合位点进行了预测与筛选, 筛选出A1与Cε3-Cε4蛋白结合的关键位点. 同时, 根据蛋白与DNA片段复合物结合界面中氨基酸残基和碱基统计分析发现, 结合界面氨基酸富集碱基G能力最强, 富集碱基T和C能力次之. 本文建立了以NPDock程序虚拟对接为基础的高效适配子优化方法, 为相关研究提供了实验参考.     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点.本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团.药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.937...     

An anchor-dependent molecular docking process for docking small flexible molecules into rigid protein receptors

A molecular docking method designated as ADDock, anchor- dependent molecular docking process for docking small flexible molecules into rigid protein receptors, is presented in this article. ADDock makes the bond connection lists for atoms based on anchors chosen for building molecular structures for docking small flexible molecules or ligands into rigid active sites of protein receptors. ADDock employs an extended version of piecewise linear potential for scoring the docked structures. Since no translational motion for small molecules is implemented during the docking process, ADDock searches the best docking result by systematically changing the anchors chosen, which are usually the single-edge connected nodes or terminal hydrogen atoms of ligands. ADDock takes intact ligand structures generated during the docking process for computing the docked scores; therefore, no energy minimization is required in the evaluation phase of docking. The docking accuracy by ADDock for 92 receptor-ligand complexes docked is 91.3%. All these complexes have been docked by other groups using other docking methods. The receptor-ligand steric interaction energies computed by ADDock for some sets of active and inactive compounds selected and docked into the same receptor active sites are apparently separated. These results show that based on the steric interaction energies computed between the docked structures and receptor active sites, ADDock is able to separate active from inactive compounds for both being docked into the same receptor.     

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.     

DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases

In this paper we describe the search strategies developed for docking flexible molecules to macomolecular sites that are incorporated into the widely distributed DOCK software, version 4.0. The search strategies include incremental construction and random conformation search and utilize the existing Coulombic and Lennard-Jones grid-based scoring function. The incremental construction strategy is tested with a panel of 15 crystallographic testcases, created from 12 unique complexes whose ligands vary in size and flexibility. For all testcases, at least one docked position is generated within 2 Å of the crystallographic position. For 7 of 15 testcases, the top scoring position is also within 2 Å of the crystallographic position. The algorithm is fast enough to successfully dock a few testcases within seconds and most within 100 s. The incremental construction and the random search strategy are evaluated as database docking techniques with a database of 51 molecules docked to two of the crystallographic testcases. Incremental construction outperforms random search and is fast enough to reliably rank the database of compounds within 15 s per molecule on an SGI R10000 cpu.     

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (M^pro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine–M^pro and somniferine–RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.     

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.