基于结构的小分子对接软件的评估与应用研究

目的 通过对比三种分子对接软件(MOE、LeDock、AutoDock …Vina)针对c-Met蛋白的虚拟筛选的准确性,选取准确性比较高的软件针对EGFR蛋白展开虚拟筛选,获得打分较高的候选化合物。方法采用虚拟筛选的方法,利用c-Met信号蛋白抑制剂作为评价体系,对MOE,AutoDock Vina,LeDock三种对接软件进行了对接结果比较,以此来评价各个软件之间的对接准确性。在此基础上,选择准确性高的软件对EGFR突变靶标开展小分子抑制剂的虚拟筛选,筛选出评价和作用良好的小分子化合物。结果与结论 对比结果显示MOE和LeDock软件的对接准确度和操作简易度要高于AutoDock Vina软件,并通过筛选,得到了评价良好的小分子化合物。     

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking

Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, GWOVina , which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, GWOVina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, GWOVina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, GWOVina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. GWOVina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.     

A New,Improved Hybrid Scoring Function for Molecular Docking and Scoring Based on AutoDock and AutoDock Vina

Automated docking is one of the most important tools for structure‐based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein–ligand complexes from pdbbind database ( www.pdbbind.org.cn , version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein–ligand complexes, as measured by the non‐parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root‐mean‐square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock.     

Study of binding of pyridoacridine alkaloids on topoisomerase II using in silico tools

In this research, the pyridoacridines alkaloids have been docked computationally to the active site of Topoisomerase II using four PDB structures (1PVG, 1QZR, 1AJ6 and 1ZXM). iGEMDOCK 2.1, AutoDock Vina 1.1.2 and AutoDock 4.2.1 were employed to perform the automated molecular docking. The results of docking studies generated docking scores and IC₅₀ values. Moreover, 3D pictures of ligand enzyme complexes afforded valuable data regarding the binding orientation of each inhibitor in the active site of Topoisomerase II.     

pso@autodock: a fast flexible molecular docking program based on Swarm intelligence

On the quest of novel therapeutics, molecular docking methods have proven to be valuable tools for screening large libraries of compounds determining the interactions of potential drugs with the target proteins. A widely used docking approach is the simulation of the docking process guided by a binding energy function. On the basis of the molecular docking program autodock, we present pso@autodock as a tool for fast flexible molecular docking. Our novel Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible ligands. Thus, a ligand with 23 rotatable bonds was successfully docked within as few as 100 000 computing steps (rmsd = 0.87 A), which corresponds to only 10% of the computing time demanded by autodock. In comparison to other docking techniques as gold 3.0, dock 6.0, flexx 2.2.0, autodock 3.05, and sodock, pso@autodock provides the smallest rmsd values for 12 in 37 protein-ligand complexes. The average rmsd value of 1.4 A is significantly lower then those obtained with the other docking programs, which are all above 2.0 A. Thus, pso@autodock is suggested as a highly efficient docking program in terms of speed and quality for flexible peptide-protein docking and virtual screening studies.     

Selective flexibility of side-chain residues improves VEGFR-2 docking score using AutoDock Vina

Selective side-chain residue flexibility is an option available on AutoDock Vina docking software. This approach is promising as it attempts to provide a more realistic ligand-protein interaction environment without an unmanageable increase in computer processing time. However, studies validating this approach are still scarce. VEGFR-2 (vascular endothelial growth factor receptor 2), a known protein target for anti-angiogenic agents, was used in this study. Four residues located in the VEGFR-2 kinase site were selected and made flexible: Lys868, Glu885, Cys919, and Asp1046. The docking scores for all possible combinations of flexible residues were compared to the docking scores using a rigid conformation. The best overall docking scores were obtained using the Glu885 flexible conformation, with Pearson and Spearman rank correlation values of 0.568 and 0.543, respectively, and a 51% increase in processing time. Using different VEGFR-2 crystal structures, a similar trend was observed with the Glu885 flexible conformation presenting best scores. This study demonstrates that careful use of selective side-chain residue flexibility can improve AutoDock Vina docking score accuracy, without a significant increase in processing time. This methodology can be a valuable tool in drug design projects using VEGFR-2 but will also probably be useful if applied to other protein targets.     

Optimization of Compound Ranking for Structure‐Based Virtual Ligand Screening Using an Established FRED–Surflex Consensus Approach

The use of multiple target conformers has been applied successfully in virtual screening campaigns; however, a study on how to best combine scores for multiple targets in a hierarchic method that combines rigid and flexible docking is not available. In this study, we used a data set of 59 479 compounds to screen multiple conformers of four distinct protein targets to obtain an adapted and optimized combination of an established hierarchic method that employs the programs FRED and Surflex. Our study was extended and verified by application of our protocol to ten different data sets from the directory of useful decoys (DUD). We quantitated overall method performance in ensemble docking and compared several consensus scoring methods to improve the enrichment during virtual ligand screening. We conclude that one of the methods used, which employs a consensus weighted scoring of multiple target conformers, performs consistently better than methods that do not include such consensus scoring. For optimal overall performance in ensemble docking, it is advisable to first calculate a consensus of FRED results and use this consensus as a sub‐data set for Surflex screening. Furthermore, we identified an optimal method for each of the chosen targets and propose how to optimize the enrichment for any target.     

Flexible receptor docking for drug discovery

Introduction: Molecular docking has become a popular method for virtual screening. Docking small molecules to a rigid biological receptor is fast but could produce many false negatives and identify less diverse compounds. Flexible receptor docking has alleviated this problem.

Areas covered: This article focuses on reviewing ensemble docking as an approximate but inexpensive method to incorporate receptor flexibility in molecular docking. It outlines key features and recent advances of this method and points out problem areas that need to be addressed to make it even more useful in drug discovery.

Expert opinion: Among the different methods introduced for flexible receptor docking, ensemble docking represents one of the most popular approaches, especially for high-throughput virtual screening. One can generate structural ensembles by using experimental structures, by structural modeling and by various types of molecular simulations. In building a structural ensemble, a judicious choice of the structures to be included can improve performance. Furthermore, reducing the size of the structural ensemble can cut computational costs, and removing the structures that can bind few ligands well could enrich the number of true actives identified by ensemble docking. The ability of ensemble docking to identify more true positives at the top of a rank-ordered list also depends on the choice of the methods to score and rank compounds, an area that needs further research.     

Drug-like leads for steric discrimination between substrate and inhibitors of human acetylcholinesterase

Protection of the enzyme acetylcholinesterase (AChE) from the toxic effects of organophosphate insecticides and chemical warfare agents (OPs) may be provided by inhibitors that bind at the peripheral binding site (P-site) near the mouth of the active-site gorge. Compounds that bind to this site may selectively block access to the acylation site (A-site) catalytic serine for OPs, but not acetylcholine itself. To identify such compounds, we employed a virtual screening approach using AutoDock 4.2 and AutoDock Vina, confirmed using compounds experimentally known to bind specifically to either the A-site or P-site. Both programs demonstrated the ability to correctly predict the binding site. Virtual screening of the NCI Diversity Set II was conducted using the apo form of the enzyme, and with acetylcholine bound at the crystallographic locations in the A-site only and in both and A- and P-sites. The docking identified 32 compounds that were obtained for testing, and one was demonstrated to bind specifically to the P-site in an inhibitor competition assay.     

Discovering Isozyme‐Selective Inhibitor Scaffolds of Human Carbonic Anhydrases Using Structural Alignment and De novo Drug Design Approaches

The development of isozyme‐selective carbonic anhydrase inhibitors is currently still a great challenge. In the present study, protein–ligand complex structures were obtained by AutoDock Vina with SBR ((R)‐N‐(3‐indol‐1‐yl‐2‐methyl‐propyl)‐4‐sulfamoyl‐benzamide) as the only inhibitor docked into the binding pockets of human isozymes CA I, II, IV, VI, IX, XII, and XIII. To make the spatial structures of complexes comparable, the co‐ordinates for CA domains were reassigned based on structural alignments. With preferred docking poses of SBR been reduced to seed structures, the LigBuilder was used to build up inhibitor molecules. The results suggested that sulfonamide derivatives with naphthalene, fluorene, and acridan as the scaffold structures can be the potential isozyme‐selective CAIs, especially for isozymes CA II, IV, and IX.     

Efficient macrocyclization for cyclicpeptide using solid-phase reaction

Cyclicpeptides are important targets in peptide synthesis because of their interesting biological properties. Constraining highly flexible linear peptides by cyclization is one of the mostly widely used approaches to define the bioactive conformation of peptides. Cyclic peptides often have increased receptor affinity and metabolic stability over their linear counterparts. We carried out virtual screening experiment via docking in order to understand the interaction between HLE-Human Leukocyte Elastase and ligand peptide and to identify the sequence that can be a target in various ligand peptides. We made cyclic peptides as a target base on Met-Ile-Phe sequence having affinity for ligand and receptor active site docking. There are three ways to cyclize certain sequences of amino acids such as Met-Ile-Phe-Gly-Ile. First is head-to-tail cyclization method, linking between N-terminal and C-terminal. Second method utilizes amino acid side chain such as thiol functional group in Cys, making a thioether bond. The last one includes an application of resin-substituted amino acids in solid phase reaction. Among the three methods, solid phase reaction showed the greatest yield. Macrocyclization of Fmoc-Met-Ile-Phe-Gly-Ile-OBn after cleavage of Fmoc protection in solution phase was carried out to give macrocyclic compound 5 in about 7% yield. In the contrast with solution phase reaction, solid phase reaction for macrocyclization of Met-Ile-Phe-Gly-Ile-Asp-Tentagel in normal concentrated condition gave macrocyclic compound 7 in more than 35% yield.     

Fragment‐ and negative image‐based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment‐ and negative image‐based (F‐NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A‐specific small‐molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F‐NiB combines features from both fragment‐based drug discovery and negative image‐based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein‐bound ligand(s) are seamlessly combined with the negative image of the target's ligand‐binding cavity. This cavity‐ and fragment‐based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure–activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F‐NiB provides a flexible way to incorporate small‐molecule fragments into the drug discovery.     

Receptor-ligand docking simulation for membrane proteins

Protein structure-based molecular design using the computational techniques of protein structure prediction, ligand docking, and virtual screening is an integral part of drug discovery for limiting the application of the structure-based approach to target proteins such as G-protein-coupled receptors (GPCRs). GPCRs play an important role in living organisms and are of major interest to the pharmaceutical industry. However, structural data on ligand-binding forms for GPCRs from experiments to elucidate structural templates for docking simulations are lacking due to the difficulties associated with crystallization and crystallography. Therefore structural prediction of GPCRs in the ligand-bound state using computational methods has been introduced, but the prediction of ligand conformation onto target GPCRs is still constructed manually by human experts. We developed a molecular modeling technique for the prediction of ligand-receptor binding using comparative ligand-binding analysis (CoLBA) that not only considers interaction energy but also the similarity of interaction profiles among ligands. The advantage of CoLBA is that it can facilitate intuitive and flexible screening based on docking results when protein structures with low resolution (or theoretical models) are targeted. We applied CoLBA to ligand-binding prediction in several GPCRs. The predicted ligand-binding models were evaluated in site-directed mutagenesis experiments in collaborative research, and the enrichment rate of activated ligands was compared with random compounds in virtual screening simulations. We propose that CoLBA can be applied in large-scale modeling of ligand-receptor complexes and virtual screening for GPCRs.     

Structure‐based discovery of new selective small‐molecule sirtuin 5 inhibitors

Human sirtuin 5 (SIRT5) is a protein deacylase regulating metabolic pathways and stress responses and is implicated in metabolism‐related diseases. Small‐molecule inhibitors for SIRT5 are sought as chemical tools and potential therapeutics. Herein, we proposed a customized virtual screening approach targeting catalytically important and unique residues Tyr102 and Arg105 of SIRT5. Of the 20 tested virtual screening hits, six compounds displayed marked inhibitory activities against SIRT5. For the hit compound 19 , a series of newly synthesized (E)‐2‐cyano‐N‐phenyl‐3‐(5‐phenylfuran‐2‐yl)acrylamide derivatives/analogues were carried out structure–activity relationship analyses, resulting in new more potent inhibitors, among which 37 displayed the most potent inhibition to SIRT5 with an IC₅₀ value of 5.59 ± 0.75 μM. The biochemical studies revealed that 37 likely acts via competitive inhibition with the succinyl‐lysine substrate, rather than the NAD⁺ cofactor, and it manifested substantial selectivity for SIRT5 over SIRT2 and SIRT6. This study will aid further efforts to develop new selective SIRT5 inhibitors as tools and therapeutics.     

虚拟筛选辅助新药发现方法研究进展

在新药发现过程中, 虚拟筛选的应用可以富集活性化合物, 降低筛选成本, 提高药物筛选的可行性, 因此已成为新药发现的重要方法。虚拟筛选与生物活性筛选的结合, 可以优势互补, 有效地促进新药的发现。本文介绍了非类药化合物排除、假阳性化合物排除、药效团搜索、分子对接计算以及分子相似性分析等几种方法在药物发现中的应用及其发展趋势, 以期更好地应用虚拟筛选方法, 促进新药的快速发现。     

In silico screening and surface plasma resonance‐based verification of programmed death 1‐targeted peptides

Programmed death 1 (PD‐1) is a key immune checkpoint molecule. When it binds to programmed death‐ligand 1 (PD‐L1), it can negatively regulate the immune response. Therefore, blockade of the PD‐1/PD‐L1 interaction could unleash the power of immune system. Though successes achieved by anti‐PD‐1/PD‐L1 antibody drugs in clinical for various cancers, many intrinsic limitations of the high molecular weight drugs require alternatives such as peptide drugs and chemical compounds. In this study, we described a novel in silico approach which was used to screen peptides from PDB database and aimed to identify peptides that have potential to bind the PD‐L1 binding area of PD‐1 molecule. Based on the docking poses, eight peptides were synthesized and measured for their binding abilities by surface plasma resonance technique. The K_D values of the synthesized peptides ranged from 10.0 to 133.0 μM. Furthermore, the binding mechanism between PD‐1 and the peptides was studied. In conclusion, we established a fast and reliable screening method for peptide discovery, which could be applied for identifying peptide inhibitors of various targets. The synthesized peptides could be served as starting points for designing PD‐1 drug for cancer immunotherapy.     

基于生物信息学与网络药理学的芹菜素抗前列腺癌的分子机制研究

目的 利用生物信息学结合网络药理学及分子对接系统性探索芹菜素抗前列腺癌的分子机制。方法 通过ETCM、HERB、HIT 2.0及SwissTargetPrediction数据库预测芹菜素靶点。计算TCGA和GEO数据集差异基因作为前列腺癌预测的疾病靶点,并检索GeneCards和OMIM数据库作为补充。应用STRING数据库和Cytoscape软件构建靶点蛋白相互作用（PPI）网络,并筛选核心靶点。采用Metascape数据库进行富集分析,使用PyMOL、AutoDock Tools、AutoDock Vina软件以及PDB数据库进行分子对接。结果 共预测到236个芹菜素抗前列腺癌作用靶点,主要与激素反应、细胞死亡调控及激酶调节有关,且京都基因与基因组百科全书（KEGG）富集通路与前列腺癌密切相关。筛选得到70个核心靶点,分子对接发现B细胞淋巴瘤因子-2（Bcl-2）、V-Jun肉瘤病毒癌基因同源物（JUN）、低氧诱导因子-1A（HIF-1A）、肿瘤坏死因子（TNF）、ERG、雌激素相关受体α（ESRRA）与芹菜素有良好结合力。结论 芹菜素通过多靶点和多通路实现抗前列腺癌作用。     

Virtual screening with AutoDock: theory and practice

Importance of the field: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules.

Areas covered in this review: We describe virtual screening methods that are available in the AutoDock suite of programs and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery.

What the reader will gain: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges.

Take home message: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.     

Synthesis,characterization, molecular modelling and biological evaluation of thieno‐pyrimidinone methanesulphonamide thio‐derivatives as non‐steroidal anti‐inflammatory agents

This study reports the synthesis, molecular docking and biological evaluation of eight ( 5 ‐ 8 and 5a ‐ 8a ) newly synthesized thieno‐pyrimidinone methanesulphonamide thio‐derivatives. The synthetic route used to prepare the new isomers thioaryl and thio‐cycloesyl derivatives of the heterocyclic system 6‐phenylthieno[3,2]pyrimidinone was economically and environmentally very advantageous and characterized by the simplicity of procedure, reduction in isolation steps, purification phases, time, costs and waste production. The study in silico for the evaluation of cyclooxygenase (COX)‐1 and COX‐2 selective inhibition was carried out by AutoDock Vina, an open‐source program for doing molecular docking which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. The research in vitro for the biological evaluation was performed by using human cartilage and chondrocytes cultures treated with 10 ng/mL of interleukin‐1beta as inflammation models. The anti‐inflammatory activity of each new compound at the concentration of 10 μmol/L was determined by assaying COX‐2, inducible nitric oxide synthetase (iNOS) and intercellular adhesion molecule 1 (ICAM 1) through Western blot. The examined derivatives showed interesting pharmacological activity, and the compound N‐[2‐[2,4‐difluorophenyl)thio]‐4‐oxo‐6‐phenylthieno[3,2‐d]pyridine‐34H‐yl]methanesulphonamide ( 7 ) was excellent COX‐2 inhibitor. In agreement with the biological data, compound 7 was able to fit into the active site of COX‐2 with highest interaction energy. These results can support the design of novel specific inhibitors of COX‐2 by the comparative modelling of COX‐1 and COX‐2 enzymes with the available pharmacophore.