计算机虚拟筛选技术在害虫行为调节剂研究中的应用

害虫行为调节剂是一种以嗅觉系统为靶标的绿色农药,在害虫的田间管理中发挥着重要的作用。然而,其先导化合物的发现通常依赖一系列生物测定的方法,不仅费时费力,且发现效率低。近年来,随着昆虫嗅觉功能数据的积累和结构生物学的飞速发展,以机器学习技术和分子对接为代表的2种基于计算机的药物虚拟筛选方法在害虫行为调节剂的先导化合物研究中发挥着重要的作用,极大地促进了先导化合物的发现效率,减少了筛选的盲目性。本文系统综述了2种虚拟筛选方法及其在害虫行为调节剂先导化合物研究中的应用,并对2种筛选策略在实际应用中存在的问题及应用前景进行了讨论。     

DFT study of the Lewis acid mediated synthesis of 3-acyltetramic acids

The recent emergence and re-emergence of alphaviruses, in particular the chikungunya virus (CHIKV), in numerous countries has invoked a worldwide threat to human health, while simultaneously generating an economic burden on affected countries. There are currently no vaccines or effective drugs available for the treatment of the CHIKV, and with few lead compounds reported, the vital medicinal chemistry is significantly more challenging. This study reports on the discovery of potential inhibitors for the nsP3 macro domain of CHIKV using molecular docking, virtual screening, and molecular dynamics simulations, as well as work done to evaluate and confirm the active site of nsP3. Virtual screening was carried out based on blind docking as well as focused docking, using the database of 1541 compounds from NCI Diversity Set II, to identify hit compounds for nsP3. The top hit compounds were further subjected to molecular dynamic simulations, yielding a greater understanding of the dynamic behavior of nsP3 and its complexes with various ligands, concurrently confirming the outcomes of docking, and establishing in silico lead compounds which target the CHIKV nsP3 enzyme.

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Discovery of novel inhibitors of Mycobacterium tuberculosis MurG: homology modelling,structure based pharmacophore,molecular docking,and molecular dynamics simulations

MurG (Rv2153c) is a key player in the biosynthesis of the peptidoglycan layer in Mycobacterium tuberculosis (Mtb). This work is an attempt to highlight the structural and functional relationship of Mtb MurG, the three-dimensional (3D) structure of protein was constructed by homology modelling using Discovery Studio 3.5 software. The quality and consistency of generated model was assessed by PROCHECK, ProSA and ERRAT. Later, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with substrate Uridine-diphosphate-N-acetylglucosamine (UD1) facilitated us to employ structure-based virtual screening approach to obtain new hits from Asinex database using energy-optimized pharmacophore modelling (e-pharmacophore). The pharmacophore model was validated using enrichment calculations, and finally, validated model was employed for high-throughput virtual screening and molecular docking to identify novel Mtb MurG inhibitors. This study led to the identification of 10 potential compounds with good fitness, docking score, which make important interactions with the protein active site. The 25 ns MD simulations of three potential lead compounds with protein confirmed that the structure was stable and make several non-bonding interactions with amino acids, such as Leu290, Met310 and Asn167. Hence, we concluded that the identified compounds may act as new leads for the design of Mtb MurG inhibitors.     

Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations

The human L-type amino acid transporter 1 (LAT1; SLC7A5) is a membrane transporter of amino acids, thyroid hormones, and drugs such as the Parkinson’s disease drug levodopa (L-Dopa). LAT1 is found in the blood-brain barrier, testis, bone marrow, and placenta, and its dysregulation has been associated with various neurological diseases, such as autism and epilepsy, as well as cancer. In this study, we combine metainference molecular dynamics simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. We first conducted a series of molecular docking experiments to identify the most relevant interactions between LAT1’s substrate-binding site and ligands, including both inhibitors and substrates. We then performed metainference molecular dynamics simulations using cryoelectron microscopy structures in different conformations of LAT1 with the electron density map as a spatial restraint, to explore the inherent heterogeneity in the structures. We analyzed the LAT1 substrate-binding site to map important LAT1-ligand interactions as well as newly described druggable pockets. Finally, this analysis guided the discovery of previously unknown LAT1 ligands using virtual screening and cellular uptake experiments. Our results improve our understanding of LAT1-inhibitor recognition, providing a framework for rational design of future lead compounds targeting this key drug target.     

BEAR, a novel virtual screening methodology for drug discovery

BEAR (binding estimation after refinement) is a new virtual screening technology based on the conformational refinement of docking poses through molecular dynamics and prediction of binding free energies using accurate scoring functions. Here, the authors report the results of an extensive benchmark of the BEAR performance in identifying a smaller subset of known inhibitors seeded in a large (1.5 million) database of compounds. BEAR performance proved strikingly better if compared with standard docking screening methods. The validations performed so far showed that BEAR is a reliable tool for drug discovery. It is fast, modular, and automated, and it can be applied to virtual screenings against any biological target with known structure and any database of compounds.     

Ensemble Docking in Drug Discovery

Ensemble docking corresponds to the generation of an “ensemble” of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.     

Identification of Potential Inhibitors of H5N1 Influenza A Virus Neuraminidase by Ligand-Based Virtual Screening Approach

The neuraminidase (NA) of the influenza virus is the target of antiviral drug, oseltamivir. Recently, cases were reported that influenza virus becoming resistant to oseltamivir, necessitating the development of new long-acting antiviral compounds. In this report, a novel class of lead molecule with potential NA inhibitory activity was identified using a combination of virtual screening (VS), molecular docking, and molecular dynamic approach. The PubChem database was used to perform the VS analysis by employing oseltamivir as query. Subsequently, the data reduction was carried out by employing molecular docking study. Furthermore, the screened lead molecules were analyzed with respect to the Lipinski rule of five, drug-likeness, toxicity profiles, and other physico-chemical properties of drugs by suitable software program. Final screening was carried out by normal mode analysis and molecular dynamic simulation approach. The result indicates that CID 25145634, deuterium-enriched oseltamivir, become a promising lead compound and be effective in treating oseltamivir sensitive as well as resistant influenza virus strains.     

Molecular modeling studies and anti-TB activity of trisubstituted indolizine analogues; molecular docking and dynamic inputs

A series of trisubstituted indolizine analogues has been designed as a result of a fragment-based approach to target the inhibition of mycobacterial enoyl-acyl carrier protein reductase. Anti-tuberculosis (TB) screening of the characterized compounds by a resazurin microplate assay method revealed that ethyl group at second position of indolizine nucleus exhibited activity against susceptible and multidrug-resistant strains of Mycobacterium tuberculosis at concentration of 5.5 and 11.3 μg/mL, respectively. A molecular docking study was also conducted to evaluate the stability of the active compounds, and compound with ethyl substitution at second position of indolizine nucleus showed the highest free binding energy of ΔG ?24.11 (kcal/mol), a low clash score of 3.04, and high lipo score of ?13.33. Indolizine analog with ethyl substitution at second position demonstrated Molecular Mechanics/Generalized Born Surface Area (?23.85 kcal/mol). Two molecular dynamics studies were computed (100 ps and 50 ns) to calculate the relationship between the potential and kinetic energies of the active anti-TB compound with time and temperature. The discovery of this lead may have a positive impact on anti-TB drug discovery.     

Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4

In drug discovery process, improvement of ADME/Tox properties of lead compounds including metabolic stability is critically important. Cytochrome P450 (CYP) is one of the major metabolizing enzymes and the prediction of sites of metabolism (SOM) on the given lead compounds is key information to modify the compounds to be more stable against metabolism. There are two factors essentially important in SOM prediction. First is accessibility of each substrate atom to the oxygenated Fe atom of heme in a CYP protein, and the other is the oxidative reactivity of each substrate atom. To predict accessibility of substrate atoms to the heme iron, conventional protein-rigid docking simulations have been applied. However, the docking simulations without consideration of protein flexibility often lead to incorrect answers in the case of very flexible proteins such as CYP3A4. In this study, we demonstrated an approach utilizing molecular dynamics (MD) simulation for SOM prediction in which multiple MD runs were executed using different initial structures. We applied this strategy to CYP3A4 and carbamazepine (CBZ) complex. Through 10 ns MD simulations started from five different CYP3A4-CBZ complex models, our approach correctly predicted SOM observed in experiments. The experimentally known epoxidized sites of CBZ by CYP3A4 were successfully predicted as the most accessible sites to the heme iron that was judged from a numerical analysis of calculated ΔG(binding) and the frequency of appearance. In contrast, the predictions using protein-rigid docking methods hardly provided the correct SOM due to protein flexibility or inaccuracy of the scoring functions. Our strategy using MD simulation with multiple initial structures will be one of the reliable methods for SOM prediction.     

Structural insights on identification of potential lead compounds targeting WbpP in Vibrio vulnificus through structure-based approaches

WbpP encoding UDP-GlcNAC C4 epimerase is responsible for the activation of virulence factor in marine pathogen Vibrio vulnificus (V. vulnificus) and it is linked to many aquatic diseases, thus making it a potential therapeutic target. There are few reported compounds that include several natural products and synthetic compounds targeting Vibrio sp, but specific inhibitor targeting WbpP are unavailable. Here, we performed structure-based virtual screening using chemical libraries such as Binding, TOSLab and Maybridge to identify small molecule inhibitors of WbpP with better drug-like properties. Deficient structural information forced to model the structure and the stable protein structure was obtained through 30?ns of MD simulations. Druggability regions are focused for new lead compounds and our screening protocol provides fast docking of entire small molecule library with screening criteria of ADME/Lipinski filter/Docking followed by re-docking of top hits using a method that incorporates both ligand and protein flexibility. Docking conformations of lead molecules interface displays strong H-bond interactions with the key residues Gly101, Ser102, Val195, Tyr165, Arg298, Val209, Ser142, Arg233 and Gln200. Subsequently, the top-ranking compounds were prioritized using the molecular dynamics simulation-based conformation and stability studies. Our study suggests that the proposed compounds may aid as a starting point for the rational design of novel therapeutic agents.     

An efficient computational method for calculating ligand binding affinities

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol⁻¹ of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.     

Improved Ligand-Protein Binding Affinity Predictions Using Multiple Binding Modes

Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.     

Virtual screening of small molecules databases for discovery of novel PARP-1 inhibitors: combination of in silico and in vitro studies

Poly(ADP-ribose) polymerase-1 (PARP-1) enzyme has critical roles in DNA replication repair and recombination. Thus, PARP-1 inhibitors play an important role in the cancer therapy. In the current study, we have performed combination of in silico and in vitro studies in order to discover novel inhibitors against PARP-1 target. Structure-based virtual screening was carried out for an available small molecules database. A total of 257,951 ligands from Otava database were screened at the binding pocket of PARP-1 using high-throughput virtual screening techniques. Filtered structures based on predicted binding energy results were then used in more sophisticated molecular docking simulations (i.e. Glide/standard precision, Glide/XP, induced fit docking – IFD, and quantum mechanics polarized ligand docking – QPLD). Potential high binding affinity compounds that are predicted by molecular simulations were then tested by in vitro methods. Computationally proposed compounds as PARP-1 inhibitors (Otava Compound Codes: 7111620047 and 7119980926) were confirmed by in vitro studies. In vitro results showed that compounds 7111620047 and 7119980926 have IC₅₀ values of 0.56 and 63 μM against PARP-1 target, respectively. The molecular mechanism analysis, free energy perturbation calculations using long multiple molecular dynamics simulations for the discovered compounds which showed high binding affinity against PARP-1 enzyme, as well as structure-based pharmacophore development (E-pharmacophore) studies were also studied.     

Exploration of Virtual Candidates for Human HMG-CoA Reductase Inhibitors Using Pharmacophore Modeling and Molecular Dynamics Simulations

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.     

Design of new chemotherapeutics against the deadly anthrax disease. Docking and molecular dynamics studies of inhibitors containing pyrrolidine and riboamidrazone rings on nucleoside hydrolase from Bacillus anthracis

Anthrax is a disease caused by Bacillus anthracis, a dangerous biological warfare agent already used for both military and terrorist purposes. An important selective target for chemotherapy against this disease is nucleoside hydrolase (NH), an enzyme still not found in mammals. Having this in mind we have performed molecular docking studies, aiming to analyze the three-dimensional positioning of six known inhibitors of Trypanosoma vivax NH (TvNH) in the active site of B. anthracis NH (BaNH). We also analyzed the main interactions of these compounds with the active site residues of BaNH and the relevant factors to biological activity. These results, together with further molecular dynamics (MD) simulations, pointed out to the most promising compounds as lead for the design of potential inhibitors of BaNH. Most of the docking and MD results obtained corroborated to each other. Additionally, the docking results also suggested a good correlation with experimental data.     

E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

Glycogen synthase kinase-3β (GSK-3β) is a serine/threonine kinase which has attracted significant attention during recent years in drug design studies. The deregulation of GSK-3β increased the loss of hippocampal neurons by triggering apoptosis-mediating production of neurofibrillary tangles and alleviates memory deficits in Alzheimer’s disease (AD). Given its role in the formation of neurofibrillary tangles leading to AD, it has been a major therapeutic target for intervention in AD, hence was targeted in the present study. Twenty crystal structures were refined to generate pharmacophore models based on energy involvement in binding co-crystal ligands. Four common e-pharmacophore models were optimized from the 20 pharmacophore models. Shape-based screening of four e-pharmacophore models against nine established small molecule databases using Phase v3.9 had resulted in 1800 compounds having similar pharmacophore features. Rigid receptor docking (RRD) was performed for 1800 compounds and 20 co-crystal ligands with GSK-3β to generate dock complexes. Interactions of the best scoring lead obtained through RRD were further studied with quantum polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area. Comparing the obtained leads to 20 co-crystal ligands resulted in 18 leads among them, lead1 had the lowest docking score, lower binding free energy and better binding orientation toward GSK-3β. The 50?ns MD simulations run confirmed the stable nature of GSK-3β-lead1 docking complex. The results from RRD, QPLD, IFD and MD simulations confirmed that lead1 might be used as a potent antagonist for GSK-3β.     

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM,pharmacophore and docking model

Abstract

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.     

Accounting for global protein deformability during protein-protein and protein-ligand docking

Computational docking methods are valuable tools aimed to simplify the costly process of drug development and improvement. Most current approaches assume a rigid receptor structure to allow virtual screening of large numbers of possible ligands and putative binding sites on a receptor molecule. However, inclusion of receptor flexibility can be of critical importance since binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a ligand. Recent approaches to efficiently account for receptor flexibility during docking simulations are reviewed. In particular, accounting efficiently for global conformational changes of the protein backbone during docking is a still challenging unsolved problem. An approximate method has recently been suggested that is based on relaxing the receptor conformation during docking in pre-calculated soft collective degrees of freedom (M. Zacharias, Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP, Proteins: Struct., Funct., Genet. 54 (2004) 759-767). Test applications on protein-protein docking and on docking the inhibitor staurosporine to the apo-form of cAMP-dependent protein kinase A catalytic domain indicate significant improvement of docking results compared to rigid docking at a very modest computational demand. Accounting for receptor conformational changes in pre-calculated global degrees of freedom might offer a promising route to improve systematic docking screening simulations.