Molecular docking as a popular tool in drug design, an in silico travel

Antioxidant-Rich Woodfordia fruticosa Leaf Extract Alleviates Depressive-Like Behaviors and Impede Hyperglycemia

Dhaiphul (Woodfordia fruticosa) is a frequently demanded plant in South-East Asian regions for its diverse medicinal values. This study was proposed to examine antioxidant, antidiabetic, and antidepressant potentials of methanol extract of W. fruticosa leaves (MEWF) and its derived n-hexane (NHFMEWF) and ethyl acetate (EAFMEWF) fractions through in vitro, in vivo, and computational models. Among test samples, MEWF and EAFMEWF contained the highest phenolic content and showed maximal antioxidant activity in DPPH radical scavenging and ferric reducing power assays. In comparison, NHFMEWF possessed maximum flavonoid content and a significantly potent α-amylase inhibitory profile comparable with positive control acarbose. In animal models of depression (forced swimming and tail suspension test), EAFMEWF and NHFMEWF demonstrated a dose-dependent antidepressant-like effect; explicitly, the depressive-like behaviors significantly declined in EAFMEWF-treated dosing groups in contrast to the control group. In the computational analysis, previously isolated flavonoid compounds from Dhaiphul leaves manifested potent binding affinity against several key therapeutic target proteins of diabetes and depressive disorders including α-amylase, serotonin transporter, dopamine transporter, and neuronal nitric oxide synthase with varying pharmacokinetics and toxicity profiles. This research's outcomes may provide potential dietary supplements for mitigating hyperglycemia, cellular toxicity, and depressive disorder.

Gene set enrichment analysis of α-amylase and α-glucosidase inhibitors of Cassia glauca

Background

The present study aimed to evaluate in vitro α-amylase and α-glucosidase inhibitory activity of various extracts of Cassia glauca, predict the binding affinity of multiple phytoconstituents with both enzymes via in silico molecular docking and identify the probably modulated pathways by the lead hit.

Methods

Different extracts of Cassia glauca i.e. acetone, ethanol, and aqueous extracts were evaluated for α-amylase and α-glucosidase inhibitory activity using in vitro method in which starch and 4-Nitrophenyl β-D-glucopyranoside were used as substrate respectively. Similarly, the docking study was performed using autodock4 to predict the binding affinity of phytoconstituents with α-amylase and α-glucosidase. After docking, ten different poses were obtained for the ligand molecule. Among them, the pose of ligand molecule with the lowest binding energy was visualized in Discovery Studio 2019.

Results and conclusion

Among the multiple extracts, the aqueous extract showed the highest α-amylase (IC₅₀:652.10 ± 20.09) and α-glucosidase (IC₅₀:482.46 ± 8.70) inhibitory activity. Similarly, cassiaoccidentalin B was predicted to have the highest binding affinity with both enzymes. The potency of aqueous extract to inhibit α-amylase and α-glucosidase could be due to multiple water-soluble compounds like saponins, flavonoids, and glycosides.

Identification of DNA Methyltransferase-1 Inhibitor for Breast Cancer Therapy through Computational Fragment-Based Drug Design

Epimutation by DNA Methyltransferase 1 (DNMT1), an epigenetic regulator enzyme, may lead to the proliferation of breast cancer. In this report, 168,686 natural products from the PubChem database were screened and modified by in silico method to acquire the potential inhibitor of DNMT1. The initial screening of PubChem natural products using Lipinski's and Veber's rules of three and toxic properties have resulted in 2601 fragment candidates. Four fragments from pharmacophore-based molecular docking simulation were modified by utilizing FragFP and the Lipinski's and Veber's rules of five, and resulted in 51,200 ligands. The toxicological screening collected 13,563 ligands for a series of pharmacophore-based molecular docking simulations to sort out the modified ligands, which had the better binding activity and interactions to DNMT1 compared to the standards, SAH, SAM, and SFG. This step resulted in five ligand candidates, namely C-7756, C-5769, C-1723, C-2129, and C-2140. The ADME-Tox properties prediction showed that the selected ligands are generally better than standards in terms of druglikeness, GI absorption, and oral bioavailability. C-7756 exhibited a stronger affinity to DNMT1 as well as better ADME-Tox properties compared to the other ligands.

In Silico Evaluation of Prospective Anti-COVID-19 Drug Candidates as Potential SARS-CoV-2 Main Protease Inhibitors

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emanating human infectious coronavirus that causes COVID-19 disease. On 11th March 2020, it has been announced as a pandemic by the World Health Organization (WHO). Recently, several repositioned drugs have been subjected to clinical investigations as anti-COVID-19 drugs. Here, in silico drug discovery tools were utilized to evaluate the binding affinities and features of eighteen anti-COVID-19 drug candidates against SARS-CoV-2 main protease (M^pro). Molecular docking calculations using Autodock Vina showed considerable binding affinities of the investigated drugs with docking scores ranging from - 5.3 to - 8.3 kcal/mol, with higher binding affinities for HIV drugs compared to the other antiviral drugs. Molecular dynamics (MD) simulations were performed for the predicted drug-M^pro complexes for 50 ns, followed by binding energy calculations utilizing molecular mechanics-generalized Born surface area (MM-GBSA) approach. MM-GBSA calculations demonstrated promising binding affinities of TMC-310911 and ritonavir towards SARS-CoV-2 M^pro, with binding energy values of - 52.8 and - 49.4 kcal/mol, respectively. Surpass potentialities of TMC-310911 and ritonavir are returned to their capabilities of forming multiple hydrogen bonds with the proximal amino acids inside M^pro's binding site. Structural and energetic analyses involving root-mean-square deviation, binding energy per-frame, center-of-mass distance, and hydrogen bond length demonstrated the stability of TMC-310911 and ritonavir inside the M^pro's active site over the 50 ns MD simulation. This study sheds light on HIV protease drugs as prospective SARS-CoV-2 M^pro inhibitors.

Molecular modeling, docking and dynamic simulations of growth hormone receptor (GHR) of Labeo rohita

Growth hormones (GH) have diverse functions like growth promotion, metabolism, appetite, reproduction and social behavior in vertebrates, which is mediated through the growth hormone receptor (GHR). This work was aimed to analyze structural features, homology modeling and molecular docking of Labeo rohita GHR protein. A physicochemical characteristic, like molecular weight was 67.2 kDa and hydropathicity was 0.336. Protein modeling and structure confirmation of L. rohita GHR protein showed 92.7% residues are in the favored region. Selection of ligands and molecular docking shown Melengestrol and Riboflavin ligand showed uppermost binding energy values -7.8 and -7.3 kcal/mol. Molecular interactions describe conventional hydrogen bonding of Melengestrol was observed with VAL94, GLU97, GLU95, TRP57, PHE33, THR34, PRO35, ASP36, PRO37, ARG49, GLY292, LYS291, ILE290, ALA287, LYS289 residues. Riboflavin hydrogen bonds interaction was at PRO37, ASP36, PRO35, THR34, ARG49, SER144, VAL443, GLN442, PRO284, ASP294, ILE285, PRO286, SER408, ALA287, GLY292, LYS291, ILE290, PRO288, LYS287. Molecular dynamics simulation outcomes revealed that complex 2 (Riboflavin and GHR protein) is better than complex1 (Melengestrol and GHR protein). Overall, the results of the present work lead identification of novel molecules that may be agonistic of growth hormone receptor protein and can be used to surge growth in fish. Communicated by Ramaswamy H. Sarma.

Molecular Docking Simulation Studies of Curcumin and Its Derivatives as Cyclin-Dependent Kinase 2 Inhibitors

Objectives

Cyclin-dependent kinase 2 (CDK2) is a protein that plays a role in regulating the cell cycle and its overexpression contributes to uncontrolled cell proliferation. Inhibition of CDK2 is known to be a mechanism of action of various anti-cancer drugs. Curcumin is an active compound of Curcuma longa and it has been reported to inhibit the activity of cyclin D, cyclin E, CDK2, CDK4, and CDK6. This study aimed to design more active curcumin derivatives as anticancer drugs by targeting CDK2 through a molecular modeling approach.

Materials and Methods

The molecular modeling approach consists of receptor and ligand preparation, method validation, pharmacophore modeling, and docking simulation.

Results

The results of the molecular docking simulation show that the free bonding energy (ΔG) of curcumin and kurkumod 23 and 24 (the best modification of curcumin) are -7.80, -9.15, and -9.36 kcal/mol, respectively. The hydrogen interaction between kurkumod 23 and 24 with CDK occurred on Lys33 residue, which is considered a potential interaction site for CDK2 inhibitor compounds. Pharmacophore modeling showed that kurkumod 23 and 24 have pharmacophore-fit values of 45.20% and 47.26%, respectively.

Conclusion

The results of this study indicate that kurkumod 23 and 24 are the best and most potent modifications of curcumin as CDK2 antagonist, based on the interactions that occur between these two derivatives with amino acid residues from the CDK2 receptor.

Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction

A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.

Identification of Novel Phyto-chemicals from Ocimum basilicum for the Treatment of Parkinson's Disease using In Silico Approach

BACKGROUND

Parkinson's disease is characterized by decreased level of dopaminergic neurotransmitters and this decrease is due to the degradation of dopamine by protein Monoamine Oxidase B (MAO-B). In order to treat Parkinson's disease, MAO-B should be inhibited.

OBJECTIVE

To find out the novel phytochemicals from plant Ocimum basilicum that can inhibit MAO-B by using the in silico methods.

METHODS

The data of chemical constituents from plant Ocimum basilicum was collected and inhibitory activity of these phytochemicals was then predicted by using the Structure-Based (SB) and Ligand-Based Virtual Screening (LBVS) methods. Molecular docking, one of the common Structure-Based Virtual Screening method, has been used during this search. Traditionally, molecular docking is used to predict the orientation and binding affinity of the ligand within the active site of the protein. Molegro Virtual Docker (MVD) software has been used for this purpose. On the other hand, Random Forest Model, one of the LBVS method, has also been used to predict the activity of these chemical constituents of Ocimum basilicum against the MAO-B.

RESULTS

During the docking studies, all the 108 compounds found in Ocimum basilicum were docked within the active site of MAO-B (PDB code: 4A79) out of which, 57 compounds successfully formed the hydrogen bond with tyr 435, a crucial amino acid for the biological activity of the enzyme. Rutin (-182.976 Kcal/mol), Luteolin (-163.171 Kcal/mol), Eriodictyol-7-O-glucoside (- 160.13 Kcal/mol), Rosmarinic acid (-133.484 Kcal/mol) and Isoquercitrin (-131.493 Kcal/mol) are among the top hits with the highest MolDock score along with hydrogen interaction with tyr 435. Using the RF model, ten compounds out of 108 chemical constituent of Ocimum basilicum were predicted to be active, Apigenin (1.0), Eriodictyol (1.0), Orientin (0.876), Kaempferol (0.8536), Luteolin (0.813953) and Rosmarinic-Acid (0.7738095) are predicted to be most active with the highest RF score.

CONCLUSION

The comparison of the two screening methods show that the ten compounds that were predicted to be active by the RF model, are also found in top hits of docking studies with the highest score. The top hits obtained during this study are predicted to be the inhibitor of MAO-B, thus, could be used further for the development of drugs for the treatment of Parkinson's disease (PD).

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors

Background:

Alzheimer’s disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer’s disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites.

Methods:

In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria.

Results:

The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme.

Conclusion:

Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

Molecular Pathogenesis, Immunopathogenesis and Novel Therapeutic Strategy Against COVID-19

The coronavirus disease 2019 (COVID-19), is a highly contagious transmittable disease caused by a recently discovered coronavirus, pathogenic SARS-CoV-2. Followed by the emergence of highly pathogenic coronaviruses in 2003 SARS-CoV, in 2012 MERS-CoV, now in 2019 pathogenic SARS-CoV-2, is associated with a global "pandemic" situation. In humans, the effects of these viruses are correlated with viral pneumonia, severe respiratory tract infections. It is believed that interaction between angiotensin converting enzyme 2 (ACE2) cell receptor and viral Spike protein mediates the coronavirus entry into human respiratory epithelial cells and establishes the host tropism. ACE2 receptor is highly expressed in airway epithelial cells. Along with viral-receptor interaction, proteolytic cleavability of S protein has been considered as the determinant of disease severity. Several studies highlight the occurrence of impaired host immune response and expression of excessive inflammatory response especially cytokines against viral infection. The mechanisms of SARS-CoV-2 induced acute lung injury are still undefined; however, the term cytokine storm has now been recognized to be closely associated with COVID-19. The levels of inflammatory mediators from cytokine storm cause damage to the host cells. In particular, the proinflammatory cytokine IL-6 appears to be the key mediator in early phase of virus-receptor interaction; however, secreted IL-6 might not be representative of lung inflammation. Understanding the cellular, and molecular factors involved in immune dysregulation and the high virulence capacity of COVID-19 will help in potential targeted therapy against it. "Drug repurposing" and "molecular docking analysis" is considered as an attractive alternative approach in analyzing suitable drug candidates to combat SARS-CoV-2 infection. Globally, extensive research is in progress to discover a new vaccine for novel COVID-19. Moreover, our review mainly focuses on the most state-of-the-art therapeutic approach mediated by "Mannose-binding lectin (MBL)." One of the most significant molecules of innate immunity is MBL. It plays a major role in the activation of the complement system as an ante-antibody prior to the response of any particular antibody. Recombinant human MBL can be used as immunomodulators against SARS-CoV-2.

Biological Evaluation and Molecular Modeling of 3,4-dihydropyrimidine- 2(1H)-one Derivatives as Cytotoxic Agents on Breast Cancer In Vitro

Background:

Kinesins and tubulin inhibitors have attracted researchers’ attention as hopeful targets for achieving effective anticancer agents. Dihydropyrimidine-2-ones (DHPMs) inhibit motor proteins Eg5 in the polymerization process of tubulin, also scaffold bearing benzothiazole heterocycle can block tubulin polymerization/depolymerization.

Objective:

In this study, the cytotoxic effects and molecular modeling of newly synthesized derivatives of DHPM that were designed by the Scaffold-hopping approach were investigated as potential dual-inhibitors of Eg5 and tubulin.

Methods:

We investigated the cytotoxic effects of DHPMs derivatives by MTT assay and measureing the Caspase 3 activity. Also, molecular modeling studies were performed by AutoDock4 and GROMACS 4.5.6.

Results:

According to the results, the d2 derivative (IC50 = 68.58 ± 7, SI = 2.57) eliminates MDA-MB- 231 cells in a dose-dependent manner through caspase-dependent and caspase-independent cell death pathways. Molecular docking studies revealed that the d2 compound could interact with both Eg5 and tubulin key residues. MD simulation also demonstrated the stability of the studied ligand-receptor complexes during the 30 ns of the production run. The effectiveness of substitutions at C4 of the DHPM ring was obtained 4-acetoxy-phenyl, 4-methoxyphenyl, and 4-nitrophenyl, respectively.

Conclusion:

The findings of the present study provide evidence that DHPM C5 amide derivatives bearing benzothiazole ring might be considered as promising lead compounds for the discovery of novel and multi-target antitumor agents.

Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications

The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure-activity relationships (QSAR)-often as its 3D QSAR variant-are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.

Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

Three-dimensional conformational crystallographic binding-modes are of paramount importance to understand the docking mechanism of protein-ligand interactions and to identify potential "leading drugs" conformers towards rational drugs-design. Herein, we present an integrated computational-experimental study tackling the problem of multiple binding modes among the ligand 3-(2-Benzothiazolylthio)-propane sulfonic acid (BTS) and the fibrinogen receptor (E-region). Based on molecular docking simulations, we found that the free energy of binding values for nine of different BTS-docking complexes (i.e., BTS-pose_1-9) were very close. We have also identified a docking-mechanism of BTS-interaction mainly based on non-covalent hydrophobic interactions with H-bond contacts stabilizing the fibrinogen-BTS docking complexes. Interestingly, the different BTS-poses_1-9 were found to be able to block the fibrinogen binding site (E-region) by inducing local perturbations in effector and allosteric residues, reducing the degree of collectivity in its flexibility normal modes. As such, we theoretically suggest that the BTS-binding modes can significantly affect the physiological condition of the unoccupied fibrinogen protein structure by bringing global and local perturbations in the frequency domain spectra. The proposed theoretical mechanisms, the interactions involved and the conformational changes suggested, were further corroborated by different experimental techniques such as isothermal titration calorimetry (ITC), zeta potential, UV-vis, fluorescence and small angle X-ray scattering (SAXS). The combined results shall open new avenues towards the application of complex supra-molecular information in rational drugs-design.

Computational Evaluation and In Vitro Validation of New Epidermal Growth Factor Receptor Inhibitors

BACKGROUND

The Epidermal Growth Factor Receptor (EGFR) is a transmembrane protein that acts as a receptor of extracellular protein ligands of the epidermal growth factor (EGF/ErbB) family. It has been shown that EGFR is overexpressed by many tumours and correlates with poor prognosis. Therefore, EGFR can be considered as a very interesting therapeutic target for the treatment of a large variety of cancers such as lung, ovarian, endometrial, gastric, bladder and breast cancers, cervical adenocarcinoma, malignant melanoma and glioblastoma.

METHODS

We have followed a structure-based virtual screening (SBVS) procedure with a library composed of several commercial collections of chemicals (615,462 compounds in total) and the 3D structure of EGFR obtained from the Protein Data Bank (PDB code: 1M17). The docking results from this campaign were then ranked according to the theoretical binding affinity of these molecules to EGFR, and compared with the binding affinity of erlotinib, a well-known EGFR inhibitor. A total of 23 top-rated commercial compounds displaying potential binding affinities similar or even better than erlotinib were selected for experimental evaluation. In vitro assays in different cell lines were performed. A preliminary test was carried out with a simple and standard quick cell proliferation assay kit, and six compounds showed significant activity when compared to positive control. Then, viability and cell proliferation of these compounds were further tested using a protocol based on propidium iodide (PI) and flow cytometry in HCT116, Caco-2 and H358 cell lines.

RESULTS

The whole six compounds displayed good effects when compared with erlotinib at 30 μM. When reducing the concentration to 10μM, the activity of the 6 compounds depends on the cell line used: the six compounds showed inhibitory activity with HCT116, two compounds showed inhibition with Caco-2, and three compounds showed inhibitory effects with H358. At 2 μM, one compound showed inhibiting effects close to those from erlotinib.

CONCLUSION

Therefore, these compounds could be considered as potential primary hits, acting as promising starting points to expand the therapeutic options against a wide range of cancers.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

Molecular Drug Discovery of Single Ginsenoside Compounds as a Potent Bruton's Tyrosine Kinase Inhibitor

: Bruton's tyrosine kinase (BTK) is known as a direct regulator of inflammasome, which is an intracellular target to therapeutically modulate innate immunity. Although there is great interest in developing small molecule-based drugs with BTK inhibition, there are only a few drugs available in the market, due to the difficulty of drug discovery and the potential side effects. To select suitable drug compounds to inhibit BTK signaling, molecular drug screening bioassay processes of single ginsenosides integrated with in silico molecular simulation were performed. The experimental results for the ginsenoside compositions (Rb2 and Rb3) exhibited showed that they effectively suppressed the activity of BTK expression in a rational agreement with molecular docking calculations of the compounds against the BTK binding site. They implemented a possible inhibiting effect of BTK signaling through increasing their molecular affinity for targeting BTK, enabling them to be useful in treating BTK-mediated diseases.

Comparative docking analysis of cholesterol analogs to ion channels to discriminate between stereospecific binding vs. stereospecific response

Cholesterol is a major component of the membrane and a key regulator of many ion channels. Multiple studies showed that cholesterol regulates ion channels in a stereospecific manner, with cholesterol but not its chiral isomers having a functional effect. This stereospecificity has been universally attributed to the specificity of cholesterol binding, with the assumption that only native cholesterol binds to the channels whereas its isomers do not. In this study, we challenge this paradigm by docking analyses of cholesterol and its chiral isomers to five ion channels whose response to cholesterol was shown to be stereospecific, Kir2.2, KirBac1.1, TRPV1, GABA_A and BK. The analysis is performed using AutoDock Vina to predict the binding poses and energies of the sterols to the channels and identify amino acids interacting with the sterol molecules. We found that for every ion channel tested herein all three sterols showed similar binding poses and significant overlap in the set of the amino acids that comprise the predicted binding sites, along with similar energetic favorability to these overlapping sites. We also found, however, that specific orientations of the three sterols within the binding sites of the channels are distinct, so that a subset of the interacting amino acids is unique to each sterol. We propose therefore, that contrary to previous thought, stereospecific effects of cholesterol should be attributed not to the lack of binding of the stereoisomers but to specific, unique interactions between the cholesterol molecule and the residues within the binding sites of the channels.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Delving into the Characteristic Features of "Menace" Mycobacterium tuberculosis Homologs: A Structural Dynamics and Proteomics Perspectives

The global increase in the morbidity/mortality rate of Mycobacterial infections, predominantly renascent tuberculosis, leprosy, and Buruli ulcers have become worrisome over the years. More challenging is the incidence of resistance mediated by mutant Mycobacterium strains against front-line antitubercular drugs. Homologous to all Mycobacteria species is the GlcNAc-6-phosphate deacetylase (NagA) which catalyzes essential amino sugars synthesis required for cell wall architecture, hence, metamorphosing into an important pharmacological target for curtailing virulence and drug-resistance. This study used integrated bioinformatics methods, MD simulations, and DynaMut and PolyPhen2 to; explore unique features, monitor dynamics, and analyze the functional impact of non-synonymous single-nucleotide polymorphisms of the six NagA of most ruinous Mycobacterium species; tuberculosis (Mtb), smegmatis (MS), marinum (MM), ulcerans, africanum, and microti respectively. This approach is essential for multi-targeting and could result in the identification of potential polypharmacological antitubercular compounds. Comparative sequential analyses revealed ≤ 50% of the overall structure, including the catalytic Asp267 and reactive Cys131, remained conserved. Interestingly, MS-NagA and MM-NagA possess unique hydrophobic isoleucine (Ile) residues at their active sites in contrast to leucine (Leu) found in other variants. More so, unique to the active sites of the NagA is a 'subunit loop' that covers the active site; probably crucial in binding (entry and exit) mechanisms of targeted NagA inhibitors. Relatively, nsSNP mutations exerted a destabilizing effect on the native NagA conformation. Structural and dynamical insights provided, basically pin-pointed the "Achilles' heel" explorable for the rational drug design of target-specific 'NagA' inhibitors potent against a wide range of mycobacterial diseases.

A Pilot Study of All-Computational Drug Design Protocol-From Structure Prediction to Interaction Analysis

Speeding up the drug discovery process is of great significance. To achieve that, high-efficiency methods should be exploited. The conventional wet-bench methods hardly meet the high-speed demand due to time-consuming experiments. Conversely, in silico approaches are much more efficient for drug discovery and design. However, in silico approaches usually serve as a supportive role in research processes. To fully exert the strength of computational methods, we propose a protocol which integrates various in silico approaches, from de novo protein structure prediction to ligand-protein interaction simulation. As a proof of concept, human SK2/calmodulin complex was used as a target for validation. First, we obtained a predicted structure of SK2/calmodulin and predicted binding sites which were consistent with the literature data. Then we investigated the ligand-protein interaction via virtual mutagenesis, flexible docking, and binding affinity calculation. As a result, the binding energies of mutants have similar trends compared with the EC₅₀ values (R = 0.6 for NS309 in V481 mutants). The results indicate that our protocol can be applied to the drug design of structure unknown proteins. Our study also demonstrates that the integration of in silico approaches is feasible and it facilitates the acceleration of new drug discovery.

Mol-CycleGAN: a generative model for molecular optimization

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improve the compound design process, we introduce Mol-CycleGAN-a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

In Vitro Antidiabetic, Anti-Obesity and Antioxidant Analysis of Ocimum basilicum Aerial Biomass and in Silico Molecular Docking Simulations with Alpha-Amylase and Lipase Enzymes

The present study explored phytochemicals, porcine pancreatic α-amylase (PPA) and lipase (PPL) inhibitory activities and antioxidant potential of polar and nonpolar extracts of the leaves and flowers of Ocimum basilicum and the in-silico mode of interaction between these enzymes and the major chemical constituents of the herb. The hexane extract (HE) and hydro-ethanolic extract (EE) obtained sequentially were used to estimate PPA and PPL inhibitory and antioxidant activities, total phenolic content (TPC) and total flavonoid content (TFC). Chemical constituents of the essential oils and HE were determined by GC-MS (Gas Chromatography-Mass Spectrometry). For PPA inhibition, IC₅₀ (µg/mL) of the extracts were 0.27-0.37, which were close to 0.24 of acarbose, while for PPL inhibition, IC₅₀ (µg/mL) of the extracts were 278.40-399.65, and that of Orlistat 145.72. The flowers EE was most potent antioxidant followed by leaves EE. The leaves EE had highest TPC and TFC followed of flowers EE. The essential oil of flowers had higher estragole (55%) than linalool (37%), while the essential oil of the leaves had higher linalool (42%) than estragole (38%). The HE of the flowers contained higher estragole (42%) than linalool (23%), while of the HE of the leaves too had higher estragole (65%) than linalool (18%). The in-silico molecular docking study showed linalool and estragole to have considerable PPA and PPL binding potential, which were further investigated through molecular dynamics simulations and binding free energy calculations. The PPA and PPL inhibitory activities of O. basilicum extracts and their notable antioxidant potential propose the herb as a multi-target complimentary medicine for diabetes, obesity and oxidative stress.

Computational Drug Design against Ebola Virus Targeting Viral Matrix Protein VP30

Ebola viral disease (EVD) is a deadly infectious hemorrhagic viral fever caused by the Ebola virus with a high mortality rate. Until date, there is no effective drug or vaccination available to combat this condition. This study focuses on designing an effective antiviral drug for Ebola viral disease targeting viral protein 30 (VP30) of Ebola virus, highly required for transcription initiation. The lead molecules were screened for Lipinski rule of five, ADMET study following which molecular docking and bioactivity prediction was carried out. The compounds with the least binding energy were analyzed using interaction software. The results revealed that 6-Hydroxyluteolin and (-)-Arctigenin represent active lead compounds that inhibit the activity of VP30 protein and exhibits efficient pharmacokinetics. Both these compounds are plant-derived flavonoids and possess no known adverse effects on human health. In addition, they bind strongly to the predicted binding site centered on Lys180, suggesting that these two lead molecules can be imperative in designing a potential drug for EVD.

Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints

Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learning methodology, it is becoming more tractable to elucidate the structural basis of drug potency. However, the applicability of machine learning models to drug design is limited by the interpretability of the resulting models in terms of feature importance. Here, we take advantage of the large number of available inhibitor-bound HIV-1 protease structures and associated potencies to evaluate inhibitor diversity and machine learning models to predict ligand affinity. First, using a hierarchical clustering approach, we grouped HIV-1 protease inhibitors and identified distinct core structures. Explicit features including protein-ligand interactions were extracted from high-resolution cocrystal structures as 3D-based fingerprints. We found that a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy. Finally, Shapley values were derived to explain local feature importance. We found specific van der Waals (vdW) interactions of key protein residues are pivotal for the predicted potency. Protein-specific and interpretable prediction models can guide the optimization of many small molecule drugs for improved potency.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Rubrofusarin as a Dual Protein Tyrosine Phosphate 1B and Human Monoamine Oxidase-A Inhibitor: An in Vitro and in Silico Study

A number of nature-derived biologically active compounds comprise glycosides. In some cases, the glycosidic residue is needed for bioactivity; however, in other cases, glycosylation just improves some pharmacokinetic/dynamic parameters. The patterns of protein tyrosine phosphatase 1B (PTP1B) and human monoamine oxidase A (hMAO-A) inhibition by rubrofusarin 6-O-β-d-glucopyranoside (1), rubrofusarin 6-O-β-d-gentiobioside (2), rubrofusarin triglucoside (3), and cassiaside B2 (4) were compared with the aglycone, rubrofusarin, isolated from Cassia obtusifolia seeds. Rubrofusarin showed potent inhibition against the PTP1B enzyme (IC₅₀; 16.95 ± 0.49 μM), and its glycosides reduced activity (IC₅₀; 87.36 ± 1.08 μM for 1 and >100 μM for 2-4) than did the reference drug, ursolic acid (IC₅₀; 2.29 ± 0.04 μM). Similarly, in hMAO-A inhibition, rubrofusarin displayed the most potent activity with an IC₅₀ value of 5.90 ± 0.99 μM, which was twice better than the reference drug, deprenyl HCl (IC₅₀; 10.23 ± 0.82 μM). An enzyme kinetic and molecular docking study revealed rubrofusarin to be a mixed-competitive inhibitor of both these enzymes. In a western blot analysis, rubrofusarin increased glucose uptake significantly and decreased the PTP1B expression in a dose-dependent manner in insulin-resistant HepG2 cells, increased the expression of phosphorylated protein kinase B (p-Akt) and phosphorylated insulin receptor substrate-1 (p-IRS1) (Tyr 895), and decreased the expression of glucose-6-phosphatase (G6Pase) and phosphoenol pyruvate carboxykinase (PEPCK), key enzymes of gluconeogenesis. Our overall results show that glycosylation retards activity; however, it reduces toxicity. Thus, Cassia seed as functional food and rubrofusarin as a base can be used for the development of therapeutic agents against comorbid diabetes and depression.

Structural and energetic evolution of fibrinogen toward to the betablocker interactions

We present a computational analysis coupled with experimental studies, focusing on the binding-interaction between beta-adrenoreceptor blocking agents (acebutolol and propranolol) with fibrinogen protein (E-region). Herein, computational modeling on structural validation and flexibility properties of fibrinogen E-region showed that the E-region interacting residues, which form the funnel-shaped hydrophobic cavity for ligand-binding, can be efficiently modeled. The obtained free energy of binding (FEB) values for the docking complexes, namely acebutolol/fibrinogen E-region and propranolol/fibrinogen E-region, were very close and amounted to - 6.9 kcal/mol and - 6.8 kcal/mol, respectively. They were supported by a high binding-accuracy (R.M.S.D < 2 Å) for the best crystallographic binding-poses in both cases. In this regard, we identify a docking-mechanism of interaction for the propranolol and acebutolol mainly based on non-covalent hydrophobic contacts with the fibrinogen E-region binding-site. Besides, the beta-adrenoreceptor blocking agents are able to induce local perturbations affecting particularly the fibrinogen E-region allosteric residues linked to significant changes in the inter-residue communication and flexibility properties of residue network. In this sense, we show that the key biophysical parameters like frequency and collectivity degree may be compromised in different ways by the interaction with acebutolol and propranolol. Isothermal titration calorimetry, zeta potential and small angle X-ray scattering (SAXS) measurements were performed to complete and corroborate computational analysis. The combined experimental results point out that acebutolol acts to a lesser extent to fibrinogen structure than propranolol.

Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds

Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.

DNA minor groove binding of a well known anti-mycobacterial drug dapsone: A spectroscopic, viscometric and molecular docking study

Dapsone is a sulfone drug mainly used as anti-microbial and anti-inflammatory agent for the treatment of various diseases including leprosy. Recently, its interaction with protein (bovine serum albumin) is evidenced. But, the binding propensity of this anti-mycobacterial drug towards DNA is still unknown. Also, the mode of dapsone-DNA interaction (if any) is still an unknown quantity. In this study, we have taken a thorough attempt to understand these two unknown aspects using various biophysical and in silico molecular docking techniques. Both UV-visible and fluorescence titrimetric studies indicated that dapsone binds to CT-DNA with a binding constant in order of 10⁴ M^-1. Circular dichroism, thermal denaturation and viscosity experiments revealed that dapsone binds to the grooves of CT-DNA. Competitive DNA binding studies clearly indicated the minor groove binding property of this anti-mycobacterial drug. Molecular docking provided detailed information about the formation of hydrogen bonding in the dapsone-DNA complex. This in silico study further revealed that dapsone binds to the AT-rich region of the minor groove of DNA having a relative binding energy of -6.22 kcal mol^-1. Overall, all these findings evolved from this study can be used for better understanding the medicinal importance of dapsone.

Molecular Docking Studies of Methamphetamine and Amphetamine- Related Derivatives as an Inhibitor against Dopamine Receptor

BACKGROUND

The catecholamines such as dopamine, norepinephrine, and epinephrine are neurotransmitters that regulate different physiological functions of the central nervous system. Some evidence suggests that the degeneration of dopamine neurons in the substantia nigra contributes to Parkinson's Disease (PD), which is a neurodegenerative disorder and it is responsible for the major symptoms of PD. It is suggested that replenishment of striatal dopamine through the oral administration of the dopamine precursor, levodopa, can compensate for the lack of endogenously produced dopamine. Some studies have shown competitive inhibition of dopamine receptor such as methamphetamine, and other amphetamine-related derivatives, which block dopamine receptor activity to uptake dopamine.

METHODS

In this study, 3D structures of amphetamine, methamphetamine, cocaine, methylphenidate, cathinone, MDMA, and mephedrone were obtained from the PubChem database, which has reported some evidence about their inhibitory effect with dopamine receptor. Then, these structures were provided for molecular docking analysis by Autodock Vina software. Eventually, the binding energies between docked dopamine receptor and them were calculated and their interactions were prognosticated.

RESULTS

Our results indicated that all chemicals can interact with dopamine receptor molecule in the active site of dopamine and the minimum binding energies belong to Cocaine and Methylphenidate with -7.9 Kcal/mol and -7.2 Kcal/mol, respectively.

CONCLUSION

It might be concluded that amphetamine, methamphetamine, cocaine, methylphenidate, cathinone, MDMA, and mephedrone could act as potential inhibitors of DA receptor for dopamine uptake, which could cause degenerative disorders.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

GroEL/ES mediated the in vivo recovery of TRAIL inclusion bodies in Escherichia coli

Inclusion body (IB) formation generates substantial bio-waste in the pharmaceutical industry and remains a major challenge for heterologous protein expression. Although chaperones can be co-expressed to improve soluble protein yield, their contribution to IB processing in vivo has not been thoroughly studied. Here, a GroEL-GroES co-expressing strain and a deficient strain were constructed to study the in vivo recovery of recombinant human tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). The interaction between GroEL/ES and TRAIL was simulated by molecular docking and identified by co-immunoprecipitation. The in vitro cytotoxicity of TRAIL IBs before and after in vivo recovery was subsequently determined by MTT assay. Additionally, IB structures were measured by Fourier transform infrared (FT-IR) spectroscopy and fluorescence spectroscopy. The results showed that after in vivo refolding, IBs retained lower levels of anti-tumor activity and fewer native-like β-sheet structures. Fewer recoverable polypeptides were trapped in IBs after GroEL/ES co-expression and refolding in vivo. Therefore, GroEL/ES mediated the in vivo recovery of TRAIL IBs in Escherichia coli. These results may identify potential uses for IBs and provide additional insight into the detailed mechanisms of in vivo protein recovery.

The Inclusion of Water Molecules in Residue Interaction Networks Identifies Additional Central Residues

The relevance of water molecules for the recognition and the interaction of biomolecules is widely appreciated. In this paper we address the role that water molecules associated to protein complexes play for the functional relevance of residues by considering their residue interaction networks (RINs). These are commonly defined on the basis of the amino acid composition of the proteins themselves, disregarding the solvation state of the protein. We determine properties of the RINs of two protein complexes, colicin E2/Im2 and barnase/barstar, with and without associated water molecules, using a previously developed methodology and its associated application RINspector. We find that the inclusion of water molecules in RINs leads to an increase in the number of central residues which adds a novel mechanism to the relevance of water molecules for protein function.

Computational characterization of the binding mode between oncoprotein Ets-1 and DNA-repair enzymes

The Ets-1 oncoprotein is a transcription factor that promotes target gene expression in specific biological processes. Typically, Ets-1 activity is low in healthy cells, but elevated levels of expression have been found in cancerous cells, specifically related to tumor progression. Like the vast majority of the cellular effectors, Ets-1 does not act alone but in association with partners. Given the important role that is attributed to Ets-1 in major human diseases, it is crucial to identify its partners and characterize their interactions. In this context, two DNA-repair enzymes, PARP-1 and DNA-PK, have been identified recently as interaction partners of Ets-1. We here identify their binding mode by means of protein docking. The results identify the interacting surface between Ets-1 and the two DNA-repair enzymes centered on the α-helix H1 of the ETS domain, leaving α-helix H3 available to bind DNA. The models highlight a hydrophobic patch on Ets-1 at the center of the interaction interface that includes three tryptophans (Trp338, Trp356, and Trp361). We rationalize the binding mode using a series of computational analyses, including alanine scanning, molecular dynamics simulation, and residue centrality analysis. Our study constitutes a first but important step in the characterization, at the molecular level, of the interaction between an oncoprotein and DNA-repair enzymes.

Structure-Based Discovery and Synthesis of Potential Transketolase Inhibitors

Transketolase (TKL) plays a key role in plant photosynthesis and has been predicted to be a potent herbicide target. Homology modeling and molecular dynamics simulation were used to construct a target protein model. A target-based virtual screening was developed to discover novel potential transketolase inhibitors. Based on the receptor transketolase 1 and a target-based virtual screening combined with structural similarity, six new compounds were selected from the ZINC database. Among the structural leads, a new compound ZINC12007063 was identified as a novel inhibitor of weeds. Two novel series of carboxylic amide derivatives were synthesized, and their structures were rationally identified by NMR and HRMS. Biological evaluation of the herbicidal and antifungal activities indicated that the compounds 4u and 8h were the most potent herbicidal agents, and they also showed potent fungicidal activity with a relatively broad-spectrum. ZINC12007063 was identified as a lead compound of potential transketolase inhibitors, 4u and 8h which has the herbicidal and antifungal activities were synthesized based on ZINC12007063. This study lays a foundation for the discovery of new pesticides.

Flavonoids inhibit cell proliferation and induce apoptosis and autophagy through downregulation of PI3Kγ mediated PI3K/AKT/mTOR/p70S6K/ULK signaling pathway in human breast cancer cells

Anticancer activities of flavonoids derived from Tephroseris kirilowii (Turcz.) Holub. were evaluated in human cancer cells. We isolated and identified, for the first time, eight flavonoids from T. kirilowii and found that three of them (IH: isorhamnetin, GN: genkwanin, and Aca: acacetin) inhibited cell proliferation in a variety of human cancer cell lines. These active flavonoids caused cell cycle arrest at G2/M phase and induced apoptosis and autophagy in human breast cancer cells. Molecular docking revealed that these flavonoids dock in the ATP binding pocket of PI3Kγ. Importantly, treatment with these flavonoids decreased the levels of PI3Kγ-p110, phospho-PI3K, phospho-AKT, phospho-mTOR, phospho-p70S6K, and phospho-ULK. Pretreatment with PI3Kγ specific inhibitor AS605240 potentiated flavonoids-mediated inactivation of AKT, mTOR, p70S6K, ULK, and apoptosis. Taken together, these findings represent a novel mechanism by which downregulation of PI3Kγ-p110 and consequent interruption of PI3K/AKT/mTOR/p70S6K/ULK signaling pathway might play a critical functional role in these flavonoids-induced cell cycle arrest at G2/M phase, apoptosis, and autophagy. Our studies provide novel insights into the anticancer activities of selected flavonoids and their potential uses in anticancer therapy.

Synergism of Plant Compound With Traditional Antimicrobials Against Streptococcus spp. Isolated From Bovine Mastitis

Mastitis is an inflammation of the mammary gland that causes major losses in the dairy industry. Streptococcus spp. are among the main agents of this disease. Increased resistance to antibiotics is one of the causes of therapeutic failure. Plants, due to their broad chemodiversity, are an interesting source of new molecules with antibacterial activity. Using these compounds along with traditional antibiotics is a possible method for reversing resistance. The objective of this work was to determine the interactions between the activities of guttiferone-A and 7-epiclusianone, two active substances isolated from the fruits of Garcinia brasiliensis, and traditional antibiotics against Streptococcus spp. isolated from bovine mastitis and known to be resistant to them. First, the MIC for the antibiotics and bioactive compounds was determined, followed by their activities, alone and in combination. Then, their cytotoxicity was measured in bovine mammary epithelial cells. Finally, molecular docking simulations were performed to elucidate molecular details of the interactions between β-lactamase and the compounds binding to it (clavulanic acid, ampicillin, 7-epiclusianone, and guttiferone-A). The bacterial isolates were resistant to ampicillin and gentamicin. Both antibiotics showed predominantly synergistic antibacterial activities in combination with guttiferone-A or 7-epiclusianone. These two active substances were not cytotoxic at synergistic concentrations and both showed strong binding to β-lactamase, which may explain the reversal of ampicillin resistance. These substances are promising for the treatment of bovine mastitis.

Inhibition of Human Immunodeficiency Type 1 Virus (HIV-1) Life Cycle by Different Egg White Lysozymes

Lysozyme is a relatively small enzyme with different biological activities, which is found in tears, saliva, egg white, and human milk. In the study, the anti-HIV-1 activity of lysozymes purified from quail, Meleagris, and hen egg white has been determined. For this end, a time-of-drug-addition assay was performed to identify the target of anti-HIV-1 agents and for determination of probable anti HIV-1 mechanism of the studied lysozyme, the binding affinity of the lysozymes to the human CD4 receptor was studied by molecular docking method. To define structural differences between studied lysozymes, structural motifs of them were predicted by MEME tool. Quail, hen, and Meleagris lysozymes showed potent anti-HIV-1 activity with EC₅₀ of 7.5, 10, and 55 nM, respectively. The time-of-drug-addition study demonstrated that the inhibitory effect of all purified lysozymes is before HIV-1 infection. The frequency and intensity of CD4 expression in PBMCs decreased in the presence of all mentioned lysozymes. Also, the expression level of C-C chemokine receptor type 5 (CCR5) and chemokine receptor type 4 (CXCR4) on CD4+ T cells was not changed in cells treated with these lysozymes. The results of in silico study confirmed that the binding energy of quail lysozyme with CD4 was more than that of other studied lysozymes. The results revealed that these lysozymes restrict HIV-1 attachment to host cell CD4.

Design of Drugs by Filtering Through ADMET, Physicochemical and Ligand-Target Flexibility Properties

There is a synergistic interaction between medicinal chemistry, chemoinformatics, and bioinformatics. The last one includes analyses of sequences as well as structural analysis which employ computational techniques such as docking studies and molecular dynamics (MD) simulations. Over the last years these techniques have allowed the development of new accurate computational tools for drug design. As a result, there have been an increased number of publications where computational methods such as pharmacophore modeling, de novo drug design, evaluation of physicochemical properties, and analysis of ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties have been quite useful for eliminating the compounds with poor physicochemical or toxicological properties. Furthermore, using MD simulations and docking analysis, it is possible to estimate the binding energy of the protein-ligand complexes by using scoring functions, as well as to structurally depict the binding pose of the compounds on proteins, in order to select the best evaluated compounds for subsequent synthetizing and evaluation through biological assays. In this work, we describe some computational tools that have been used for structure-based drug design of new compounds that target histone deacetylases (HDACs), which are known to be potential targets in cancer and parasitic diseases.

Computational and biological evidences on the serotonergic involvement of SeTACN antidepressant-like effect in mice

A series of phenylselanyl-1H-1,2,3-triazole-4-carbonitriles with different substituents were screened for their binding affinity with serotonin transporter (SERT) and dopamine transporter (DAT) by docking molecular. 5-(4methoxyphenyl)-1-(2-(phenylselanyl)phenyl)-1H-1,2,3-triazole-4-carbonitrile (SeTACN) exhibited the best conformation with SERT even higher than fluoxetine and serotonin, suggesting a competitive inhibition. SeTACN demonstrated additional affinity to other serotonergic receptors involved in antidepressant effects: 5HT_1a, 5HT_2a and 5HT₃. In another set of experiments, SeTACN led to significant reductions in the immobility time of mice submitted to forced swimming test (FST) in the dose range of 0.1- 20mg/kg, suggesting an antidepressant-like effect. The possible mechanism of action was investigated using serotonergic and dopaminergic antagonists. The antidepressant-like effect of SeTACN (0.1mg/kg i.g.) was prevented by the pretreatment with WAY100635 (a selective 5HT_1a antagonist), ketanserin (a 5HT2_a/c antagonist) and ondansetron (a selective 5ht₃ antagonist), PCPA (an inhibitor of serotonin synthesis) but not with SCH23390 (dopaminergic D₁ antagonist) and sulpiride (D₂ antagonist). Sub-effective dose of fluoxetine was able to potentiate the effects of a sub-effective dose of SeTACN in FST. None of the treatments affected locomotor activity in open field test (OFT). These results together, suggest that the SeTACN antidepressant-like effect is mediate, at least in parts, by serotonergic system.

Binding Space Concept: A New Approach To Enhance the Reliability of Docking Scores and Its Application to Predicting Butyrylcholinesterase Hydrolytic Activity

Docking simulations are very popular approaches able to assess the capacity of a given ligand to interact with a target. Docking simulations are usually focused on a single best complex even though many studies showed that ligands retain a significant mobility within a binding pocket by assuming different binding modes all of which may contribute to the monitored ligand affinity. The present study describes an innovative concept, the binding space, which allows an exploration of the ligand mobility within the binding pocket by simultaneously considering several ligand poses as generated by docking simulations. The multiple poses and the relative docking scores can then be analyzed by taking advantage of the same concepts already used in the property space analysis; hence the binding space can be parametrized by (a) mean scores, (b) score ranges, and (c) score sensitivity values. The first parameter represents a very simple procedure to account for the contribution of the often neglected alternative binding modes, while the last two descriptors encode the degree of mobility which a given ligand retains within the binding cavity (score range) as well as the ease with which a ligand explores such a mobility (score sensitivity). Here, the binding space concept is applied to the prediction of the hydrolytic activity of BChE by synergistically considering multiple poses and multiple protein structures. The obtained results shed light on the remarkable potential of the binding space concept, whose parameters allow a significant increase of the predictive power of the docking results as revealed by extended correlative analyses. Mean scores are the parameters affording the largest statistical improvement, and all the here proposed docking-based descriptors show enhancing effects in developing predictive models. Finally, the study describes a new score function (Contacts score) simply based on the number of surrounding residues which appears to be particularly productive in the framework of the binding space.

Identification of inhibitors against α-Isopropylmalate Synthase of Mycobacterium tuberculosis using docking-MM/PBSA hybrid approach

α-Isopropylmalate Synthase (α-IPMS) encoded by leuA in Mycobacterium tuberculosis (M.tb) is involved in the leucine biosynthesis pathway and is extremely critical for the synthesis of branched-chain amino acids (leucine, isoleucine and valine). α-IPMS activity is required not only for the proliferation of M.tb but is also indispensable for its survival during the latent phase of infection. It is absent in humans and is widely regarded as one of the validated drug targets against Tuberculosis (TB). Despite its essentiality, any study on designing of potential chemical inhibitors against α-IPMS has not been reported so far. In the present study, in silico identification of putative inhibitors against α-IPMS exploring three chemical databases i.e. NCI, DrugBank and ChEMBL is reported through structurebased drug design and filtering of ligands based on the pharmacophore feature of the actives. In the absence of experimental results of any inhibitor against α-IPMS, a stringent validation of docking results is done by comparing with molecular mechanics/Poisson- Boltzmann surface area (MM/PBSA) calculations by investigating two more proteins for which experimental results are known.

Characterizing the Hot Spots Involved in RON-MSPβ Complex Formation Using In Silico Alanine Scanning Mutagenesis and Molecular Dynamics Simulation

Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d'Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in protein-protein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln¹⁹³, Arg²²⁰, Glu²⁸⁷, Pro²⁸⁸, Glu²⁸⁹, and His⁴²⁴ residues from RON and Arg⁵²¹, His⁵²⁸, Ser⁵⁶⁵, Glu⁶⁵⁸, and Arg⁶⁸³ from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required, and the design of experimental site directed mutagenesis studies.

EGFR gene regulation in colorectal cancer cells by garlic phytocompounds with special emphasis on S-Allyl-L-Cysteine Sulfoxide

Colorectal cancer is one among the most common cancers in the world and a major cause of cancer related deaths. Similar to other cancers, colorectal carcinogenesis is often associated with over expression of genes related to cell growth and proliferation, especially Epidermal Growth Factor Receptor (EGFR). There is an increasing attention towards the plant derived compounds in prevention of colorectal carcinogenesis by downregulating EGFR. Among plants, garlic (Allium sativum L.) is emerging with anticancer properties by virtue of its organosulfur compounds. The present study was aimed to analyze the interaction ability of garlic compounds in the active region of EGFR gene by in silico molecular docking studies and in vitro validation. This was conducted using the Discovery studio software version 4.0. Among the tested compounds, s-allyl-l-cysteine-sulfoxide (SACS)/alliin showed higher affinity towards EGFR. Furthermore, wet lab analysis using cell viability test and EGFR expression analysis in colorectal cancer cells confirmed its efficacy as a potent anticancer agent.