Comparing protein-ligand docking programs is difficult

Novel 3,6-Disubstituted Pyridazine Derivatives Targeting JNK1 Pathway: Scaffold Hopping and Hybridization-Based Design, Synthesis, Molecular Modeling, and In Vitro and In Vivo Anticancer Evaluation

A series of novel 3,6-disubstituted pyridazine derivatives were designed, synthesized, and biologically evaluated as preclinical anticancer candidates. Compound 9e exhibited the highest growth inhibition against most of the NCI-60 cancer cell lines. The in vivo anticancer activity of 9e was subsequently investigated at two dose levels using the Ehrlich ascites carcinoma solid tumor animal model, where a reduction in the mean tumor volume allied with necrosis induction was reported without any signs of toxicity in the treated groups. Interestingly, compound 9e was capable of downregulating c-jun N-terminal kinase-1 (JNK1) gene expression and curbing the protein levels of its phosphorylated form, in parallel with a reduction in its downstream targets, namely, c-Jun and c-Fos in tumors, along with restoring p53 activity. Furthermore, molecular docking and dynamics simulations were carried out to predict the binding mode of 9e and prove its stability in the JNK1 binding pocket.

GDP-mannose pyrophosphorylase is an efficient target in Xanthomonas citri for citrus canker control

Xanthomonas citri subsp. citri (Xcc) is a bacterium that causes citrus canker, an economically important disease that results in premature fruit drop and reduced yield of fresh fruit. In this study, we demonstrated the involvement of XanB, an enzyme with phosphomannose isomerase (PMI) and guanosine diphosphate-mannose pyrophosphorylase (GMP) activities, in Xcc pathogenicity. Additionally, we found that XanB inhibitors protect the host against Xcc infection. Besides being deficient in motility, biofilm production, and ultraviolet resistance, the xanB deletion mutant was unable to cause disease, whereas xanB complementation restored wild-type phenotypes. XanB homology modeling allowed in silico virtual screening of inhibitors from databases, three of them being suitable in terms of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, which inhibited GMP (but not PMI) activity of the Xcc recombinant XanB protein in more than 50%. Inhibitors reduced citrus canker severity up to 95%, similarly to copper-based treatment. xanB is essential for Xcc pathogenicity, and XanB inhibitors can be used for the citrus canker control.

IMPORTANCE

Xcc causes citrus canker, a threat to citrus production, which has been managed with copper, being required a more sustainable alternative for the disease control. XanB was previously found on the surface of Xcc, interacting with the host and displaying PMI and GMP activities. We demonstrated by xanB deletion and complementation that GMP activity plays a critical role in Xcc pathogenicity, particularly in biofilm formation. XanB homology modeling was performed, and in silico virtual screening led to carbohydrate-derived compounds able to inhibit XanB activity and reduce disease symptoms by 95%. XanB emerges as a promising target for drug design for control of citrus canker and other economically important diseases caused by Xanthomonas sp.

Expression analysis and mapping of Viral-Host Protein interactions of Poxviridae suggests a lead candidate molecule targeting Mpox

BACKGROUND

Monkeypox (Mpox) is an important human pathogen without etiological treatment. A viral-host interactome study may advance our understanding of molecular pathogenesis and lead to the discovery of suitable therapeutic targets.

METHODS

GEO Expression datasets characterizing mRNA profile changes in different host responses to poxviruses were analyzed for shared pathway identification, and then, the Protein-protein interaction (PPI) maps were built. The viral gene expression datasets of Monkeypox virus (MPXV) and Vaccinia virus (VACV) were used to identify the significant viral genes and further investigated for their binding to the library of targeting molecules.

RESULTS

Infection with MPXV interferes with various cellular pathways, including interleukin and MAPK signaling. While most host differentially expressed genes (DEGs) are predominantly downregulated upon infection, marked enrichments in histone modifiers and immune-related genes were observed. PPI analysis revealed a set of novel virus-specific protein interactions for the genes in the above functional clusters. The viral DEGs exhibited variable expression patterns in three studied cell types: primary human monocytes, primary human fibroblast, and HeLa, resulting in 118 commonly deregulated proteins. Poxvirus proteins C6R derived protein K7 and K7R of MPXV and VACV were prioritized as targets for potential therapeutic interventions based on their histone-regulating and immunosuppressive properties. In the computational docking and Molecular Dynamics (MD) experiments, these proteins were shown to bind the candidate small molecule S3I-201, which was further prioritized for lead development.

RESULTS

MPXV circumvents cellular antiviral defenses by engaging histone modification and immune evasion strategies. C6R-derived protein K7 binding candidate molecule S3I-201 is a priority promising candidate for treating Mpox.

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. The PoseBusters test suite validates chemical and geometric consistency of a ligand including its stereochemistry, and the physical plausibility of intra- and intermolecular measurements such as the planarity of aromatic rings, standard bond lengths, and protein-ligand clashes. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.

Binding order of substrate and cofactor in sulfonamide monooxygenase during sulfa drug degradation: in silico studies

For decades, sulfonamide antibiotics have been used across industries such as agriculture and animal husbandry. However, the use and inadvertent misuse of these antibiotics have resulted in the advent of sulfonamide-drug-resistant strains due to antibiotic pollution. Enzymatic bioremediation of antibiotics remains a potential emerging solution to combat antibiotic pollution. Here, we propose an enzymatic model for the degradation of sulfonamides by Microbacterium sp. We have employed a multi-pronged computational strategy involving - protein structure modelling, ligand docking and molecular dynamics simulations to decipher a plausible binding order for the enzymatic degradation of sulfonamides by the bacterial sulfonamide monooxygenase, SulX. Our results enable us to predict that this degradation is achieved through the sequential binding of the antibiotic sulfonamide followed by the reduced flavin cofactor FMNH2, thereby laying the computational foundation for further advancements in enzyme-mediated degradation of the antibiotic. We also provide a list of experiments which may be performed to verify and follow-up on our in-silico studies.Communicated by Ramaswamy H. Sarma.

In silico and in vitro studies: investigating the chemical composition, DFT, molecular docking, and dynamic simulation of Satureja candidissima (Munby) Briq essential oil as a potential antibacterial agent

This study aimed to investigate the chemical composition and antibacterial properties of the essential oil (EO) derived from the aerial parts of Satureja candidissima (Munby) Briq (SC), as well as the mechanisms of interaction between SCEO chemical components and target proteins related to antibacterial activity mechanisms using a molecular docking approach, and for more accuracy molecular dynamic simulation and DFT calculations were carried out. The GC-MS technique was used to analyze the chemical composition of SCEO. The results showed that SCEO contained various chemical compounds, with pulegone being identified as the major component (53.26%). The results also indicated the presence of (+)-menthone (11.02%), borneol (4.43%), 2-cyclohexen-1-one, 3-methyl-6-(1-methylethylidene) (2.50%), and 3-octanol (2.09%). The study revealed that the SCEO displayed antibacterial activity against all tested gram-positive bacteria. To further understand the mechanism behind its antibacterial activity, in silico molecular docking studies were performed. The results indicated that the antibacterial effect of SCEO compounds could be due to the combination with enoyl-[acyl-carrier-protein] reductase [NADPH] FabI (PDB ID: 4ALL) in a variety of ways. The molecular dynamics simulation analysis yielded favorable outcomes for the docked complex involving 1H-cycloprop[e]azulen-7-ol, decahydro-1,1,7-trimethyl-4-methylene, and 1,4,7-tetramethyldecahydro-1H-cyclopropa[e]azulen-4-ol with enoyl-[acyl-carrier-protein] reductase [NADPH]. Geometry optimization, coupled with Density Functional Theory (DFT), can be employed to assess the importance of quantum chemical descriptors in elucidating potential antibacterial activity. Quantum descriptors were computed based on EHOMO and ELUMO. The results of this study provide important insights into the potential use of Satureja candidissima (Munby) Briq EO as antibacterial agent.Communicated by Ramaswamy H. Sarma.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

In Silico Identification of a Potential TNF-Alpha Binder Using a Structural Similarity: A Potential Drug Repurposing Approach to the Management of Alzheimer's Disease

Introduction

Alzheimer's disease (AD) is a neurodegenerative disorder with no conclusive remedy. Yohimbine, found in Rauwolfia vomitoria, may reduce brain inflammation by targeting tumour necrosis factor-alpha (TNFα), implicated in AD pathogenesis. Metoserpate, a synthetic compound, may inhibit TNFα. The study is aimed at assessing the potential utility of repurposing metoserpate for TNFα inhibition to reduce neuronal damage and inflammation in AD. The development of safe and effective treatments for AD is crucial to address the growing burden of the disease, which is projected to double over the next two decades.

Methods

Our study repurposed an FDA-approved drug as TNFα inhibitor for AD management using structural similarity studies, molecular docking, and molecular dynamics simulations. Yohimbine was used as a reference compound. Molecular docking used SeeSAR, and molecular dynamics simulation used GROMACS.

Results

Metoserpate was selected from 10 compounds similar to yohimbine based on pharmacokinetic properties and FDA approval status. Molecular docking and simulation studies showed a stable interaction between metoserpate and TNFα over 100 ns (100000 ps). This suggests a reliable and robust interaction between the protein and ligand, supporting the potential utility of repurposing metoserpate for TNFα inhibition in AD treatment.

Conclusion

Our study has identified metoserpate, a previously FDA-approved antihypertensive agent, as a promising candidate for inhibiting TNFα in the management of AD.

Potential Utilization of Phenolic Acid Compounds as Anti-Inflammatory Agents through TNF-α Convertase Inhibition Mechanisms: A Network Pharmacology, Docking, and Molecular Dynamics Approach

Inflammation is a dysregulated immune response characterized by an excessive release of proinflammatory mediators, such as cytokines and prostanoids, leading to tissue damage and various pathological conditions. Natural compounds, notably phenolic acid phytocompounds from plants, have recently garnered substantial interest as potential therapeutic agents to bolster well-being and combat inflammation recently. Based on previous research, the precise molecular mechanism underlying the anti-inflammatory activity of phenolic acids remains elusive. Therefore, this study aimed to predict the molecular mechanisms underpinning the anti-inflammatory properties of selected phenolic acid phytocompounds through comprehensive network pharmacology, molecular docking, and dynamic simulations. Network pharmacology analysis successfully identified TNF-α convertase as a potential target for anti-inflammatory purposes. Among tested compounds, chlorogenic acid (-6.90 kcal/mol), rosmarinic acid (-6.82 kcal/mol), and ellagic acid (-5.46 kcal/mol) exhibited the strongest binding affinity toward TNF-α convertase. Furthermore, phenolic acid compounds demonstrated molecular binding poses similar to those of the native ligand, indicating their potential as inhibitors of TNF-α convertase. This study provides valuable insights into the molecular mechanisms that drive the anti-inflammatory effects of phenolic compounds, particularly through the suppression of TNF-α production via TNF-α convertase inhibition, thus reinforcing their anti-inflammatory attributes.

Anti-Trypanosoma cruzi Potential of Vestitol Isolated from Lyophilized Red Propolis

Chagas disease (CD) is a worldwide public health problem, and the drugs available for its treatment have severe limitations. Red propolis is a natural extract known for its high content of phenolic compounds and for having activity against T. cruzi. The aim of this study was to investigate the trypanocidal potential of red propolis to isolate, identify, and indicate the mode of action of the bioactive compounds. The results revealed that the total phenolic content was 15.4 mg GAE/g, and flavonoids were 7.2 mg QE/g. The extract was fractionated through liquid-liquid partitioning, and the trypanocidal potential of the samples was evaluated using the epimastigote forms of the Y strain of T. cruzi. In this process, one compound was characterized by MS, 1H, and 13C NMR and identified as vestitol. Cytotoxicity was evaluated employing MRC-5 fibroblasts and H9C2 cardiomyocytes, showing cytotoxic concentrations above 15.62 μg/mL and 31.25 μg/mL, respectively. In silico analyses were applied, and the data suggested that the substance had a membrane-permeation-enhancing effect, which was confirmed through an in vitro assay. Finally, a molecular docking analysis revealed a higher affinity of vestitol with farnesyl diphosphate synthase (FPPS). The identified isoflavan appears to be a promising lead compound for further development to treat Chagas disease.

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.

Analgesic Activity of 5-Acetamido-2-Hydroxy Benzoic Acid Derivatives and an In-Vivo and In-Silico Analysis of Their Target Interactions

The design, synthesis, and evaluation of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are big challenges today. In this work, two 5-acetamido-2-hydroxy benzoic acid derivatives were proposed, increasing the alkyl position (methyl) in an acetamide moiety, and synthesized, and their structural elucidation was performed using 1H NMR and 13C NMR. The changes in methyl in larger groups such as phenyl and benzyl aim to increase their selectivity over cyclooxygenase 2 (COX-2). These 5-acetamido-2-hydroxy benzoic acid derivatives were prepared using classic methods of acylation reactions with anhydride or acyl chloride. Pharmacokinetics and toxicological properties were predicted using computational tools, and their binding affinity (kcal/mol) with COX-2 receptors (Mus musculus and Homo sapiens) was analyzed using docking studies (PDB ID 4PH9, 5KIR, 1PXX and 5F1A). An in-silico study showed that 5-acetamido-2-hydroxy benzoic acid derivates have a better bioavailability and binding affinity with the COX-2 receptor, and in-vivo anti-nociceptive activity was investigated by means of a writhing test induced by acetic acid and a hot plate. PS3, at doses of 20 and 50 mg/kg, reduced painful activity by 74% and 75%, respectively, when compared to the control group (20 mg/kg). Regarding the anti-nociceptive activity, the benzyl showed reductions in painful activity when compared to acetaminophen and 5-acetamido-2-hydroxy benzoic acid. However, the proposed derivatives are potentially more active than 5-acetamido-2-hydroxy benzoic acid and they support the design of novel and safer derivative candidates. Consequently, more studies need to be conducted to evaluate the different pharmacological actions, the toxicity of possible metabolites that can be generated, and their potential use in inflammation and pain therapy.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

In silico screening of Andrographis paniculata secondary metabolites as anti-diabetes mellitus through PDE9 inhibition

Background and purpose

Andrographis paniculata (AP) has long been used as an anti-diabetic agent, but the mechanism of action and active substance responsible for the anti-diabetic effect, particularly by inhibiting phosphodiesterase-9 (PDE9), which is one of the targets of anti-diabetic medications, have not been reported. The aim of the present study was to identify a new anti-diabetes candidate from secondary metabolite compounds of AP through PDE9 inhibition.

Experimental approach

In order to prepare the chemical structures of the secondary metabolites of AP and PDE9, docking and molecular dynamics simulations were run using Discovery Studio Visualizer, AutoDockTools, AutoDock, and Gromacs, along with a few other supporting software packages.

Findings/Results

Molecular docking simulations showed that two of the 46 secondary metabolites of AP had higher free energies of binding, C00003672 (-11.35 kcal/mol) and C00041378 (-9.27 kcal/mol), than native ligand (-9.23 kcal/mol). The results of molecular dynamics showed that compound C00041378 interacted with TRY484 and PHE516, two active side residues of PDE9. ΔGMMGBSA interactions of PDE9 with C00003672, C00041378, and 49E compounds are 51.69, -56.43, and -48.13 kcal/mol, respectively, as well as ΔGMMPBSA interactions of PDE9 with C00003672, C00041378, and 49E compounds, were -12.26, -16.24, and -11.79 kcal/mol kcal/mol, respectively.

Conclusions and implications

Based on the evaluations of AP secondary metabolites using docking and molecular dynamics simulation, it is suggested that the C00041378 compound has the potential to be an antidiabetic candidate by inhibiting PDE9.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Compounds Isolated from Lawsonia inermis L. Collected in Vietnam and Evaluation of Their Potential Activity Against the Main Protease of SARS-CoV-2 Using In silico Molecular Docking and Molecular Dynamic Simulation

Since late 2019 to early 2020, an outbreak caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has become a worldwide health emergency due to its rapid infection and mortality of millions of people around the world. As the main protease Mpro or 3CLpro produced by the virus plays an important role in coronavirus survival and proliferation, it becomes an excellent drug target to identify COVID-19 inhibitors. Lawsonia inermis L. (henna) is a medicinal plant that has been used for a long time for the treatment of many fungal and bacterial infections. In the search for new anti-COVID agents from medicinal plants, we report the results of our study into the potential inhibition of Mpro by the compounds isolated from the extracts of L. inermis roots and leaves using molecular docking and molecular dynamics simulation. The molecular modeling results showed that all isolated compounds bonded spontaneously into the catalytic pockets of Mpro with binding energies <0. The docking and calculated pharmacokinetic results of the compounds (1-3, 6-8) were similar to and even better than those of the commercial COVID-19 inhibitor remdesivir. In particular, the triterpenoid glycoside suavissimoside R1 (8) showed the best binding to SARS-CoV Mpro, with the lowest binding energy ΔG and IC50,calc. values of −8.19 kcal/mol and 0.98 μM, respectively. Furthermore, the calculations of ADMET (absorption, distribution, metabolism, excretion, and toxicity) showed that it had the lowest toxicity, with a predicted LD50 value of 3320 mg/kg. These triterpenoids are worthy of further study to evaluate their actual bioactivity against SARS-CoV-2 in vitro and in vivo in the hope of contributing valuable scientific data for natural resources for the development novel drug formulations for either the prevention or treatment of COVID-19.

Structural analysis of cannabinoids against EGFR-TK leads a novel target against EGFR-driven cell lines

Epidermal growth factor receptor (EGFR) is a member of the ErbB family of proteins and are involved in downstream signal transduction, plays prominent roles in cell growth regulation, proliferation, and the differentiation of many cell types. They are correlated with the stage and severity of cancer. Therefore, EGFRs are targeted proteins for the design of new drugs to treat cancers that overexpress these proteins. Currently, several bioactive natural extracts are being studied for therapeutic purposes. Cannabis has been reported in many studies to have beneficial medicinal effects, such as anti-inflammatory, analgesic, antibacterial, and anti-inflammatory effects, and antitumor activity. However, it is unclear whether cannabinoids reduce intracellular signaling by inhibiting tyrosine kinase phosphorylation. In this study, cannabinoids (CBD, CBG, and CBN) were simulated for binding to the EGFR-intracellular domain to evaluate the binding energy and binding mode based on molecular docking simulation. The results showed that the binding site was almost always located at the kinase active site. In addition, the compounds were tested for binding affinity and demonstrated their ability to inhibit kinase enzymes. Furthermore, the compounds potently inhibited cellular survival and apoptosis induction in either of the EGFR-overexpressing cell lines.

Pharmacophore-based virtual screening from phytocannabinoids as antagonist r-CB1

Search for new pharmacological alternatives for obesity is based on the design and development of compounds that can aid in weight loss so that they can be used safely and effectively over a long period while maintaining their function. The endocannabinoid system is related to obesity by increasing orexigenic signals and reducing satiety signals. Cannabis sativa is a medicinal plant of polypharmaceutical potential that has been widely studied for various medicinal purposes. The in silico evaluation of their natural cannabinoids (also called phytocannabinoids) for anti-obesity purpose stems from the existence of synthetic cannabinoid compounds that have already presented this result, but which did not guarantee patient safety. In order to find new molecules from C. sativa phytocannabinoids, with the potential to interact peripherally with the pharmacological target cannabinoid receptor 1, a pharmacophore-based virtual screening was performed, including the evaluation of physicochemical, pharmacokinetic, toxicological predictions and molecular docking. The results obtained from the ZINC¹² database pointed to Zinc 69 (ZINC33053402) and Zinc 70 (ZINC19084698) molecules as promising anti-obesity agents. Molecular dynamics (MD) studies disclose that both complexes were stable by analyzing the RMSD (root mean square deviation) values, and the binding free energy values demonstrate that the selected structures can interact and inhibit their catalytic activity.

Molecular Docking of Gallic Acid and Its Derivatives as the Potential nNOS Inhibitors

The global prevalence of anxiety and depression rates have increased by 25% due to the impact of the COVID-19 pandemic. Depression can occur due to an increase in NO produced by the nNOS enzyme. Gallic acid and its derivatives can be obtained from nature and have various biological activities. This study aimed to determine the potential of gallic acid and its derivatives as nNOS inhibitors using the molecular docking method with parameters of binding energy values, RMSD values, and specific binding to amino acid residues. The results showed that gallic acid, 4-O-methyl gallic acid, and epigallocatechin gallate had bond energies of −1.87; −2.36; and −0.12 kcal/mol, respectively. Compared to the standard ligand, which had binding energy of −2.84 kcal/mol, gallic acid 4-O-(6-galloyl glucoside) had binding energy of −4.12 kcal/mol. Based on these results, gallic acid 4-O-(6-galloyl glucoside) can potentially inhibit nNOS.

Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach

Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01−M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.

Dynamic Docking of Macrocycles in Bound and Unbound Protein Structures with DynaDock

Macrocycles are interesting molecules with unique features due to their conformationally constrained yet flexible ring structure. This characteristic poses a difficult challenge for computational modeling studies since they rely on accurate structural descriptions. In particular, molecular docking calculations suffer from the lack of ring flexibility during pose generation, which is often compensated by using pregenerated ligand conformer ensembles. Moreover, receptor structures are mainly treated rigidly, which limits the use of many docking tools. In this study, we optimized our previous molecular dynamics-based sampling and docking pipeline specifically designed for the accurate prediction of macrocyclic compounds. We developed a dihedral classification procedure for in-depth conformational analysis of the macrocyclic rings and extracted structural ensembles that were subsequently docked in both bound and unbound protein structures employing a fully flexible approach. Our results suggest that including a ring conformer close to the bound state in the starting ensemble increases the chance of successful docking. The bioactive conformations of a diverse set of ligands could be predicted with high and decent accuracy in bound and unbound protein structures, respectively, due to the incorporation of full molecular flexibility in our approach. The remaining unsuccessful docking calculations were mainly caused by large flexible substituents that bind to surface-exposed binding sites, rather than the macrocyclic ring per se and could be further improved by explicit molecular dynamics simulations of the docked complex.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CLpro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CLpro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CLpro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Isolation, Characterization, and In Silico Interaction Studies of Bioactive Compounds from Caesalpinia bonducella with Target Proteins Involved in Alzheimer's Disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by memory loss, cognitive deterioration, and neuropsychiatric symptoms. Various drug targets implicated in AD are amyloid beta peptides, cholinesterase enzymes, and anti-amylogenic protein. Medicinal plants derived phytochemical constituents provide a vast pool of diverse compounds as a source of novel drugs. In view of this, the Caesalpinia bonducella seed extract and its active phytoconstituents were used to study the disease-modifying effects in Alzheimer's disease. The present study successfully demonstrated the therapeutic potential of various phytochemicals as it binds to multiple drug targets, resulting in inhibition of acetylcholinesterase (AChE) enzyme, butyrylcholinesterase (BuChE), BACE-1 enzyme, and anti-amylogenic protein as indicated by docking analysis. In conclusion, phytochemicals identified can be used as a suitable lead to developing a molecule that might have multi-targeted directed ligand (MTDL) potential and disease amelioration effects in Alzheimer's disease.

Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem

Despite considerable advances obtained by applying machine learning approaches in protein–ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a solution to this problem, the optimum choice of receptor conformations is still an open question considering the issues related to the computational cost and false positive pose predictions. Here, a combination of ensemble learning and ensemble docking is suggested to rank different conformations of the target protein in light of their importance for the final accuracy of the model. Available X-ray structures of cyclin-dependent kinase 2 (CDK2) in complex with different ligands are used as an initial receptor ensemble, and its redundancy is removed through a graph-based redundancy removal, which is shown to be more efficient and less subjective than clustering-based representative selection methods. A set of ligands with available experimental affinity are docked to this nonredundant receptor ensemble, and the energetic features of the best scored poses are used in an ensemble learning procedure based on the random forest method. The importance of receptors is obtained through feature selection measures, and it is shown that a few of the most important conformations are sufficient to reach 1 kcal/mol accuracy in affinity prediction with considerable improvement of the early enrichment power of the models compared to the different ensemble docking without learning strategies. A clear strategy has been provided in which machine learning selects the most important experimental conformers of the receptor among a large set of protein–ligand complexes while simultaneously maintaining the final accuracy of affinity predictions at the highest level possible for available data. Our results could be informative for future attempts to design receptor-specific docking-rescoring strategies.

Potential implications of vouacapan compounds for insecticidal activity: an in silico study

BACKGROUND

From the fruits and seeds of the species of Pterodon, it is possible to obtain two main products: the essential oil and oleoresin. In oleoresin, numerous vouacapan compounds have been demonstrated to have biological potential, including insecticidal activity.

OBJECTIVE

In silico studies were performed to identify potential candidates for natural insecticides among the vouacapans present in the genus Pterodon.

MATERIALS AND METHODS

Molecular docking and molecular dynamics studies were performed to analyze the interaction of vouacapan compounds with acetylcholinesterase of Drosophila melanogaster. Pharmacokinetic parameters regarding physicochemical properties, plasma protein binding, and activity in the central nervous system were evaluated. The toxicological properties of the selected molecules were predicted using Malathion as the reference compound.

RESULTS

6α,7β-dimethoxivouacapan-17-ene (15) showed a high number of interactions and scores in molecular docking studies. This result suggests that this compound exhibits an inhibitory activity of the enzyme acetylcholinesterase. Regarding physicochemical properties, this compound showed the best results, besides presenting low cutaneous permeability values, suggesting null absorption. Molecular dynamics studies demonstrated few conformational changes in the structure of the complex formed by compound 4 and acetylcholinesterase enzyme throughout the simulation time.

CONCLUSION

It was determined that compound 4 (vouacapan 6α,7β,17β,19-tetraol) is an excellent candidate for usage as a natural insecticide.

In silico Study of Acetylcholinesterase and Beta-secretase Inhibitors: Potential Multitarget Anti-Alzheimer's Agents

BACKGROUND

Alzheimer's disease is a progressive neurodegenerative process with multifactorial characteristics. This disease follows the natural aging process, affecting mainly people over 65 years. Pharmacotherapeutic treatment currently combats symptoms related to cognitive function. Several targets have begun to attract the interest of the scientific community to develop new drug candidates which have better pharmacokinetic and lower toxicity parameters.

OBJECTIVE

The present study aims to design new candidates for acetylcholinesterase/β-secretase (AChE/BACE1) multitarget inhibitor drugs.

METHODS

17 natural products were selected from the literature with anticholinesterase activity and 1 synthetic molecule with inhibitory activity for BACE1. Subsequently, the molecular docking study was performed, followed by the derivation of the pharmacophoric pattern and prediction of pharmacokinetic and toxicological properties. Finally, the hybrid prototype was designed.

RESULTS

All selected molecules showed interactions with their respective target enzymes. Derivation of the pharmacophoric pattern from molecules that interacted with the AChE enzyme resulted in 3 pharmacophoric regions: an aromatic ring, an electron-acceptor region and a hydrophobic region. The molecules showed good pharmacokinetic and toxicological results, showing no warnings of mutagenicity and/or carcinogenicity. After the hybridization process, three hybrid molecules were obtained, which showed inhibitory activity for both targets.

CONCLUSION

It is concluded that research in the field of medicinal chemistry is advancing towards the discovery of new drug candidates that bring a better quality of life to patients with AD.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

Synthesis, antitumor activity and in silico analyses of amino acid derivatives of artepillin C, drupanin and baccharin from green propolis

Breast cancer has the highest incidence and mortality in females, while prostate cancer has the second-highest incidence in males. Studies have shown that compounds from Brazilian green propolis have antitumor activities and can selectively inhibit the AKR1C3 enzyme, overexpressed in hormone-dependent prostate and breast tumors. Thus, in an attempt to develop new cytotoxic inhibitors against these cancers, three prenylated compounds, artepillin C, drupanin and baccharin, were isolated from green propolis to synthesize new derivatives via coupling reactions with different amino acids. All obtained derivatives were submitted to antiproliferative assays against four cancer cells (MCF-7, MDA MB-231, PC-3, and DU145) and two normal cell lines (MCF-10A and PNT-2) to evaluate their cytotoxicity. In general, the best activity was observed for compound6e, derived from drupanin, which exhibited half-maximal inhibitory concentration (IC₅₀) of 9.6 ± 3 μM and selectivity index (SI) of 5.5 against MCF-7 cells.In silicostudies demonstrated that these derivatives present coherent docking interactions and binding modes against AKR1C3, which might represent a possible mechanism of inhibition in MCF-7 cells.

Potential colchicine binding site inhibitors unraveled by virtual screening, molecular dynamics and MM/PBSA

Microtubules have been widely studied in recent decades as an important pharmacological target for the treatment of cancer especially due to its key role in the mitosis process. Among the constituents of the microtubules, αβ-tubulin dimers stand out in view of their four distinct interaction sites, including the so-called colchicine binding site (CBS) - a promising target for the development of new tubulin modulators. When compared to other tubulin sites, targeting the CBS is advantageous because this site is able to host ligands with lower molecular volume and lipophilicity, thus reducing the chances of entailing the phenomenon of multiple drug resistance (MDR) - one of the main reasons of failure in chemotherapy. However, colchicine, the first ligand ever discovered with affinity towards the CBS, despite modulating the action of microtubules, has shown toxicity in clinical studies. Therefore, in order to expand the known chemical space of scaffolds capable of interacting with CBS and to design non-toxic colchicine binding site inhibitors, we conducted a robust virtual screening pipeline. This has been rigorously validated and consisted of ligand- and structure-based methodologies, which allowed us to select four promising CBS inhibitors called tubLCQF1-4. These four compounds were also evaluated with long trajectories molecular dynamics simulations and respective results were used for the theoretical determination of the free energy released in the formation of the complexes, using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) methodology.

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Effect of Net Charge on DNA-Binding, Protein-Binding and Anticancer Properties of Copper(I) Phosphine-Diimine Complexes

The syntheses of [Cu(PPh3)2(L)]NO3 and [Cu(PPh3)2(L-SO3Na)]NO3 were achieved through the reaction of Cu(PPh3)2NO3 and equimolar amount of the ligands (L = 5,6-diphenyl-3-[2-pyridyl]-1,2,4-triazine; LSO3Na = 5,6-diphenyl-3-[2-pyridyl]-1,2,4-triazine-4,4′-disulfonic acid disodium salt). The complexes were characterized by NMR and IR spectroscopy and mass spectrometry. The compounds exhibit similar absorption and emission spectra, suggesting a similar electronic structure. Ct-DNA binding studies show the strong influence of the net charge as Cu-L (positively charged) is able to bind to ct-DNA while Cu-LSO3Na (negatively charged) is not. The net charge of the complexes affects the thermodynamic and kinetic binding parameters toward human serum albumin. HSA-binding of the complexes was further investigated by molecular docking, revealing different binding sites on the HSA protein as a function of the net charge. The different anticancer activities of the complexes towards ovcar-3 and hope-62 cancer cell lines are suggestive of a role for the overall charge of the complexes. Interaction with the DNA is not the major mechanism for this class of complexes. The overall net charge of the pharmacophore (anticancer agent) should be a key consideration in the design of anticancer metal complexes.

Identification of known drugs as potential SARS-CoV-2 Mpro inhibitors using ligand- and structure-based virtual screening

Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment – in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy – application of ligand- and structure-based virtual screening – using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.

Synthesis, Structural Studies, and Anticancer Properties of [CuBr(PPh3)2(4,6-Dimethyl-2-Thiopyrimidine-κS]

CuBr(PPh3)2(4,6-dimethylpyrimidine-2-thione) (Cu-L) was synthesized by stirring CuBr(PPh3)3 and 4,6-dimethylpyrimidine-2-thione in dichloromethane. The crystal structure of Cu-L was obtained, and indicated that the complex adopts a distorted tetrahedral structure with several intramolecular hydrogen bonds. Moreover, a centrosymmetric dimer is formed by the intermolecular hydrogen bonding of the bromine acceptor created by symmetry operation 1−x, 1−y, 1−z to the methyl group (D3 = C42) of the pyrimidine–thione ligand. HSA-binding of Cu-L and its ligand were evaluated, revealing that Cu-L binds to HSA differently than its ligand. The HSA-bindings were modeled by molecular docking, which suggested that Cu-L binds to the II A domain while L binds between the I B and II A domains. Anticancer activities toward OVCAR-3 and HeLa cell lines were tested and indicated the significance of the copper center in enhancing the cytotoxic effect; negligible toxicities for L and Cu-L were observed towards a non-cancer cell line. The current study highlights the potential of copper(I)-phosphine complexes containing thione ligands as therapeutic agents.

Synthesis, in vitro bioassays, and computational study of heteroaryl nitazoxanide analogs

Antiprotozoal drug nitazoxanide (NTZ) has shown diverse pharmacological properties and has appeared in several clinical trials. Herein we present the synthesis, characterization, in vitro biological investigation, and in silico study of four hetero aryl amide analogs of NTZ. Among the synthesized molecules, compound 2 and compound 4 exhibited promising antibacterial activity against Escherichia coli (E. coli), superior to that displayed by the parent drug nitazoxanide as revealed from the in vitro antibacterial assay. Compound 2 displayed zone of inhibition of 20 mm, twice as large as the parent drug NTZ (10 mm) in their least concentration (12.5 µg/ml). Compound 1 also showed antibacterial effect similar to that of nitazoxanide. The analogs were also tested for in vitro cytotoxic activity by employing cell counting kit-8 (CCK-8) assay technique in HeLa cell line, and compound 2 was identified as a potential anticancer agent having IC50 value of 172 µg which proves it to be more potent than nitazoxanide (IC50  = 428 µg). Furthermore, the compounds were subjected to molecular docking study against various bacterial and cancer signaling proteins. The in vitro test results corroborated with the in silico docking study as compound 2 and compound 4 had comparatively stronger binding affinity against the proteins and showed a higher docking score than nitazoxanide toward human mitogen-activated protein kinase (MAPK9) and fatty acid biosynthesis enzyme (FabH) of E. coli. Moreover, the docking study demonstrated dihydrofolate reductase (DHFR) and thymidylate synthase (TS) as probable new targets for nitazoxanide and its synthetic analogs. Overall, the study suggests that nitazoxanide and its analogs can be a potential lead compound in the drug development.

Alteration of Anticancer and Protein-Binding Properties of Gold(I) Alkynyl by Phenolic Schiff Bases Moieties

A set of five gold complexes with the general formula Au(PR₃)(C≡C-C₆H₄-4-R′) (R = PPh₃, R′ = –CHO (1), R = PCy₃, R′ = –CHO (2), R = PPh₃, R′ = –N=CH-C₆H₄-2-OH (3), R = PPh₃, R′ = –N=CH-C₆H₄-4-OH (4), R = PCy₃, R′ = –N=CH-C₆H₄-2-OH (5)) were synthesized and characterized by elemental analysis, ¹H-NMR spectroscopy, ³¹P-NMR spectroscopy, and mass spectrometry. The structures of complexes 2 and 5 were determined by X-ray crystallography. The effects of the structural modifications on the protein binding affinities and anticancer activities of the five gold complexes were assessed. Fluorescence quenching experiments to assess binding to human serum albumin (HSA) revealed that the Schiff base complexes (3, 4, and 5) had binding constants that were superior to their parent aldehyde complexes and highlighted the position of the hydroxy group because complex 4 (4-hydroxy) had a binding constant 6400 times higher than complex 3 (2-hydroxy). The anticancer activities of the complexes against the OVCAR-3 (ovarian carcinoma) and HOP-62 (non-small-cell lung) cancer cell lines showed that the Schiff bases (3–5) were more cytotoxic than the aldehyde-containing complexes (1 and 2). Notably, compound 4 had cytotoxic activity comparable to that of cisplatin against OVCAR-3, demonstrating the significance of the para position for the hydroxy group. Molecular docking studies against the enzyme thioredoxin reductase (TrxR) and human serum albumin were conducted, with docking scores in good agreement with the experimental data. The current study highlights how small structural modifications can alter physiochemical and anticancer properties. Moreover, this simple design strategy using the aldehyde group can generate extensive opportunities to explore new gold(I)-based anticancer drugs via condensation, cyclization, or nucleophilic addition reactions of the aldehyde.

Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases

Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Synthesis of 4,6-diphenylpyrimidin-2-ol derivatives as new benzodiazepine receptor ligands

Benzodiazepines (BZDs) have been widely used in neurological disorders such as insomnia, anxiety, and epilepsy. The use of classical BZDs, e.g., diazepam, has been limited due to adverse effects such as interaction with alcohol, ataxia, amnesia, psychological and physical dependence, and tolerance. In the quest for new benzodiazepine agonists with more selectivity and low adverse effects, novel derivatives of 4,6-diphenylpyrimidin-2-ol were designed, synthesized, and evaluated. In this series, compound 2, 4-(2-(benzyloxy)phenyl)-6-(4-fluorophenyl)pyrimidin-2-ol, was the most potent analogue in radioligand binding assay with an IC₅₀ value of 19 nM compared to zolpidem (IC₅₀ = 48 nM), a nonbenzodiazepine central BZD receptor (CBR) agonist. Some compounds with a variety of affinities in radioligand receptor binding assay were selected for in vivo evaluations. Compound 3 (IC₅₀ = 25 nM), which possessed chlorine instead of fluorine in position 4 of the phenyl ring, exhibited an excellent ED₅₀ value in most in vivo tests. Proper sedative-hypnotic effects, potent anticonvulsant activity, appropriate antianxiety effect, and no memory impairment probably served compound 3, a desirable candidate as a benzodiazepine agonist. The pharmacological effects of compound 3 were antagonized by flumazenil, a selective BZD receptor antagonist, confirming the BZD receptors' involvement in the biological effects of the novel ligand.

AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses

RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., the starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNA.

Drug development is a lengthy and complicated process, which requires costly experiments on a very large number of chemical compounds. The identification of chemical molecules with desired properties can be facilitated by computational methods. Several methods were developed for computer-aided design of drugs that target protein molecules. However, recently the ribonucleic acid (RNA) emerged as an attractive target for the development of new drugs. Unfortunately, the portfolio of the computer methods that can be applied to study RNA and its interactions with small chemical molecules is very limited. This situation motivated us to develop a new computational method, with which to predict RNA-small molecule interactions. To this end, we collected the information on the statistics of interactions in experimentally determined structures of complexes formed by RNA with small molecules. We then used the statistical data to train machine learning methods aiming to distinguish between RNA-ligand interactions observed experimentally and other interactions that can be observed in theoretical analyses, but are not observed in nature. The resulting method called AnnapuRNA is superior to other similar tools and can be used to predict preferred ligands of RNA molecules and how RNA and small molecules interact with each other.

Perillyl alcohol decreases the frequency and severity of convulsive-like behavior in the adult zebrafish model of acute seizures

This research aimed to assess the effect of perillyl alcohol (PA) on convulsive behavior in vivo using adult zebrafish (Danio rerio, both sexes). The seizures were induced with pentylenetetrazole (PTZ) intraperitoneally at 170 mg/kg, and diazepam (DZP) was used as the control anticonvulsant (2 mg/kg, oral); PA was tested at 10, 50, and 100 mg/kg orally. The groups had ten animals per group (total n = 60), observed for 10 minutes after seizure induction. We manually appraised typical seizure phenotypes for quantification and used an animal tracking software (Toxtrac) to assess the motor parameters. Next, we sought to find a mechanism of action for PA anticonvulsant activity in silico using a structure-based activity prediction server and molecular docking. The results show that PTZ induced seizure-like behavior in all untreated animals with hyperlocomotion episodes, seizure itself, posture loss, and immobility. DZP inhibited the seizures in all animals of the positive control group. PA, in turn, inhibited the occurrence of seizures in a dose-dependent manner, with frequencies of 90%, 70%, and 40% (for 10, 50, and 100 mg/kg, respectively). The PA treatments also decreased several seizure endpoints in a dose-dependent manner. Also, the difference of the group treated with highest dose of PA was statistically significant compared with the negative control group for all the endpoints assessed (p < 0.05, Kruskal-Wallis). The in silico analyses suggested that PA can affect the GABAergic system, which might be involved in its anticonvulsant activity, but other mechanisms cannot be ruled out. Overall, our results suggest an anticonvulsant potential in perillyl alcohol.

A Consistent Scheme for Gradient-Based Optimization of ProteinLigand Poses

Scoring and numerical optimization of protein-ligand poses is an integral part of docking tools. Although many scoring functions exist, many of them are not continuously differentiable and they are rarely explicitly analyzed with respect to their numerical optimization behavior. Here, we present a consistent scheme for pose scoring and gradient-based pose optimization. It consists of a novel variant of the BFGS algorithm enabling step-length control, named LSL-BFGS (limited step length BFGS), and the empirical JAMDA scoring function designed for pose prediction and good numerical optimizability. The JAMDA scoring function shows a high pose prediction performance in the CASF-2016 docking power benchmark, top-ranking a pose with an RMSD of ≤2 Å in about 89% of the cases. The combination of JAMDA scoring with the LSL-BFGS algorithm shows a significantly higher optimization locality (i.e., no excessive movement of poses) than with the classical BFGS algorithm while retaining the characteristically low number of scoring function evaluations. The JAMDA scoring and optimization scheme is freely available for noncommercial use and academic research.

Identification of novel αβ-tubulin modulators with antiproliferative activity directed to cancer therapy using ligand and structure-based virtual screening

Among several strategies related to cancer therapy targeting the modulation of αβ-tubulin has shown encouraging findings, more specifically when this is achieved by inhibitors located at the colchicine binding site. In this work, we aim to fish new αβ-tubulin modulators through a diverse and rational VS study, and thus, exhibiting the development of two VS pipelines. This allowed us to identify two compounds 5 and 9 that showed IC₅₀ values of 19.69 and 21.97 μM, respectively, towards possible modulation of αβ-tubulin, such as assessed by in vitro assays in C6 glioma and HEPG2 cell lines. We also evaluated possible mechanisms of action of obtained hits towards the colchicine binding site of αβ-tubulin by using docking approaches. In addition, assessment of the stability of the active (5 and 9) and inactive compounds (3 and 13) within the colchicine binding site was carried out by molecular dynamics (MD) simulations, highlighting the solvent effect and revealing the compound 5 as the most stable in the complex. At last, deep analysis of these results provided some valuable insights on the importance of using mixed ligand- and structure-based strategies in VS campaigns, in order to achieve higher chemical diversity and biological effect as well.

Exploring the concerted mechanistic pathway for HIV-1 PR-substrate revealed by umbrella sampling simulation

HIV-1 protease (HIV-1 PR) is an essential enzyme for the replication process of its virus, and therefore considered an important target for the development of drugs against the acquired immunodeficiency syndrome (AIDS). Our previous study shows that the catalytic mechanism of subtype B/C-SA HIV-1 PR follows a one-step concerted acyclic hydrolysis reaction process using a two-layered ONIOM B3LYP/6-31++G(d,p) method. This present work is aimed at exploring the proposed mechanism of the proteolysis catalyzed by HIV-1 PR and to ensure our proposed mechanism is not an artefact of a single theoretical technique. Hence, we present umbrella sampling method that is suitable for calculating potential mean force (PMF) for non-covalent ligand/substrate-enzyme association/dissociation interactions which provide thermodynamic details for molecular recognition. The free activation energy results were computed in terms of PMF analysis within the hybrid QM(DFTB)/MM approach. The theoretical findings suggest that the proposed mechanism corresponds in principle with experimental data. Given our observations, we suggest that the QM/MM MD method can be used as a reliable computational technique to rationalize lead compounds against specific targets such as the HIV-1 protease.

Identification of novel CDK 9 inhibitors based on virtual screening, molecular dynamics simulation, and biological evaluation

AIMS

Cyclin-dependent kinase 9 (CDK9) is a member of the CDK subfamily and plays a major role in the regulation of transcriptional elongation. It has attracted widespread attention as a therapeutic target for cancer. Here, we aimed to explore novel CDK 9 inhibitors by using a hybrid virtual screening strategy.

MAIN METHODS

A hybrid virtual screening strategy was constructed with computer-aided drug design (CADD). First, compounds were filtered in accordance with Lipinski's rule of five and adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Second, a 3D-QSAR pharmacophore model was built and used as a 3D query to screen the obtained hit compounds. Third, the hit compounds were subjected to molecular docking studies. Fourth, molecular dynamics (MD) simulations were performed on CDK9 in complex with the final hits to examine the structural stability. Finally, CDK9 kinase biochemical assay was performed to identify the biological activity of the hit compounds.

KEY FINDINGS

Seven hit compounds were screened out. These hit compounds showed drug-like properties in accordance with Lipinski's rule of five and ADMET. Complexes involving the six hit compounds bound to CDK9 exhibited good structural stability in the MD simulation. Furthermore, these six hit compounds had strong inhibitory activity against CDK9 kinase. In particular, hit 3 showed the most promising activity with the percentage of 71%.

SIGNIFICANCE

The six hit compounds may be promising novel CDK9 inhibitors, and the hybrid virtual screening strategy designed in this study provides an important reference for the design and synthesis of novel CDK9 inhibitors.