Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5

BACKGROUND

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles.

PROBLEM STATEMENT

Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases.

METHODS

A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties.

RESULTS

Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc.

CONCLUSION

This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSA

This study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro and preclinical studies are needed to confirm these findings.Communicated by Ramaswamy H. Sarma.

Structure and Ligand Based Virtual Screening and MPO Topological Analysis of Triazolo Thiadiazepine-fused Coumarin Derivatives as Anti-Parkinson Drug Candidates

Parkinson's disease (PD) is a debilitating condition that can cause locomotor problems in affected patients, such as tremors and body rigidity. PD therapy often includes the use of monoamine oxidase B (MAOB) inhibitors, particularly phenylhalogen compounds and coumarin-based semi-synthetic compounds. The objective of this study was to analyze the structural, pharmacokinetic, and pharmacodynamic profile of a series of Triazolo Thiadiazepine-fused Coumarin Derivatives (TDCDs) against MAOB, in comparison with the inhibitor safinamide. To achieve this goal, we utilized structure-based virtual screening techniques, including target prediction and absorption, distribution, metabolism, and excretion (ADME) prediction based on multi-parameter optimization (MPO) topological analysis, as well as ligand-based virtual screening techniques, such as docking and molecular dynamics. The findings indicate that the TDCDs exhibit structural similarity to other bioactive compounds containing coumarin and MAOB-binding azoles, which are present in the ChEMBL database. The topological analyses suggest that TDCD3 has the best ADME profile, particularly due to the alignment between low lipophilicity and high polarity. The coumarin and triazole portions make a strong contribution to this profile, resulting in a permeability with Papp estimated at 2.15 × 10-5 cm/s, indicating high cell viability. The substance is predicted to be metabolically stable. It is important to note that this is an objective evaluation based on the available data. Molecular docking simulations showed that the ligand has an affinity energy of - 8.075 kcal/mol with MAOB and interacts with biological substrate residues such as Pro102 and Phe103. The results suggest that the compound has a safe profile in relation to the MAOB model, making it a promising active ingredient for the treatment of PD.

Binding selectivity analysis of new delhi metallo-beta-lactamase-1 inhibitors using molecular dynamics simulations: Exploring possibilities for decoding antimicrobial drug resistance

BACKGROUND

New Delhi metallo-beta-lactamase-1 (NDM1) confers resistance to several bacterial species against a broad range of beta-lactam antibiotics and turning them into superbugs that pose a significant threat to healthcare systems worldwide. As such, it is a potentially relevant biological target for counteracting bacterial infections. Given the lack of effective treatment options against NDM1 producing bacteria, finding a reliable inhibitor for the NDM1 enzyme is crucial.

METHODS

Using molecular dynamics simulations, the binding selectivities and affinities of three ligands, viz. PNK, 3S0, and N1G were investigated against NDM1.

RESULTS

The results indicate that N1G binds with more affinity to NDM1 than PNK and 3S0. The binding energy decomposition analysis revealed that residues I35, W93, H189, K211, and N220 showed significant binding energies with PNK, 3S0, and N1G, and hence are crucially involved in the binding of the ligands to NDM1. Molecular dynamics trajectory analysis further elicited that the ligands influence dynamic flexibility of NDM1 morphology, which contributes to the partial selectivities of PNK, 3S0, and N1G.

CONCLUSIONS

This in silico study offers a vital information for developing potential NDM1 inhibitors with high selectivity. Nevertheless, in vitro and in vivo experimental validation is mandated to extend the possible applications of these ligands as NDM1 inhibitors that succor in combating antimicrobial resistance.

Discovering allatostatin type-C receptor specific agonists

Currently, there is no pesticide available for the selective control of the pine processionary moth (Thaumetopoea pityocampa-specific), and conventional methods typically rely on mechanical techniques such as pheromone traps or broad-spectrum larvicidal chemicals. As climate change increases the range and dispersion capacity of crop and forest pests, outbreaks of the pine processionary occur with greater frequency and significantly impact forestry and public health. Our study is carried out to provide a T. pityocampa-specific pesticide targeting the Allatostatin Type-C Receptor (AlstR-C). We use a combination of computational biology methods, a cell-based screening assay, and in vivo toxicity and side effect assays to identify, for the first time, a series of AlstR-C ligands suitable for use as T. pityocampa-specific insecticides. We further demonstrate that the novel AlstR-C targeted agonists are specific to lepidopteran larvae, with no harmful effects on coleopteran larvae or adults. Overall, our study represents an important initial advance toward an insect GPCR-targeted next-generation pesticide design. Our approach may apply to other invertebrate GPCRs involved in vital metabolic pathways.

Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease

Alzheimer's disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051 kcal/mol better than the reference compound (- 11.828 kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100 ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be - 31.39, - 42.8, - 37.04, - 39.01, - 36.5, - 34.16 kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

Discovery of novel PARP-1 inhibitors using tandem in silico studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach

Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has an important role in DNA repair. The focus of the study was to identify novel PARP-1 inhibitors using a blend of tandem structure-based screening (Docking and e-pharmacophore-based screening) and artificial intelligence (deep learning)-based de novo approaches. The scrutiny of compounds having good binding characteristics for PARP-1 was carried out using a tandem mode of screening along with parameters such as binding energy and ADME analysis. The efforts afforded compound Vab1 (PubChem ID 129142036), which was chosen as a seed for obtaining novel compounds through a trained artificial intelligence (AI)-based model. Resultant compounds were assessed for PARP-1 inhibition; binding affinity prediction and interaction pattern analysis were carried out using the extra precision (XP) mode of docking. Two best hits, Vab1-b and Vab1-g, exhibiting good dock scores and suitable interactions, were subjected to 100 nanoseconds (ns) of molecular dynamics simulation in the active site of PARP-1 and compared with the reference Protein-Ligand Complex. The stable nature of PARP-1 upon binding to these compounds was revealed through MD simulation.Communicated by Ramaswamy H. Sarma.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates.Communicated by Ramaswamy H. Sarma.

In vitro and in silico analysis unravelled clinically desirable attributes of Bacillus altitudinis L-asparaginase

AIMS

To identify a marine L-asparaginase with clinically desirable attributes and characterize the shortlisted candidate through in silico tools.

METHODS AND RESULTS

Marine bacterial strains (number = 105) isolated from marine crabs were evaluated through a stepwise strategy incorporating the crucial attributes for therapeutic safety. The results demonstrated the potential of eight bacterial species for extracellular L-asparaginase production. However, only one isolate (Bacillus altitudinis CMFRI/Bal-2) showed clinically desirable attributes, viz. extracellular production, type-II nature, lack of concurrent L-glutaminase and urease activities, and presence of ansZ (functional gene for clinical type). The enzyme production was 22.55 ± 0.5 µM/mg protein/min within 24 h without optimization. The enzyme also showed good activity and stability in pH 7-8 and temperature 37°C, predicting the functioning inside the human body. The Michealis-Menten constant (Km) was 14.75 µM. Detailed in silico analysis based on functional gene authenticating the results of in vitro characterization and predicted the nonallergenic characteristic of the candidate. Docking results proved the higher affinity of the shortlisted candidate to L-asparagine than L-glutamine and urea.

CONCLUSION

Comprehensively, the study highlighted B. altitudinis type II asparaginase as a competent candidate for further research on clinically safe asparaginases.

Exploring promising natural compounds for breast cancer treatment: in silico molecular docking targeting WDR5-MYC protein interaction

Cancer is an aberrant differentiation of normal cells, characterized by uncontrolled growth and the potential to acquire invasive and aggressive properties that ultimately lead to metastasis. In the realm of scientific exploration, a multitude of pathways has been investigated and targeted by researchers, among which one specific pathway is recognized as WDR5-MYC. Continuous investigations and research show that WDR5-MYC is a therapeutic target protein. Hence, the discovery of naturally occurring compounds with anticancer properties has been suggested as a rapid and efficient alternative for the development of anticancerous therapeutics. A virtual screening approach was used to identify the most potent compounds from the NP-lib database at the MTiOpenScreen webserver against WDR5-MYC. This process yielded a total of 304 identified compounds. Subsequently, after screening, four potent compounds, namely Estrone (ZINC000003869899), Ethyl-1,2-benzanthracene (ZINC000003157052), Strychnine (ZINC000000119434) and 7H-DIBENZO [C, G] CARBAZOLE (ZINC000001562130), along with a cocrystallized 5-[4-(trifluoromethyl) phenyl]-1H-tetrazole inhibitor (QBP) as a reference ligand, were considered for stringent molecular docking. Thus, each compound exhibited significant docking energy between -8.2 and -7.7 kcal/mol and molecular contacts with essential residue Asn225, Lys250, Ser267 and Lys272 in the active pocket of WDR5-MYC against the QBP inhibitor (the native ligand QBP serves as a reference in the comparative analysis of docked complexes). The results support the potent compounds for drug-likeness and strong binding affinity with WDR5-MYC protein. Further, the stability of the selected compounds was predicted by molecular dynamics simulation (100 ns) contributed by intermolecular hydrogen bonds and hydrophobic interactions. This demonstrates the potential of the selected compounds to be used against breast cancer treatment.Communicated by Ramaswamy H. Sarma.

Structure‐Based Approaches for the Prediction of Alzheimer's Disease Inhibitors: Comparative Interactions Analysis, Pharmacophore Modeling and Molecular Dynamics Simulations

Due to its significant role in neurodegeneration, Cyclin‐dependent kinase 5 (CDK5) has emerged as a potential target for addressing neuropathological disorders, including Alzheimer's disease (AD). The application of CDK5 inhibitors has demonstrated promise in the treatment of AD. This prompted us to model this interesting target using a computational workflow named Docking‐based Comparative Intermolecular Contacts Analysis (dbCICA). Approaches including 3D‐QSAR, genetic algorithm, and pharmacophore modeling were employed to discover new CDK inhibitors.

Computational and AI-driven 3D structural analysis of human papillomavirus (HPV) oncoproteins E5, E6, and E7 reveal significant divergence of HPV E5 between low-risk and high-risk genotypes

There are over 220 identified genotypes of Human papillomavirus (HPV), and the HPV genome encodes 3 major oncogenes, E5, E6, and E7. Conservation and divergence in protein sequence and function between low-risk versus high-risk oncogenic HPV genotypes has not been fully characterized. Here, we used modern computational and structural folding algorithms to perform a comparative analysis of HPV E5, E6, and E7 between multiple low risk and high risk genotypes. We first identified significantly greater sequence divergence in E5 between low- and high-risk genotypes compared to E6 and E7. Next, we used AlphaFold to model the structure of papillomavirus proteins and complexes with high confidence, including some with no established consensus structure. We observed that HPV E5, but not E6 or E7, had a dramatically different 3D structure between low-risk and high-risk genotypes. To our knowledge, this is the first comparative analysis of HPV proteins using Alphafold artificial intelligence (AI) system. The marked differences in E5 sequence and structure in high-risk HPVs may contribute in important and underappreciated ways to the development of HPV-associated cancers.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation for identification of potential ERK inhibitors

As the downstream component of the mitogen-activated protein kinases (MAPK) pathway, the extracellular signal-regulated kinase (ERK) is responsible for phosphorylating a broad range of substrates in cell proliferation, differentiation, and survival. Direct targeting the ERK proteins by the piperidinopyrimidine urea-based inhibitors has been demonstrated to be an effective way to block the MAPK signaling pathway in inhibiting tumor growth. In order to discover better inhibitors, a computer-aided drug design (CADD) approach was employed to reveal the pharmacological characteristics and mechanisms of action. The pharmacophore model was generated on the basis of the compounds with eight features, i.e., four hydrogen bond acceptor atoms, one hydrogen bond donor atom, and three hydrophobic centers. A total of 14 hit compounds were obtained through virtual screening. Two potential inhibitors, namely VS01 and VS02, have been identified by molecular docking and molecular dynamics simulations. Both compounds are capable of attaching to the ERK pocket precisely. The binding free energies of VS01 and VS02 are about 15 kJ/mol and 4 kJ/mol stronger than that of the clinic Ulixertinib because of the characteristic hydrogen bonding, electrostatic, and hydrophilic interactions. The present theoretical investigations shed new light on the rational design of the potential ERK inhibitors to stimulate further experimental tests.Communicated by Ramaswamy H. Sarma.

Oplodiol and nitidine as potential inhibitors of Plasmodium falciparum dihydrofolate reductase: insights from a computational study

Many natural products have been shown to possess antiplasmodial activities, but their protein targets are unknown. This work employed molecular docking and molecular dynamics simulations to explore the inhibitory activity of some antiplasmodial natural products against wild-type and mutant strains of Plasmodium falciparum dihydrofolate reductase (PfDHFR). From the molecular docking study, 6 ligands preferentially bind at the active site of the DHFR domain with binding energies ranging from -6.4 to -9.5 kcal/mol. Interactions of compounds with MET55 and PHE58 were mostly observed in the molecular docking study. From the molecular dynamics study, the binding of 2 of the ligands-nitidine and oplodiol-was observed to be stable against all tested strains of PfDHFR. The average binding free energy of oplodiol in complex with the various PfDHFR strains was -93.701 kJ/mol whereas that of nitidine was -106.206 kJ/mol. The impressive in silico activities of the 2 compounds suggest they could be considered for development as potential antifolate agents.Communicated by Ramaswamy H. Sarma.

Molecular mechanism of Acanthopanax senticosus in the treatment of Alzheimer's disease based on network pharmacology and molecular docking

Alzheimer's disease is the most common neurodegenerative disease. Acanthopanax senticosus, also known as Ciwujia or Siberian ginseng in Chinese, has a wide range of antioxidant and anti-inflammatory activities. The study aims to explore the action mechanism of A. senticosus against Alzheimer's disease using network pharmacology and molecular docking. The active ingredients and targets of A. senticosus were searched through the ETCM database, and Alzheimer's disease-related targets were obtained through the OMIM and GeneCards databases. The Cytoscape 3.7.2 software was used to construct a "drug-component-target" relationship network, and the target genes of A. senticosus against Alzheimer's disease were imported into the String database to establish a protein interaction (PPI) network. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes gene enrichment analyses were performed through the Metascape database to obtain potential pathways of action of A. senticosus for the treatment of Alzheimer's disease, and the ability of these active ingredients to bind to core targets was then verified by molecular docking. 51 active ingredients were screened from A. senticosus, and 88 effective targets for Alzheimer's disease were screened. Topological and pathway-enrichment analyses revealed that A. senticosus could play a beneficial role in the treatment of Alzheimer's disease by regulating apoptosis and inflammation. Molecular docking results showed that Ciwujianoside B, Chiisanoside, and Ciwujianoside D1 had strong binding abilities to key target proteins (TNFα, IL1β, and CASP3). Collectively, A. senticosus is feasible in the treatment of Alzheimer's disease.

Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study

STAT3 belongs to a family of seven transcription factors. It plays an important role in activating the transcription of various genes involved in a variety of cellular processes. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. However, since STAT3 inhibitors bind to the shallow SH2 domain of the protein, it is expected that hydration water molecules play significant role in ligand-binding complicating the discovery of potent binders. To remedy this issue, we herein propose to extract pharmacophores from molecular dynamics (MD) frames of a potent co-crystallized ligand complexed within STAT3 SH2 domain. Subsequently, we employ genetic function algorithm coupled with machine learning (GFA-ML) to explore the optimal combination of MD-derived pharmacophores that can account for the variations in bioactivity among a list of inhibitors. To enhance the dataset, the training and testing lists were augmented nearly a 100-fold by considering multiple conformers of the ligands. A single significant pharmacophore emerged after 188 ns of MD simulation to represent STAT3-ligand binding. Screening the National Cancer Institute (NCI) database with this model identified one low micromolar inhibitor most likely binds to the SH2 domain of STAT3 and inhibits this pathway.

Pharmacophore based virtual screening & molecular docking approach for identification of mycobacterial membrane protein large 3 (MmpL3) inhibitors

Tuberculosis (TB) disease continues to remain one of the global threats for mankind. Till date many antibacterial compounds have been identified to target mycobacterium tuberculosis (MTB). However, the mutating nature of the mycobacteria has always posed a challenge for designing newer drugs which can target both the non-mutating and mutating forms of TB. In this process, Mycobacterial membrane protein Large 3 (MmpL3) transporter was identified as one of the key targets for inhibiting tuberculosis. Herein we have made an effort to find potential inhibitors against MmpL3 by using a pharmacophore-based virtual screening workflow, followed by molecular docking studies and molecular dynamic simulations. Based on a set of 220 compounds showing anti-tubercular activity proposed to target MmpL3 transporter with MIC values ranging from 0.003 to 737 μM, a 5-point pharmacophore ADHHR_2 model possessing one hydrogen acceptor, one hydrogen donor, two hydrophobic groups and an aromatic ring system was generated. The model validated by enrichment study was used to screen Asinex and DrugBank database to identify a potential lead compound such as DrugBank_6059 that was found to show better binding affinity (-11.36) and hydrophobic interactions with target protein in comparison to standard drug SQ109.Communicated by Ramaswamy H. Sarma.

Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach

Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.

In-silico and in-vitro functional validation of imidazole derivatives as potential sirtuin inhibitor

Introduction

Epigenetic enzymes can interact with a wide range of genes that actively participate in the progression or repression of a diseased condition, as they are involved in maintaining cellular homeostasis. Sirtuins are a family of Class III epigenetic modifying enzymes that regulate cellular processes by removing acetyl groups from proteins. They rely on NAD+ as a coenzyme in contrast to classical histone deacetylases (HDACs) (Class I, II, and IV) that depend on Zn+ for their activation, linking their function to cellular energy levels. There are seven mammalian sirtuin isoforms (Sirt1-7), each located in different subcellular compartments. Sirtuins have emerged as a promising target, given that inhibitors of natural and synthetic sources are highly warranted. Imidazole derivatives are often investigated as sirtuin regulators due to their ability to interact with the binding site and modulate their activity. Imidazole bestows many possible substitutions on its ring and neighboring atoms to design and synthesize derivatives with specific target selectivity and improved pharmacokinetic properties, optimizing drug development.

Materials and methods

Ligand preparation, protein preparation, molecular docking, molecular dynamics, density function theory (DFT) analysis, and absorption, distribution, metabolism, and excretion (ADME) analysis were performed to understand the interacting potential and effective stability of the ligand with the protein. RT-PCR and Western blot analyses were performed to understand the impact of ligands on the gene and protein expression of Class III HDAC enzymes.

Results and discussion

We evaluated the sirtuin inhibition activity of our in-house compound comprised of imidazole derivatives by docking the molecules with the protein data bank. ADME properties of all the compounds used in the study were evaluated, and it was found that all fall within the favorable range of being a potential drug. The molecule with the highest docking score was analyzed using DFT, and the specific compound was used to treat the non-small cell lung cancer (NSCLC) cell lines A549 and NCI-H460. The gene and protein expression data support the in-silico finding that the compound Ethyl 2-[5-(4-chlorophenyl)-2-methyl-1-H-Imidazole-4-yl) acetate has an inhibitory effect on nuclear sirtuins. In conclusion, targeting sirtuins is an emerging strategy to combat carcinogenesis. In this study, we establish that Ethyl 2-[5-(4-chlorophenyl)-2-methyl-1-H-Imidazole-4-yl) acetate possesses a strong inhibitory effect on nuclear sirtuins in NSCLC cell lines.

Expanding the Structural Diversity at the Phenylene Core of Ligands for the von Hippel-Lindau E3 Ubiquitin Ligase: Development of Highly Potent Hypoxia-Inducible Factor-1α Stabilizers

Hypoxia-inducible factor-1α (HIF-1α) constitutes the principal mediator of cellular adaptation to hypoxia in humans. The HIF-1α protein level and activity are tightly regulated by the ubiquitin E3 ligase von Hippel-Lindau (VHL). Here, we performed a structure-guided and bioactivity-driven design of new VHL inhibitors. Our iterative and combinatorial strategy focused on chemical variability at the phenylene unit and encompassed further points of diversity. The exploitation of tailored phenylene fragments and the stereoselective installation of the benzylic methyl group provided potent VHL ligands. Three high-resolution structures of VHL-ligand complexes were determined, and bioactive conformations of these ligands were explored. The most potent inhibitor (30) exhibited dissociation constants lower than 40 nM, independently determined by fluorescence polarization and surface plasmon resonance and an enhanced cellular potency, as evidenced by its superior ability to induce HIF-1α transcriptional activity. Our work is anticipated to inspire future efforts toward HIF-1α stabilizers and new ligands for proteolysis-targeting chimera (PROTAC) degraders.

In silico research on new sulfonamide derivatives as BRD4 inhibitors targeting acute myeloid leukemia using various computational techniques including 3D-QSAR, HQSAR, molecular docking, ADME/Tox, and molecular dynamics

Acute myeloid leukemia, a serious condition affecting stem cells, drives uncontrollable myeloblast proliferation, leading to accumulation. Extensive research seeks rapid, effective chemotherapeutics. A potential option is a BRD4 inhibitor, known for suppressing cell proliferation. Sulfonamide derivatives probed essential structural elements for potent BRD4 inhibitors. To achieve this goal, we employed 3D-QSAR molecular modeling techniques, including CoMFA, CoMSIA, and HQSAR models, along with molecular docking and molecular dynamics simulations. The validation of the 2D/3D QSAR models, both internally and externally, underscores their robustness and reliability. The contour plots derived from CoMFA, CoMSIA, and HQSAR analyses played a pivotal role in shaping the design of effective BRD4 inhibitors. Importantly, our findings highlight the advantageous impact of incorporating bulkier substituents on the pyridinone ring and hydrophobic/electrostatic substituents on the methoxy-substituted phenyl ring, enhancing interactions with the BRD4 target. The interaction mode of the new compounds with the BRD4 receptor (PDB ID: 4BJX) was investigated using molecular docking simulations, revealing favorable binding energies, supported by the formation of hydrogen and hydrophobic bonds with key protein residues. Moreover, these novel inhibitors exhibited good oral bioavailability and demonstrated non-toxic properties based on ADMET analysis. Furthermore, the newly designed compounds along with the most active one from series 58, underwent a molecular dynamics simulation to analyze their behavior. The simulation provided additional evidence to support the molecular docking results, confirming the sustained stability of the analyzed molecules over the trajectory. This outcome could serve as a valuable reference for designing and developing novel and effective BRD4 inhibitors.Communicated by Ramaswamy H. Sarma.

Computational Tool to Design Small Synthetic Inhibitors Selective for XIAP-BIR3 Domain

X-linked inhibitor of apoptosis protein (XIAP) exercises its biological function by locking up and inhibiting essential caspase-3, -7 and -9 toward apoptosis execution. It is overexpressed in multiple human cancers, and it plays an important role in cancer cells' death skipping. Inhibition of XIAP-BIR3 domain and caspase-9 interaction was raised as a promising strategy to restore apoptosis in malignancy treatment. However, XIAP-BIR3 antagonists also inhibit cIAP1-2 BIR3 domains, leading to serious side effects. In this study, we worked on a theoretical model that allowed us to design and optimize selective synthetic XIAP-BIR3 antagonists. Firstly, we assessed various MM-PBSA strategies to predict the XIAP-BIR3 binding affinities of synthetic ligands. Molecular dynamics simulations using hydrogen mass repartition as an additional parametrization with and without entropic term computed by the interaction entropy approach produced the best correlations. These simulations were then exploited to generate 3D pharmacophores. Following an optimization with a training dataset, five features were enough to model XIAP-BIR3 synthetic ligands binding to two hydrogen bond donors, one hydrogen bond acceptor and two hydrophobic groups. The correlation between pharmacophoric features and computed MM-PBSA free energy revealed nine residues as crucial for synthetic ligand binding: Thr308, Glu314, Trp323, Leu307, Asp309, Trp310, Gly306, Gln319 and Lys297. Ultimately, and three of them seemed interesting to use to improve XIAP-BR3 versus cIAP-BIR3 selectivity: Lys297, Thr308 and Asp309.

In Vitro and In Silico Evaluation of Red Algae Laurencia obtusa Anticancer Activity

Studies estimate that nearly 2 million new cases of gastric cancer will occur worldwide during the next two decades, which will increase mortality associated with cancer and the demand for new treatments. Marine algae of the Laurencia genus have secondary metabolites known for their cytotoxic action, such as terpenes and acetogenins. The species Laurencia obtusa has demonstrated cytotoxicity against many types of tumors in previous analyses. In this study, we determined the structure of terpenes, acetogenins, and one fatty acid of Laurencia using mass spectrometry (ESI-FT-ICR/MS). In vitro cytotoxicity assays were performed with adenocarcinoma gastric cells (AGS) to select the most cytotoxic fraction of the crude extract of L. obtusa. The Hex:AcOEt fraction was the most cytotoxic, with IC₅₀ 9.23 µg/mL. The selectivity index of 15.56 shows that the Hex:AcOEt fraction is selective to cancer cells. Compounds obtained from L. obtusa were tested by the analysis of crystallographic complexes. Molecular docking calculations on the active site of the HIF-2α protein showed the highest affinity for sesquiterpene chermesiterpenoid B, identified from HEX:AcOEt fraction, reaching a score of 65.9. The results indicate that L. obtusa presents potential compounds to be used in the treatment of neoplasms, such as gastric adenocarcinoma.

In Silico Design of a Chimeric Humanized L-asparaginase

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermine both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS-MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein-ligand docking.

Concise Synthesis of Molecular Hyperboloids by Oligomeric Macrocyclization of Octagonal Molecules

Molecular hyperboloids were designed and synthesized. The synthesis was achieved by development of oligomeric macrocyclization of an octagonal molecule with a saddle shape. The saddle-shaped molecule, that is, [8]cyclo-meta-phenylene ([8]CMP), was decorated with two linkers for the oligomeric macrocyclization and was synthetically assembled by Ni-mediated Yamamoto coupling. Three congeners of the molecular hyperboloids (2mer-4mer) were obtained, and 2mer and 3mer were subjected to X-ray crystallographic analysis. Crystal structures revealed nanometer-sized hyperboloidal structures with 96π and 144π electrons, which also possessed nanopores on the curved molecular structures. Structures of [8]CMP cores of the molecular hyperboloids were compared with those of saddle-shaped phenine [8]circulene with a negative Gauss curvature to confirm their structural resemblance, which suggests further explorations of expanded networks of molecular hyperboloids.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

3D-QSAR, molecular docking, DFT and ADMET studies on quinazoline derivatives to explore novel DHFR inhibitors

Resistance to folate antagonists is caused by mutations in the dihydrofolate reductase (DHFR) genes. These mutations affect the amino acids at positions 51, 59, 108 and 164 of DHFR, which appear to play a major role in malaria treatment failure. Therefore, the design of new drugs able to overcome the problem of antifolate drug resistance should receive urgent attention. In this study, a three-dimensional quantitative structure-activity relationship (3 D-QSAR) and molecular docking studies have been performed on antimalarial quinazoline derivatives. The CoMFA (Q² = 0.63, R² = 0.83 and Rpred2 = 0.70) and the CoMSIA (Q² = 0.584, R² = 0.816, and Rpred2= 0.73) models show a good prediction of antimalarial activity. The reliability and robustness of the proposed models have been tested using several validation methods, which showed that the steric, electrostatic, hydrophobic and H-bond acceptor fields of the CoMSIA model play a key role in the prediction of antimalarial activity. Molecular docking studies reveal important interactions between two isomeric compounds (meta and para) and the DHFR receptor in its wild and mutant forms. The obtained outcomes of molecular docking studies have been validated using a new method based on visual inspection. The DFT study of the two isomeric compounds confirms clearly the trends of 3 D-QSAR and molecular docking for the design of new compounds. Moreover, the consistency between theoretical, 3 D-QSAR and molecular docking analysis provides guidance for the design of new drug candidates, which have been tested using ADMET properties and drug likeness analysis.Communicated by Ramaswamy H. Sarma.

Deciphering the competitive inhibition of dihydropteroate synthase by 8 marcaptoguanine analogs: enhanced potency in phenylsulfonyl fragments

The emergence of sulfa-drug resistance and reduced efficacy of pterin-based analogs towards Dihydropteroate synthase (DHPS) inhibition dictate a pressing need of developing novel antimicrobial agents for immune-compromised patients. Recently, a series of 8-Marcaptoguanin (8-MG) derivatives synthesized for 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (experimental K_D ∼ 100-.0.36) showed remarkable homology with the pteroic-acid and serve as a template for product antagonism in DHPS. The present work integrates ligand-based drug discovery techniques with structure-based docking, enhanced MD simulation, and MM/PBSA techniques to demonstrate the essential features of 8-MG analogs which make it a potent inhibitor for DHPS. The delicate balance in hydrophilic, hydrophobic substitutions on the 8-MG core is the crucial signature for DHPS inhibition. It is found that the dynamic interactions of active compounds are mainly dominated by consistent hydrogen bonding network with Asp 96, Asn 115, Asp 185, Ser 222, Arg 255 and π-π stacking, π-cation interactions with Phe 190, Lys 221. Further, two new 8-MG compounds containing N-phenylacetamide (compound S1, ΔG_bind-eff = -62.03 kJ/mol) and phenylsulfonyl (compound S3, ΔG_bind-eff = -71.29 kJ/mol) fragments were found to be the most potent inhibitor of DHPS, which stabilize the flexible pABA binding loop, thereby increasing their binding affinity. MM/PBSA calculation shows electrostatic energy contribution to be the principal component in stabilizing the inhibitors in the binding pocket. This fact is further confirmed by the higher energy barrier obtained in umbrella sampling for this class of inhibitors.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Evaluation of flavonoids as potential inhibitors of the SARS-CoV-2 main protease and spike RBD: Molecular docking, ADMET evaluation and molecular dynamics simulations

The 3CLpro main protease and the RDB spike (s) protein of SARS-CoV-2 are critical targets in the treatment of coronavirus 19 disease (COVID-19), as they are responsible for the COVID-19 replication and infection. With this in mind, Molecular docking of 26 natural compounds belonging to the flavonoid family with the 3CLpro and RBD sites of SARS-CoV-2 has been performed. The docking results revealed that the ligands Silibinin, Tomentin A, Tomentin B, 4′-O-methyldiplacone, Hesperidin Amentoflavone and Bilobetin act as a potential inhibitor of SARS-CoV-2 3CLpro, and that the ligands Herbacetin, Morin, Silibinin, Tomentin E, Amentoflavone, Bilobetin, Baicalein and Quercetin can be potential inhibitors of SARS-CoV-2 RBD. It has been noticed that three ligands can inhibit both sites of SARS-CoV-2, indicating a great potential of these compounds to combat COVID-19. Moreover, molecular docking has been validated by a new validation method based on visual inspiration. Evaluation of ADMET pharmacokinetic properties and the drug likeness in silico revealed that six compounds could be effective drugs against COVID-19. Finally, the docking results were verified by molecular dynamics simulations and MM-GBSA calculation to confirm the stability of hydrogen bonding interactions with crucial residues, which are essential to overcome SARS-CoV-2. These results could direct researchers toward plant-derived compounds that could be further investigated as therapeutic targets against COVID-19 replication and infection.

Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation

Ideonella sakaiensis PET hydrolase (IsPETase) is a well-characterized enzyme for effective PET biodegradation. However, the low soluble expression level of the enzyme hampers its practical implementation in the biodegradation of PET. Herein, the expression of IsPETase^Mut, one of the most active mutants of IsPETase obtained so far, was systematically explored in E. coli by adopting a series of strategies. A notable improvement of soluble IsPETase^Mut was observed by using chaperon co-expression and fusion expression systems. Under the optimized conditions, GroEL/ES co-expression system yielded 75 ± 3.4 mg·L^-1 purified soluble IsPETase^Mut (GroEL/ES), and NusA fusion expression system yielded 80 ± 3.7 mg·L^-1 purified soluble NusA-IsPETase^Mut, which are 12.5- and 4.6-fold, respectively, higher than its commonly expression in E. coli. The two purified enzymes were further characterized. The results showed that IsPETase^Mut (GroEL/ES) displayed the same catalytic behavior as IsPETase^Mut, while the fusion of NusA conferred new enzymatic properties to IsPETase^Mut. Although NusA-IsPETase^Mut displayed a lower initial hydrolysis capacity than IsPETase^Mut, it showed a 1.4-fold higher adsorption constant toward PET. Moreover, the product inhibition effect of terephthalic acid (TPA) on IsPETase was reduced with NusA-IsPETase^Mut. Taken together, the latter two catalytic properties of NusA-IsPETase^Mut are more likely to contribute to the enhanced product release by NusA-IsPETase^Mut PET degradation for two weeks.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Binding and Activation of Serotonergic G-Protein Coupled Receptors by the Multimodal Antidepressant Vortioxetine

G-protein coupled receptors (GPCRs) are important pharmacological targets. Despite substantial progress, important questions still remain concerning the details of activation: how can a ligand act as an agonist in one receptor but as an antagonist in a homologous receptor, and how can agonists activate a receptor despite lacking polar functional groups able to interact with helix 5 as is the case for the related adrenergic receptors? Studying vortioxetine (VXT), an important multimodal antidepressant drug, may elucidate both questions. Herein, we present a thorough in silico analysis of VXT binding to 5-HT_1A, 5-HT_1B, and 5-HT₇ receptors and compare it with available experimental data. We are able to rationalize the differential mode of action of VXT at different receptors, but also, in the case of the 5-HT_1A receptor, we observe the initial steps of activation that inform about an activation mechanism that does not involve polar interaction with helix 5. The results extend our current understanding of agonist and antagonist action at aminergic GPCRs.

Transcriptional Feedback Loops in the Caprine Circadian Clock System

The circadian clock system is based on interlocked positive and negative transcriptional and translational feedback loops of core clock genes and their encoded proteins. The mammalian circadian clock system has been extensively investigated using mouse models, but has been poorly investigated in diurnal ruminants. In this study, goat embryonic fibroblasts (GEFs) were isolated and used as a cell model to elucidate the caprine circadian clock system. Real-time quantitative PCR analysis showed that several clock genes and clock-controlled genes were rhythmically expressed in GEFs over a 24 h period after dexamethasone stimulation. Immunofluorescence revealed that gBMAL1 and gNR1D1 proteins were expressed in GEFs, and western blotting analysis further verified that the proteins were expressed with circadian rhythmic changes. Diurnal changes in clock and clock-controlled gene expression at the mRNA and protein levels were also observed in goat liver and kidney tissues at two representative time points in vivo. Amino acid sequences and tertiary structures of goat BMAL1 and CLOCK proteins were found to be highly homologous to those in mice and humans. In addition, a set of goat representative clock gene orthologs and the promoter regions of two clock genes of goats and mice were cloned. Dual-luciferase reporter assays showed that gRORα could activate the promoter activity of the goat BMAL1, while gNR1D1 repressed it. The elevated pGL4.10-gNR1D1-Promoter-driven luciferase activity induced by mBMAL1/mCLOCK was much higher than that induced by gBMAL1/gCLOCK, and the addition of gCRY2 or mPER2 repressed it. Real-time bioluminescence assays revealed that the transcriptional activity of BMAL1 and NR1D1 in goats and mice exhibited rhythmic changes over a period of approximately 24 h in NIH3T3 cells or GEFs. Notably, the amplitudes of gBMAL1 and gNR1D1 promoter-driven luciferase oscillations in NIH3T3 cells were higher than those in GEFs, while mBMAL1 and mNR1D1 promoter-driven luciferase oscillations in NIH3T3 cells had the highest amplitude. In sum, transcriptional and translational loops of the mammalian circadian clock system were found to be broadly conserved in goats and not as robust as those found in mice, at least in the current experimental models. Further studies are warranted to elucidate the specific molecular mechanisms involved.

DK, Ragunathan V, Tiwari P, A. S, P BD. Molecular docking, validation, dynamics simulations, and pharmacokinetic prediction of natural compounds against the SARS-CoV-2 main-protease

The study aims to evaluate the potency of two hundred natural antiviral phytocompounds against the active site of the Severe Acquired Respiratory Syndrome - Coronavirus - 2 (SARS-CoV-2) Main-Protease (Mpro) using AutoDock 4.2.6. The three- dimensional crystal structure of the Mpro (PDB Id: 6LU7) was retrieved from the Protein Data Bank (PDB), the active site was predicted using MetaPocket 2.0. Food and Drug Administration (FDA) approved viral protease inhibitors were used as standards for comparison of results. The compounds theaflavin-3-3'-digallate, rutin, hypericin, robustaflavone, and (-)-solenolide A with respective binding energy of -12.41 (Ki = 794.96 pM); -11.33 (Ki = 4.98 nM); -11.17 (Ki = 6.54 nM); -10.92 (Ki = 9.85 nM); and -10.82 kcal/mol (Ki = 11.88 nM) were ranked top as Coronavirus Disease - 2019 (COVID-19) Mpro inhibitors. The interacting amino acid residues were visualized using Discovery Studio 3.5 to elucidate the 2-dimensional and 3-dimensional interactions. The study was validated by i) re-docking the N3-peptide inhibitor-Mpro and superimposing them onto co-crystallized complex and ii) docking decoy ligands to Mpro. The ligands that showed low binding energy were further predicted for and pharmacokinetic properties and Lipinski's rule of 5 and the results are tabulated and discussed. Molecular dynamics simulations were performed for 50 ns for those compounds using the Desmond package, Schrödinger to assess the conformational stability and fluctuations of protein-ligand complexes during the simulation. Thus, the natural compounds could act as a lead for the COVID-19 regimen after in-vitro and in- vivo clinical trials.Communicated by Ramaswamy H. Sarma.

Ligand-Based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

BACKGROUND

Chemokines are involved in several human diseases and in different stages of COVID-19 infection and play critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

METHODS

We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM.

RESULTS AND CONCLUSION

These results prove the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases towards new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

In silico design and in vitro assessment of anti-Helicobacter pylori compounds as potential small-molecule arginase inhibitors

Related to a variety of gastrointestinal disorders ranging from gastric ulcer to gastric adenocarcinoma, the infection caused by the gram-negative bacteria Helicobacter pylori (H. pylori) poses as a great threat to human health; hence, the search for new treatments is a global priority. The H. pylori arginase (HPA) protein has been widely studied as one of the main virulence factors of this bacterium, being involved in the prevention of nitric oxide-mediated bacterial cell death, which is a central component of innate immunity. Given the growing need for the development of new drugs capable of combating the infection by H. pylori, the present work describes the search for new HPA inhibitors, using virtual screening techniques based on molecular docking followed by the evaluation of the proposed modes of interaction at the HPA active site. In vitro studies of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), followed by cytotoxicity activity in gastric adenocarcinoma and non-cancer cells, were performed. The results highlighted compounds 6, 11, and 13 as potential inhibitors of HPA; within these compounds, the results indicated 13 presented an improved activity toward H. pylori killing, with MIC and MBC both at 64 µg/mL. Moreover, compound 13 also presented a selectivity index of 8.3, thus being more selective for gastric adenocarcinoma cells compared to the commercial drug cisplatin. Overall, the present work demonstrates the search strategy based on in silico and in vitro techniques is able to support the rational design of new anti-H. pylori drugs.

Evaluating the performance of drug-repurposing technologies

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

Correlated Motions and Dynamics in Different Domains of Epidermal Growth Factor Receptor With L858R and T790M Mutations

Non-small cell lung cancer with an activating epidermal growth factor receptor (EGFR) mutation responds well to targeted drugs. In most cases, drug resistance appears after about a year. Several studies have been conducted on the kinase domain of EGFR to understand the drug resistance mechanism. Since EGFR is a multi-domain protein, mutation in the kinase domain may affect the other domains as well. In this study, we examine the complete structure of the multi-domain EGFR protein and its mutants. We performed molecular dynamics simulations for wildtype EGFR, EGFR with L858R mutation, and EGFR with L858R and T790M mutations. We applied normal mode analysis and complex network analysis to extract the correlated motions in the domains of EGFR. The normal modes are used to construct the dynamic cross-correlation map (DCCM). Simulation results show different patterns of correlated motions in each domain of EGFR mutants compared to the wildtype. In Domains 1 and 3 of the extracellular region, a small number of weak positively correlated motions are extracted. Domains 2 and 4 show large numbers of both positive and negative motions. However, the negatively correlated motions are stronger in mutant structures compared to the wildtype. In Domain 7, some residues showed a positive correlation around the main diagonal. We also identified different communities, nodes and crucial residues in the domains of the structures, which can be important for the function of EGFR. Moreover, hydrogen bond analysis is performed for the stability analysis. The mutant structures have fewer hydrogen bonds compared to the wildtype. Overall, these findings are useful for understanding the dynamics and communications in EGFR domains.

Pharmacophore-guided Virtual Screening to Identify New β3-Adrenergic Receptor Agonists

The β₃‐adrenergic receptor (β₃‐AR) is found in several tissues such as adipose tissue and urinary bladder. It is a therapeutic target because it plays a role in thermogenesis, lipolysis, and bladder relaxation. Two β₃‐AR agonists are used clinically: mirabegron 1 and vibegron 2, which are indicated for overactive bladder syndrome. However, these drugs show adverse effects, including increased blood pressure in mirabegron patients. Hence, new β₃‐AR agonists are needed as starting points for drug development. Previous pharmacophore modeling studies of the β₃‐AR did not involve experimental in vitro validation. Therefore, this study aimed to conduct prospective virtual screening and confirm the biological activity of virtual hits. Ligand‐based pharmacophore modeling was performed since no 3D structure of human β₃‐AR is yet available. A dataset consisting of β₃‐AR agonists was prepared to build and validate the pharmacophore models. The best model was employed for prospective virtual screening, followed by physicochemical property filtering and a docking evaluation. To confirm the activity of the virtual hits, an in vitro assay was conducted, measuring cAMP levels at the cloned β₃‐AR. Out of 35 tested compounds, 4 compounds were active in CHO−K1 cells expressing the human β₃‐AR, and 8 compounds were active in CHO−K1 cells expressing the mouse β₃‐AR.