PharmaGist: a webserver for ligand-based pharmacophore detection

Pharmacophore modeling in drug design

A successful and expanded area of computational drug design is pharmacophore modeling. A pharmacophore is a description of the structural features of a compound that are essential to its biological activity. The rational design of new drugs has made extensive use of the pharmacophore concept. By schematically illustrating the essential components of molecular recognition, Pharmacophores can be used to represent and identify molecules in two or three dimensions. Besides target identification, the pharmacophore concept is also helpful for side effects, off-target, and absorption, distribution, and toxicity modeling. Moreover, to enhance virtual screening, pharmacophores, and molecular docking simulations are frequently coupled. A completely new area of drug design has been made possible by the development of machine learning techniques and pharmacophore mapping algorithms, wherein an ineffective molecule with the right modifications may have the potential to function as an inhibitor. This approach has been stimulated by its predictive abilities to assess the possibility that a set of compounds will be active against protein targets of interest. With alignment to the standard pharmacophore model, active compounds of the protein target can be developed. The pharmacophore modeling/screening technique is used to identify possible proteins of interest and seek out/suggest novel therapeutic uses for the drug.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review

Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.

Computational exploration of natural compounds targeting Staphylococcus aureus: inhibiting AgrA promoter binding for antimicrobial intervention

Staphylococcus aureus is a highly virulent nosocomial pathogen that poses a significant threat to individuals exposed to healthcare settings. Due to its sophisticated machinery for producing virulence factors, S. aureus can cause severe and potentially fatal infections in humans. This study focuses on the response regulator AgrA, which plays a crucial role in regulating the production of virulence factors in S. aureus. The objective is to identify natural compounds that can inhibit the binding of AgrA to its promoter site, thus inhibiting the expression of virulence genes. To achieve this, a pharmacophore model was generated using known drugs and applied to screen the ZINC natural product database. The resulting compounds were subjected to molecular docking-based virtual screening against the C-terminal DNA binding domain of AgrA. Three compounds, namely ZINC000077269178, ZINC000051012304, and ZINC000004266026, were shortlisted based on their strong affinity for key residues involved in DNA binding and transcription initiation. Subsequently, the unbound and ligand-bound complexes were subjected to a 200 ns molecular dynamics simulation to assess their conformational stability. Various analyses, including RMSD, RMSF, Rg, SASA, Principal Component Analysis, and Gibbs free energy landscape, were conducted on the simulation trajectory. The RMSD profile indicated similar fluctuations in both bound and unbound structures, while the Rg profile demonstrated the compactness of the protein without any unfolding during the simulation. Furthermore, Principal component analysis revealed that ligand binding reduced the overall atomic motion of the protein whereas free energy landscape suggested the energy variations obtained in complexes.Communicated by Ramaswamy H. Sarma.

Perspectives on current approaches to virtual screening in drug discovery

INTRODUCTION

For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

AREAS COVERED

The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

EXPERT OPINION

The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Identification of potential biogenic chalcones against antibiotic resistant efflux pump (AcrB) via computational study

The cases of bacterial multidrug resistance are increasing every year and becoming a serious concern for human health. Multidrug efflux pumps are key players in the formation of antibiotic resistance, which transfer out a broad spectrum of drugs from the cell and convey resistance to the host. Efflux pumps have significantly reduced the efficacy of the previously available antibiotic armory, thereby increasing the frequency of therapeutic failures. In gram-negative bacteria, the AcrAB-TolC efflux pump is the principal transporter of the substrate and plays a major role in the formation of antibiotic resistance. In the current work, advanced computer-aided drug discovery approaches were utilized to find hit molecules from the library of biogenic chalcones against the bacterial AcrB efflux pump. The results of the performed computational studies via molecular docking, drug-likeness prediction, pharmacokinetic profiling, pharmacophore mapping, density functional theory, and molecular dynamics simulation study provided ZINC000004695648, ZINC000014762506, ZINC000014762510, ZINC000095099506, and ZINC000085510993 as stable hit molecules against the AcrB efflux pumps. Identified hits could successfully act against AcrB efflux pumps after optimization as lead molecules.Communicated by Ramaswamy H. Sarma.

In Silico Drug Repurposing Uncovered the Antiviral Potential of the Antiparasitic Drug Oxibendazole Against the Chikungunya Virus

Chikungunya virus (CHIKV) has been reported in over 120 countries and is the causative agent of Chikungunya fever. The debilitating nature of this disease, which can persist months to years after acute infection, drastically impacts the quality of life of patients. Yet, specific antivirals are lacking for the treatment of this disease, which makes the search for new drugs necessary. In this context, the nsP2 protease emerges as an attractive therapeutic target, and drug repurposing strategies have proven to be valuable. Therefore, we combined in silico and in vitro methods to identify known drugs as potential CHIKV nsP2 protease inhibitors with antiviral properties within DrugBank. Herein, we developed a hybrid virtual screening pipeline comprising pharmacophore- and target-based screening, drug-like, and pharmaceutical filtering steps. Six virtual hits were obtained, and two of them, capecitabine (CPB) and oxibendazole (OBZ), were evaluated against CHIKV replication in Vero cells. CPB did not present antiviral activity, whereas OBZ inhibited the replication of two different strains of CHIKV, namely, 181-25 (Asian genotype) and BRA/RJ/18 (clinical isolate from ECSA genotype). OBZ showed potent antiviral activity against the CHIKV BRA/RJ/18 (EC50 = 11.4 μM) with a high selectivity index (>44). Analogs of OBZ (albendazole, fenbendazole, and mebendazole) were also evaluated, but none exhibited anti-CHIKV activity, and further, their stereoelectronic features were analyzed. Additionally, we observed that OBZ acts mainly at post-entry steps. Hence, our results support further in vivo studies to investigate the antiviral potential of OBZ, which offers a new alternative to fight CHIKV infections.

Targeting the GPI transamidase subunit GPAA1 abrogates the CD24 immune checkpoint in ovarian cancer

CD24 is frequently overexpressed in ovarian cancer and promotes immune evasion by interacting with its receptor Siglec10, present on tumor-associated macrophages, providing a "don't eat me" signal that prevents targeting and phagocytosis by macrophages. Factors promoting CD24 expression could represent novel immunotherapeutic targets for ovarian cancer. Here, using a genome-wide CRISPR knockout screen, we identify GPAA1 (glycosylphosphatidylinositol anchor attachment 1), a factor that catalyzes the attachment of a glycosylphosphatidylinositol (GPI) lipid anchor to substrate proteins, as a positive regulator of CD24 cell surface expression. Genetic ablation of GPAA1 abolishes CD24 cell surface expression, enhances macrophage-mediated phagocytosis, and inhibits ovarian tumor growth in mice. GPAA1 shares structural similarities with aminopeptidases. Consequently, we show that bestatin, a clinically advanced aminopeptidase inhibitor, binds to GPAA1 and blocks GPI attachment, resulting in reduced CD24 cell surface expression, increased macrophage-mediated phagocytosis, and suppressed growth of ovarian tumors. Our study highlights the potential of targeting GPAA1 as an immunotherapeutic approach for CD24+ ovarian cancers.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Integrated use of ligand and structure-based virtual screening, molecular dynamics, free energy calculation and ADME prediction for the identification of potential PTP1B inhibitors

Protein tyrosine phosphatases (PTPs) are the group of enzymes that control both cellular activity and the dephosphorylation of tyrosine (Tyr)-phosphorylated proteins. Dysregulation of PTP1B has contributed to numerous diseases including Diabetes Mellitus, Alzheimer's disease, and obesity rendering PTP1B as a legitimate target for therapeutic applications. It is highly challenging to target this enzyme because of its highly conserved and positively charged active-site pocket motivating researchers to find novel lead compounds against it. The present work makes use of an integrated approach combining ligand-based and structure-based virtual screening to find hit compounds targeting PTP1B. Initially, pharmacophore modeling was performed to find common features like two hydrogen bond acceptors, an aromatic ring and one hydrogen bond donor from the potent PTP1B inhibitors. The dataset of compounds matching with the common pharmacophoric features was filtered to remove Pan-Assay Interference substructure and to match the Lipinski criteria. Then, compounds were further prioritized using molecular docking and top fifty compounds with good binding affinity were selected for absorption, distribution, metabolism, and excretion (ADME) predictions. The top five compounds with high solubility, absorption and permeability holding score of - 10 to - 9.3 kcal/mol along with Ertiprotafib were submitted to all-atom molecular dynamic (MD) studies. The MD studies and binding free energy calculations showed that compound M4, M5 and M8 were having better binding affinity for PTP1B enzyme with ∆Gtotal score of - 24.25, - 31.47 and - 33.81 kcal/mol respectively than other compounds indicating that compound M8 could be a suitable lead compound as PTP1B inhibitor.

Multi-targeted anti-Alzheimer's agents: Synthesis, biological evaluation, and molecular modeling study of some pyrazolopyridine hybrids

A new series of compounds bearing a pyrazolopyridine scaffold was synthesized as integrated anti-Alzheimer's disease (AD) multi-targeted ligands. Compounds 49 and 51 showed remarkable activity as hAChE inhibitors with IC50 values of 0.17 and 0.16 μM, respectively; and proved to be active hBuChE inhibitors with IC50 values 0.17 and 0.69 μM, eight and two-fold more active than the reference compound rivastigmine, respectively. Compounds 49 and 51 showed potent GSK3β inhibition with IC50 values of 0.21 and 0.26 μM, respectively compared to L807mts. Also, 49 and 51 showed 66.0 and 60.0% as tau protein aggregation inhibitors; and Aβ1-42 self-aggregation inhibitors with 79.0 and 75.0% respectively. Furthermore, 49 and 51 could bind virtually with the PAS affecting Aβ aggregation, thus preventing Aβ-dependent neurotoxicity. They proved to have the ability to chelate bio-metals such as Fe2+, Cu2+, and Zn2+ preventing their oxidative damage in the brain of AD patients, in addition to their safety upon WI-38 cell line. Both compounds could virtually penetrate BBB and obeyed Lipinski's rule of five. Compounds 49 and 51 could be considered as MTDLs for AD patients and the obtained model and pattern of substitution could be used for further development of new multi-targeted anti-Alzheimer's agents.

Pharmacophore modelling-based drug repurposing approaches for monkeypox therapeutics

Monkeypox is a zoonotic viral disease that mainly affects tropical rainforest regions of central and west Africa, with sporadic exportations to other places. Since there is no cure, treating monkeypox with an antiviral drug developed for smallpox is currently acceptable. Our study mainly focused on finding new therapeutics to target monkeypox from existing compounds or medications. It is a successful method for discovering or developing medicinal compounds with novel pharmacological or therapeutic applications. In this study, homology modelling developed the Monkeypox VarTMPK (IMNR) structure. Ligand-based pharmacophore was generated using the best docking pose of standard ticovirimat. Further, molecular docking analysis showed compounds, tetrahydroxycurcumin, procyanidin, rutin, vicenin-2, kaempferol 3-(6''-malonylglucoside) were the top five binding energy compounds against VarTMPK (1MNR). Furthermore, we carried out MD simulations for 100 ns for the six compounds, including reference based on the binding energies and interactions. MD studies revealed that as ticovirimat interacted with residues Lys17, Ser18, and Arg45, all the above five compounds interacted with the same amino acids at the active site during docking and simulation studies. Among all the compounds, ZINC4649679 (Tetrahydroxycurcumin) was shown to have the highest binding energy -9.7 kcal/mol and also observed stable protein-ligand complex during MD studies. ADMET profile estimation showed that the docked phytochemicals were safe. However, further biological assessment through a wet lab is essential to measure the efficacy and safety of the compounds.Communicated by Ramaswamy H. Sarma.

Exploring the possibility of drug repurposing for cancer therapy targeting human lactate dehydrogenase A: a computational approach

Human lactate dehydrogenase A (LDHA) is an anaerobic glycolytic enzyme involved in the inter-conversion of pyruvate to lactate. The level of LDHA in various types of cancer cells is found to be elevated and the dependence of cancer cells on anaerobic glycolysis is viewed as the reason for this elevation. Moreover, inhibition of LDHA activity has been shown to be effective in impairing the growth of tumors, making the LDHA as a potential target for cancer therapy. In this computational study, we have performed a pharmacophore based screening of approved drugs followed by a molecular docking based screening to find a few potential LDHA inhibitors. Molecular dynamics simulations have also been performed to examine the stability of the LDHA-drug complexes as obtained from the docking study. The result of the study showed that darunavir, moxalactam and eprosartan can bind to the active site of LDHA with high affinity in comparison to two known synthetic inhibitors of LDHA. The results of the molecular dynamics simulation showed that these drugs can bind stably with the enzyme through hydrogen bond and hydrophobic interactions. Hence, it is concluded that darunavir, moxalactam and eprosartan may be considered as potential inhibitors of LDHA and can be used for cancer therapy after proper validation of their effectiveness through in vitro, in vivo and clinical trials.Communicated by Ramaswamy H. Sarma.

Anti-COVID-19, Anti-Inflammatory, and Anti-Osteoarthritis Activities of Sesamin from Sesamum indicum L

During the COVID-19 (coronavirus disease 2019) outbreak, many people were infected, and the symptoms may persist for several weeks or months for recovering patients. This is also known as "long COVID" and includes symptoms such as fatigue, joint pain, muscle pain, et cetera. The COVID-19 virus may trigger hyper-inflammation associated with cytokine levels in the body. COVID-19 can trigger inflammation in the joints, which can lead to osteoarthritis (OA), while long-term COVID-19 symptoms may lead to joint damage and other inflammation problems. According to several studies, sesame has potent anti-inflammatory properties due to its major constituent, sesamin. This study examined sesamin's anti-inflammatory, anti-osteoarthritis, and anti-COVID-19 effects. Moreover, in vivo and in vitro assays were used to determine sesamin's anti-inflammatory activity against the RAW264.7 and SW1353 cell lines. Sesamin had a dose-dependent effect (20 mg/kg) in a monoiodoacetic acid (MIA)-induced osteoarthritis rat model. Sesamin reduced paw swelling and joint discomfort. In addition, the findings indicated that sesamin suppressed the expression of iNOS (inducible nitric oxide synthase) and COX-2 (cyclooxygenase-2) in the RAW264.7 cell line within the concentration range of 6.25-50 μM. Furthermore, sesamin also had a suppressive effect on MMP (matrix metalloproteinase) expression in chondrocytes and the SW1353 cell line within the same concentration range of 6.25-50 μM. To examine the anti-viral activity, an in silico analysis was performed to evaluate sesamin's binding affinity with SARS-CoV-2 RdRp (severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase) and human ACE2 (angiotensin-converting enzyme 2). Compared to the controls, sesamin exhibited strong binding affinities towards SARS-CoV-2 RdRp and human ACE2. Furthermore, sesamin had a higher binding affinity for the ACE2 target protein. This study suggests that sesamin shows potential anti-SARS-CoV-2 activity for drug development.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

A pharmacophore-guided deep learning approach for bioactive molecular generation

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

Ligand-based pharmacophore modelling, virtual screening and docking studies to identify potential compounds against FtsZ of Mycobacterium tuberculosis

BACKGROUND AND INTRODUCTION

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (M.tb) which is the most common cause of death from bacterial illness. Millions of victims of TB infections have been recorded including 20,800 deaths amongst HIV positive individuals. Hence, there is a rising need for new and active compounds against M. tb protein targets especially as there is a persistent resistance to the current drug treatment regime.

AIM

This study identifies new potential compounds against the M. tb target protein ftsZ via pharmacophore modelling, QSAR analysis and docking studies.

METHOD

Inhibitors with known PIC50 were used as a training set and the pharmacophore features (1 aromatic center, 2 hydrophobic, 2 hydrogen bond acceptors and 1 hydrogen bond donor) were validated against four test set compounds. The identified hits were subjected to rigorous ADMET properties and docked using PyRx. DS visualizer was used in binding interactions study. Stability was measured based on the total number of interactions and preference given to the number of hydrogen bond interactions.

RESULTS

Based on the number of interactions, hydrogen bonds, extensive virtual screening and ADMET filtration, 40 compounds have been identified as potential inhibitors of ftsZ with only 3 considered to be the best leads.

SIGNIFICANCE OF RESEARCH

The identified compounds have potential of being drug candidate against Mycobacterium tuberculosis and may possess a novel mechanistic route in inhibiting the resistant strains.

Identification of hits as anti-obesity agents against human pancreatic lipase via docking, drug-likeness, in-silico ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis

Obesity, characterized by excessive fat accumulation, is a major health concern. Inhibition of human pancreatic lipase, an enzyme involved in fat digestion, offers a potential strategy for weight loss and obesity treatment. This study aimed to identify polyphenols capable of forming stable complexes with human pancreatic lipase to block its activity. Molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and MMPBGBSA calculations were employed to evaluate ligand binding, stability, and energy profiles. Pharmacophore modeling was also performed to identify key structural features for effective inhibition. Virtual screening identified ZINC000015120539, ZINC000000899200, ZINC000001531702, and ZINC000013340267 as potential candidates, exhibiting favorable binding and stable interactions over 100 ns MD simulations. These findings provide insights into the inhibitory potential of selected polyphenols on human pancreatic lipase and support further experimental investigations for obesity treatment.Communicated by Ramaswamy H. Sarma.

Structural basis of antibacterial photodynamic action of curcumin against S. aureus

Antimicrobial photodynamic therapy (aPDT) is an alternative tool to commercial antibiotics for the inactivation of pathogenic bacteria (e.g., S. aureus). However, there is still a lack of understanding of the molecular modeling of the photosensitizers and their mechanism of action through oxidative pathways. Herein, a combined experimental and computational evaluation of curcumin as a photosensitizer against S. aureus was performed. The radical forms of keto-enol tautomers and the energies of curcumin's frontier molecular orbitals were evaluated by density functional theory (DFT) to point out the photodynamic action as well as the photobleaching process. Furthermore, the electronic transitions of curcumin keto-enol tautomers were undertaken to predict the transitions as a photosensitizer during the antibacterial photodynamic process. Moreover, molecular docking was used to evaluate the binding affinity with the S. aureus tyrosyl-tRNA synthetase as the proposed a target for curcumin. In this regard, the molecular orbital energies show that the curcumin enol form has a character of 4.5% more basic than the keto form - the enol form is a more promising electron donor than its tautomer. Curcumin is a strong electrophile, with the enol form being 4.6% more electrophilic than its keto form. In addition, the regions susceptible to nucleophilic attack and photobleaching were evaluated by the Fukui function. Regarding the docking analysis, the model suggested that four hydrogen bonds contribute to the binding energy of curcumin's interaction with the ligand binding site of S. aureus tyrosyl-tRNA synthetase. Finally, residues Tyr36, Asp40, and Asp177 contact curcumin and may contribute to orienting the curcumin in the active area. Moreover, curcumin presented a photoinactivation of 4.5 log unit corroborating the necessity of the combined action of curcumin, light, and O2 to promote the photooxidation damage of S. aureus. These computational and experimental data suggest insights regarding the mechanism of action of curcumin as a photosensitizer to inactivate S. aureus bacteria.

Identification of potential inhibitors for N-myristoyltransferase (NMT) protein of Plasmodium vivax

Malaria is a neglected parasitic infection of global importance. It is mainly present in tropical countries and caused by a protozoa that belongs to the genus Plasmodium. The disease vectors are female Anopheles mosquitoes infected with the Plasmodium spp. According to the World Health Organization (WHO), there were 241 million malaria cases worldwide in 2020 and approximately 627 thousand malaria deaths in the same year. The increasing resistance to treatment has been a major problem since the beginning of the 21st century. New studies have been conducted to find possible drugs that can be used for the eradication of the disease. In this scenario, a protein named N-myristoyltransferase (NMT) has been studied as a potential drug target. NMT has an important role on the myristoylation of proteins and binds to the plasma membrane, contributing to the stabilization of protein-protein interactions. Thus, inhibition of NMT can lead to death of the parasite cell. Therefore, in order to predict and detect potential inhibitors against Plasmodium NMT, Computer-Aided Drug Design techniques were used in this research that involve virtual screening, molecular docking, and molecular dynamics. Three potential compounds similar to a benzofuran inhibitor were identified as stable PvNMT ligands. These compounds (EXP90, ZBC205 and ZDD968) originate from three different sources, respectively: a commercial library, a natural product library, and the FDA approved drugs dataset. These compounds may be further tested in in vitro and in vivo inhibition tests against Plasmodium vivax NMT.Communicated by Ramaswamy H. Sarma.

Molecular basis of two pyrimidine-sulfonylurea herbicides: from supramolecular arrangement to acetolactate synthase inhibition

CONTEXT

The design and synthesis of safe and highly active sulfonylurea herbicides is still a challenge. Therefore, following some principles of structure-activity relationship (SAR) of sulfonylurea herbicides, this work focuses on evaluating two sulfonylurea derivatives bearing electron-withdrawing substituents, namely, -(CO)OCH₃ and -NO₂ on the aryl group, on herbicidal activity. To understand the effects caused by the substituent groups, the molecular and electronic structures of the sulfonylureas were evaluated by density functional theory. Likewise, the crystalline supramolecular arrangements of both compounds were analyzed by Hirshfeld surface, QTAIM, and NBO, with the aim of verifying changes in intermolecular interactions caused by substituent groups. Finally, through a toxicophoric analysis, we were able to predict the interacting groups in their biological target, acetolactate synthase, and verify the interactions with the binding site.

METHODS

All theoretical calculations were conducted using the highly parameterized empirical exchange-correlation functional M06-2X accompanied by the diffuse and polarized basis set 6-311++G(d,p). The atomic coordinates were obtained directly from the crystalline structures, and from the energies of the frontier molecular orbitals (HOMO and LUMO), chemical descriptors were obtained that indicated the influence of the functional groups in the sulfonylureas on the reactivity of the molecules. The intermolecular interactions in the crystals were analyzed using the Hirshfeld, QTAIM, and NBO surfaces. Toxicophoric modeling was performed by the PharmaGist webserver and molecular docking calculations were performed by the GOLD 2022.1.0 software package so that the ligand was fitted to the binding site in a 10 Å sphere. For this, genetic algorithm parameters were used using the ChemPLP scoring function for docking and ASP for redocking.

Ferroptosis Mediated Novel Drug Design Approach in the Treatment of Oral Squamous Cell Carcinoma

BACKGROUND

Globally, Oral Squamous Cell Carcinoma (OSCC) is the highest prevalent type of oral cancer. Implementing a successful treatment plan for the aforementioned tumor has always been a primary concern. There are numerous targeted therapies of which Ferroptosis has been receiving increasing attention in the recent decade. A novel form of controlled cell death "Ferroptosis' is caused by iron-dependent lipid peroxidation. A well-known mechanism for controlling ferroptosis is the Cysteine/GSH/GPX4 axis, in which System X͞c is crucial. System X͞c inhibitors have been proven earlier to improve chemotherapy sensitivity.

MATERIALS AND METHODS

Five System X͞c inhibitors were selected from the literature. The structure of these molecules from Zinc15 and the protein sequence of the target from Protein Data Bank were obtained. Twenty new molecules were identified following pharmacophore modeling and were docked with the target protein using SwissDock. The binding energies of the new molecules with the target were compared with that of the reported molecules.

RESULT

The molecular docking study showed that two new molecules (ZINC89362298 and ZINC1730544) resulted in the highest binding pattern (-8.64) than that of the reported molecules (-7.75).

CONCLUSION

The present study concluded that ZINC89362298 and ZINC1730544 had better binding efficiencies than that of the reported System xc- inhibitors. Hence these two molecules could be used in targeted drug therapy and could be a promising lead in the management of oral cancer in the future.

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Development of Multi-Target Pharmacophore-Based Virtual Screening Agent Against COVID-19.. [DOI: 10.21203/rs.3.rs-2975975/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The worldwide outbreak of the COVID-19 pandemic compelled scientists to develop new, highly effective therapeutic approaches to fight it. Multitarget drugs have been proven to be effective in managing complex disorders. But designing multitarget drugs is a great challenge. In this study, to prevent lack of efficacy due to viral mutation escape, a multi-target agent against the COVID-19 virus was discovered. As crucial targets, RNA-dependent RNA polymerase (RdRp), COVID-19 main protease (Mpro), and SARS-CoV-2 Nsp15 were selected. A pharmacophore model was developed using the native ligands of the chosen targets. This model was used to screen the ZINC Drug Database for commercially available compounds having similar features to the experimentally tested drugs. Pharmacophore-based virtual screening yielded 1331 hits, which were further docked into the binding sites of selected proteins using PyRx AutoDock Vina. Evaluation of docking results revealed that glisoxepide (Zn 00537804) has the highest binding scores for the three target proteins. It showed binding free energies of -6.8, -6.2, and -7.8 kcal/mol towards SARS-CoV-2 Mpro, Nsp15, and RdRp, respectively. According to an in silicoADME study, glisoxepide follows Lipinski's rule. The results of a molecular dynamics simulation study and subsequent investigations showed that glisoxepide had good dynamics and stability within the active sites of selected targets. The promise of glisoxepide as a potential treatment for SARS-CoV-2 still needs to be further evaluated through experimental research.

Design and Identification of Inhibitors for the Spike-ACE2 Target of SARS-CoV-2

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport^®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of -8.645 kcal·mol^-1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC₅₀ values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation.

Deciphering Houttuynia cordata extract as electron shuttles with anti-COVID-19 activity and its performance in microbial fuel cells

Background

Traditional herbal medicines usually contain electron shuttle (ES)-like structures compounds which are potential candidates for antiviral compounds selection. Houttuynia cordata is applied as a biomaterial to decipher its potential applications in bioenergy extraction in microbial fuel cells (MFCs) and anti-COVID-19 via molecular docking evaluation.

Methods

H. cordata leaves extracts by water and 60% ethanol solvent were analyzed for total polyphenols, antioxidant activity, cyclic voltammetry (CV), and MFCs. The bioactive compounds of H. cordata leaves extracts were assayed via LC/MS analysis. Identification of the marker substances for potential antiviral activity using a molecular docking model was provided.

Significant findings

60% ethanol extract exhibits the highest total polyphenols and antioxidant activity compared with water extracts. Bioenergy extraction in MFCs showed that 60% ethanol extracts could give 1.76-fold more power generation compared to the blank. Flavonoids and their sugar-to-glycan ratios increased after CV scanning and they are expected to be effective ES substances. Quercitrin, from the H. cordata extract that shares an ES-like structure, was found to exhibit strong binding affinities towards ACE2 and RdRp. This indicated the potential of H. cordata leaves as a promising antiviral herb.

An in silico study on pulmonary fibrosis inhibitors from Tinospora cordifolia and Curcuma longa targeting TGF-β RI

Pulmonary fibrosis is characterized by damage to the epithelial cells and alveolar-capillary basement membrane. The increased expression levels of transforming growth factor β (TGF-β) and TGF-β-receptor-1 induced differentiation of lung fibroblasts to myofibroblasts, an alarming sign and considered the hallmark event development of pulmonary fibrosis. In the current study, the stability of phytochemicals of Curcuma longa and Tinospora cordifolia as inhibitors of transforming growth factor β RI (TGF-β RI) were evaluated using molecular docking and molecular dynamics studies. A total of 108 Curcuma longa and 16 Tinospora cordifolia constituents were screened against TGF-β RI as the target. Further, their ADMET properties were evaluated using the pkCSM online server. The compounds tembetarine, magnoflorine from T. cordiolia, and 2-(Hydroxymethyl) anthraquinone and quercetin in C. longa showed significant binding affinities bonding interactions with the target, TGF-β RI, and the study was compared with the known inhibitors from the literature. The MD simulations study also supported that the selected compounds show a close affinity with the binding site and maintained stable behavior throughout the simulation time. The pharmacophore feature analysis of the selected compounds and inhibitors were analyzed using the pharmagist web server, and the common features like H-bond donor and aromatic ring were mapped.Communicated by Ramaswamy H. Sarma.

Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach

Cases of drug-resistant tuberculosis (TB) have increased worldwide in the last few years, and it is a major threat to global TB control strategies and the human population. Mycobacterium tuberculosis is a common causative agent responsible for increasing cases of TB and as reported by WHO, approximately, 1.5 million death occurred from TB in 2020. Identification of new therapies against drug-resistant TB is an urgent need to be considered primarily. The current investigation aims to find the potential biogenic chalcone against the potential targets of drug-resistant TB via in silico approach. The ligand library of biogenic chalcones was screened against DprE1. Results of molecular docking and in silico ADMET prediction revealed that ZINC000005158606 has lead-like properties against the targeted protein. Pharmacophore modeling was done to identify the pharmacophoric features and their geometric distance present in ZINC000005158606. The binding stability study performed using molecular dynamics (MD) simulation of the DprE1-ZINC000005158606 complex revealed the conformational stability of the complex system over 100 ns with minimum deviation. Further, the in silico anti-TB sensitivity of ZINC000005158606 was found to be higher as compared to the standards against Mycobacterium tuberculosis. The overall in silico investigation indicated the potential of identified hit to act as a lead molecule against Mycobacterium tuberculosis.

Structural Insight into the Working Mechanism of the FAD Synthetase from the Human Pathogen Streptococcus pneumoniae: A Molecular Docking Simulation Study

Flavin adenine dinucleotide synthetases (FADSs) catalyze FAD biosynthesis through two consecutive catalytic reactions, riboflavin (RF) phosphorylation and flavin mononucleotide (FMN) adenylylation. Bacterial FADSs have RF kinase (RFK) and FMN adenylyltransferase (FMNAT) domains, whereas the two domains are separated into two independent enzymes in human FADSs. Bacterial FADSs have attracted considerable attention as drug targets due to the fact that they differ from human FADSs in structure and domain combinations. In this study, we analyzed the putative FADS structure from the human pathogen Streptococcus pneumoniae (SpFADS) determined by Kim et al., including conformational changes of key loops in the RFK domain upon substrate binding. Structural analysis and comparisons with a homologous FADS structure revealed that SpFADS corresponds to a hybrid between open and closed conformations of the key loops. Surface analysis of SpFADS further revealed its unique biophysical properties for substrate attraction. In addition, our molecular docking simulations predicted possible substrate-binding modes at the active sites of the RFK and FMNAT domains. Our results provide a structural basis to understand the catalytic mechanism of SpFADS and develop novel SpFADS inhibitors.

Identification of potential andrographolide-based drug candidate against Keap1-Nrf2 pathway through rigorous cheminformatics screening

The Keap1-Nrf2 [Kelch-like ECH-associated protein-1-Nuclear factor erythroid-2-related factor-2] regulatory pathway plays a vital role in the protection of cells by regulating transcription of antioxidant and detoxification genes. Andrographolide (AGP) regulates the Keap1-Nrf2 pathway by inhibiting the Keap1 protein. To identify a more potent AGP analog as a therapeutic agent against Keap1 protein, in this work, cheminformatics analysis of 237 AGP analogs was carried out. Amongst these, five AGP analogs were screened through virtual screening followed by their molecular docking analysis against Keap1 protein, which revealed greater binding affinities (binding energy =  - 4.15 to - 5.59 kcal/mol) for the shortlisted AGP analogs compared to AGP (binding energy =  - 4.02 kcal/mol). Pharmacophore mapping indicated 14 spatial features, including 3 hydrogen bond acceptors and 11 hydrophobic, while ADME analysis established the potential of all five analogs as orally-active drug-like candidates based on Lipinski's rule of five. We also examined the chemical reactivity of AGP and the shortlisted AGP analogs using DFT analysis, which revealed that except for one analog (AGP_A2) all are more chemically reactive than AGP. Further, molecular dynamics simulation analysis and MM/GBSA evidenced that AGP_A1 (PubchemID-123361152), AGP_A3 (PubchemID-58209855) and AGP_A4 (PubchemID-101362374) are the best drug like candidates compared to AGP and have greater potential to activate the Keap1-Nrf2 pathway by inhibiting the Keap1 protein.

First evidence of a serine arginine protein kinase (SRPK) in leishmania braziliensis and its potential as therapeutic target

Leishmaniasis is a parasitic disease found in tropical and subtropical regions around the world, caused by parasites of the genus Leishmania. The disease is a public health concern and presents clinical manifestations that can cause death, disability, and mutilation. The parasite has promastigote (vector) and amastigote (vertebrate host) forms and kinase enzymes are involved in this differentiation process. In the present investigation, we show, for the first time, evidence of a serine/arginine protein kinase in Leshmania braziliensis (LbSRPK). Our results show that amastigotes express more LbSRPK than promastigotes.  Analogues of SRPIN340 (a known inhibitor of SRPK) were evaluated for their leishmanicidal activity and two of them, namely SRVIC22 and SRVIC32 showed important leishmanicidal activity in vitro. SRVIC22 and SRVIC32 were able to reduce the infection rate in macrophages and the number of intracellular amastigotes by 55 and 60%, respectively. Bioinformatics analysis revealed the existence of two different amino acid residues in the active site of LbSRPK compared to their human homologue (Tyr/Leu-and Ser/Tyr), which could explain the absence of leishmanicidal activity of SRPIN340 on infected macrophages. In order to enhance leishmanicidal activity of the analogues, optimizations were proposed in the structures of the ligands, suggesting strong interactions with the catalytic site of LbSRPK. Although the evidence on the action of inhibitors upon LbSRPK is only indirect, our studies not only reveal, for the first time, evidence of a SRPK in Leishmania, but also shed light on a new therapeutic target for drug development.

Galantamine Based Novel Acetylcholinesterase Enzyme Inhibitors: A Molecular Modeling Design Approach

Acetylcholinesterase (AChE) enzymes play an essential role in the development of Alzheimer's disease (AD). Its excessive activity causes several neuronal problems, particularly psychopathies and neuronal cell death. A bioactive pose on the hAChE B site of the human acetylcholinesterase (hAChE) enzyme employed in this investigation, which was obtained from the Protein Data Bank (PDB ID 4EY6), allowed for the prediction of the binding affinity and free binding energy between the protein and the ligand. Virtual screening was performed to obtain structures similar to Galantamine (GNT) with potential hAChE activity. The top 200 hit compounds were prioritized through the use of filters in ZincPharmer, with special features related to the pharmacophore. Critical analyses were carried out, such as hierarchical clustering analysis (HCA), ADME/Tox predictions, molecular docking, molecular simulation studies, synthetic accessibility (SA), lipophilicity, water solubility, and hot spots to confirm the stable binding of the two promising molecules (ZINC16951574-LMQC2, and ZINC08342556-LMQC5). The metabolism prediction, with metabolites M3-2, which is formed by Glutathionation reaction (Phase II), M1-2, and M2-2 formed from the reaction of S-oxidation and Aliphatic hydroxylation (Phase I), were both reactive but with no side effects. Theoretical synthetic routes and prediction of synthetic accessibility for the most promising compounds are also proposed. In conclusion, this study shows that in silico modeling can be used to create new drug candidate inhibitors for hAChE. The compounds ZINC16951574-LMQC2, and ZINC08342556-LMQC5 are particularly promising for oral administration because they have a favorable drug-likeness profile, excellent lipid solubility, high bioavailability, and adequate pharmacokinetics.

An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus

BACKGROUND

The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere.

METHODS

In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result.

RESULT

We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (-7.7 and -7.3 kcal mol-1) when compared with the control drug Telaprevir (-5.9 and -6.0 kcal mol-1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition.

CONCLUSION

To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus.

Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking

Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30 target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30 proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcal mol^-1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcal mol^-1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligand-protein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10 kcal mol^-1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition.

A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors

Despite COVID-19 vaccination, immune escape of new SARS-CoV-2 variants has created an urgent priority to identify additional antiviral drugs. Targeting main protease (Mpro) expressed by SARS-CoV-2 is a therapeutic strategy for drug development due to its prominent role in viral replication cycle. Leaves of Murraya koenigii are used in various traditional medicinal applications and this plant is known as a rich source of carbazole alkaloids. Thus, this computational study was designed to investigate the inhibitory potential of carbazole alkaloids from Murraya koenigii against Mpro. Molecular docking was initially used to determine the binding affinity and molecular interactions of carbazole alkaloids and the reference inhibitor (3WL) in the active site of SARS-CoV-2 Mpro (PDB ID: 6M2N).The top scoring compounds were further assessed for protein structure flexibility, physicochemical properties and drug-likeness, pharmacokinetic and toxicity (ADME/T) properties, antiviral activity, and pharmacophore modeling. Five carbazole alkaloids (koenigicine, mukonicine, o-methylmurrayamine A, koenine, and girinimbine) displayed a unique binding mechanism that shielded the catalytic dyad of Mpro with stronger binding affinities and molecular interactions than 3WL. Furthermore, the compounds with high affinity displayed favorable physicochemical and ADME/T properties that satisfied the criteria for oral bioavailability and druggability. The pharmacophore modeling study shows shared pharmacophoric features of those compounds for their biological interaction with Mpro. During the molecular dynamics simulation, the top docking complexes demonstrated precise stability except koenigicine. Therefore, mukonicine, o-methylmurrayamine A, koenine, and girinimbine may have the potential to restrict SARS-CoV-2 replication by inactivating the Mpro catalytic activity.

Screening of BACE1 inhibitors with antiamyloidogenic activity: A study of flavonoids and flavonoid derivatives

Aggregates of β-amyloid peptide are found to occur in brains of AD patients and are formed upon sequential cleavage of the amyloid precursor protein by BACE1 and γ-secretase. Strategies inhibiting either peptide aggregation or the rate limiting enzyme BACE1 have been in demand for its implication in AD therapeutics. The present study is undertaken to mine compounds with dual ability. In this context, some natural compounds that were already predicted as BACE1 inhibitors by our group, were further tested for their activity as aggregation inhibitors. A pharmacophore model built with known antiamyloidogenic compounds was then applied for screening the natural compounds previously predicted as BACE1 inhibitors. Subsequently experimental validation by Thioflavin-T and Aβ-GFP assay filtered four compounds genistein, syringetin, tamarixetin and ZINC53276039. Out of them, ZINC53276039 showed promising antiamyloidogenic activity to act as a potent inhibitor of aggregation. Interestingly, our previous study revealed syringetin and ZINC53276039 to be good BACE1 inhibitors while tamarixetin to be a moderate BACE1 inhibitor. These good to moderate BACE1 inhibitors with moderate to reasonable antiamyloidogenic activity might show potency in reducing the amyloid load of AD brains.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

De novo design of anti-tuberculosis agents using a structure-based deep learning method

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

Computational design, synthesis and biological evaluation of PDE5 inhibitors based on N2,N4-diaminoquinazoline and N2,N6-diaminopurine scaffolds

We report the synthesis, and characterization of twenty-nine new inhibitors of PDE5. Structure-based design was employed to modify to our previously reported 2,4-diaminoquinazoline series. Modification include scaffold hopping to 2,6-diaminopurine core as well as incorporation of ionizable groups to improve both activity and solubility. The prospective binding mode of the compounds was determined using 3D ligand-based similarity methods to inhibitors of known binding mode, combined with a PDE5 docking and molecular dynamics based-protocol, each of which pointed to the same binding mode. Chemical modifications were then designed to both increase potency and solubility as well as validate the binding mode prediction. Compounds containing a quinazoline core displayed IC₅₀s ranging from 0.10 to 9.39 µM while those consisting of a purine scaffold ranging from 0.29 to 43.16 µM. We identified 25 with a PDE5 IC₅₀ of 0.15 µM, and much improved solubility (1.77 mg/mL) over the starting lead. Furthermore, it was found that the predicted binding mode was consistent with the observed SAR validating our computationally driven approach.

In-silico molecular interactions among the secondary metabolites of Caulerpa spp. and colorectal cancer targets

Caulerpa spp. secrete more than thirty different bioactive chemicals which have already been used in cancer treatment research since they play a pivotal role in cancer metabolism. Colorectal cancer is one of the most common cancer types, thus using novel and effective chemicals for colorectal cancer treatment is crucial. In the cheminformatics pipeline of this study, ADME-Tox and drug-likeness tests were performed for filtering the secondary metabolites of Caulerpa spp. The ligands which were selected from the ADME test were used for in silico molecular docking studies against the enzymes of the oxidative branch of the pentose phosphate pathway (glucose-6-phosphate dehydrogenase and 6-phosphoglutarate dehydrogenase), which is of great importance for colorectal cancer, by using AutoDock Vina. Pharmacophore modeling was carried out to align the molecules. Molecular dynamic simulations were performed for each target to validate the molecular docking studies and binding free energies were calculated. According to the ADME test results, 13 different secondary metabolites were selected as potential ligands. Molecular docking studies revealed that vina scores of caulerpin and monomethyl caulerpinate for G6PDH were found as -10.6 kcal mol-1, -10.5 kcal mol-1, respectively. Also, the vina score of caulersin for 6PGD was found as -10.7 kcal mol-1. The highest and the lowest binding free energies were calculated for monomethyl caulerpinate and caulersin, respectively. This in silico study showed that caulerpin, monomethyl caulerpinate, and caulersin could be evaluated as promising marine phytochemicals against pentose phosphate pathway enzymes and further studies are recommended to investigate the detailed activity of these secondary metabolites on these targets.

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite-A Computational Strategy

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future.

New Insights on Glutathione's Supramolecular Arrangement and Its In Silico Analysis as an Angiotensin-Converting Enzyme Inhibitor

Angiotensin-converting enzyme (ACE) inhibitors are one of the most active classes for cardiovascular diseases and hypertension treatment. In this regard, developing active and non-toxic ACE inhibitors is still a continuous challenge. Furthermore, the literature survey shows that oxidative stress plays a significant role in the development of hypertension. Herein, glutathione's molecular structure and supramolecular arrangements are evaluated as a potential ACE inhibitor. The tripeptide molecular modeling by density functional theory, the electronic structure by the frontier molecular orbitals, and the molecular electrostatic potential map to understand the biochemical processes inside the cell were analyzed. The supramolecular arrangements were studied by Hirshfeld surfaces, quantum theory of atoms in molecules, and natural bond orbital analyses. They showed distinct patterns of intermolecular interactions in each polymorph, as well as distinct stabilizations of these. Additionally, the molecular docking study presented the interactions between the active site residues of the ACE and glutathione via seven hydrogen bonds. The pharmacophore design indicated that the hydrogen bond acceptors are necessary for the interaction of this ligand with the binding site. The results provide useful information for the development of GSH analogs with higher ACE inhibitor activity.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Pharmacophore modeling, docking and molecular dynamics simulation for identification of novel human protein kinase C beta (PKCβ) inhibitors

Protein kinase Cβ (PKCβ) is considered as an attractive molecular target for the treatment of COVID-19-related acute respiratory distress syndrome (ARDS). Several classes of inhibitors have been already identified. In this article, we developed and validated ligand-based PKCβ pharmacophore models based on the chemical structures of the known inhibitors. The most accurate pharmacophore model, which correctly predicted more than 70% active compounds of test set, included three aromatic pharmacophore features without vectors, one hydrogen bond acceptor pharmacophore feature, one hydrophobic pharmacophore feature and 158 excluded volumes. This pharmacophore model was used for virtual screening of compound collection in order to identify novel potent PKCβ inhibitors. Also, molecular docking of compound collection was performed and 28 compounds which were selected simultaneously by two approaches as top-scored were proposed for further biological research.

Identification of Potential New Aedes aegypti Juvenile Hormone Inhibitors from N-Acyl Piperidine Derivatives: A Bioinformatics Approach

Aedes aegypti mosquitoes transmit several human pathogens that cause millions of deaths worldwide, mainly in Latin America. The indiscriminate use of insecticides has resulted in the development of species resistance to some such compounds. Piperidine, a natural alkaloid isolated from Piper nigrum, has been used as a hit compound due to its larvicidal activity against Aedes aegypti. In the present study, piperidine derivatives were studied through in silico methods: pharmacophoric evaluation (PharmaGist), pharmacophoric virtual screening (Pharmit), ADME/Tox prediction (Preadmet/Derek 10.0^®), docking calculations (AutoDock 4.2) and molecular dynamics (MD) simulation on GROMACS-5.1.4. MP-416 and MP-073 molecules exhibiting ΔG binding (MMPBSA −265.95 ± 1.32 kJ/mol and −124.412 ± 1.08 kJ/mol, respectively) and comparable to holo (ΔG binding = −216.21 ± 0.97) and pyriproxyfen (a well-known larvicidal, ΔG binding= −435.95 ± 2.06 kJ/mol). Considering future in vivo assays, we elaborated the theoretical synthetic route and made predictions of the synthetic accessibility (SA) (SwissADME), lipophilicity and water solubility (SwissADME) of the promising compounds identified in the present study. Our in silico results show that MP-416 and MP-073 molecules could be potent insecticides against the Aedes aegypti mosquitoes.

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.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Integration of In Silico Strategies for Drug Repositioning towards P38α Mitogen-Activated Protein Kinase (MAPK) at the Allosteric Site

P38α mitogen-activated protein kinase (p38α MAPK), one of the p38 MAPK isoforms participating in a signaling cascade, has been identified for its pivotal role in the regulation of physiological processes such as cell proliferation, differentiation, survival, and death. Herein, by shedding light on docking- and 100-ns dynamic-based screening from 3210 FDA-approved drugs, we found that lomitapide (a lipid-lowering agent) and nilotinib (a Bcr-Abl fusion protein inhibitor) could alternatively inhibit phosphorylation of p38α MAPK at the allosteric site. All-atom molecular dynamics simulations and free energy calculations including end-point and QM-based ONIOM methods revealed that the binding affinity of the two screened drugs exhibited a comparable level as the known p38α MAPK inhibitor (BIRB796), suggesting the high potential of being a novel p38α MAPK inhibitor. In addition, noncovalent contacts and the number of hydrogen bonds were found to be corresponding with the great binding recognition. Key influential amino acids were mostly hydrophobic residues, while the two charged residues including E71 and D168 were considered crucial ones due to their ability to form very strong H-bonds with the focused drugs. Altogether, our contributions obtained here could be theoretical guidance for further conducting experimental-based preclinical studies necessary for developing therapeutic agents targeting p38α MAPK.