In Silico Discovery of Potential Inhibitors Targeting the RNA Binding Loop of ADAR2 and 5-HT2CR from Traditional Chinese Natural Compounds.

Adenosine deaminase acting on RNA 2 (ADAR2) is an important enzyme involved in RNA editing processes, particularly in the conversion of adenosine to inosine in RNA molecules. Dysregulation of ADAR2 activity has been implicated in various diseases, including neurological disorders (including schizophrenia), inflammatory disorders, viral infections, and cancers. Therefore, targeting ADAR2 with small molecules presents a promising therapeutic strategy for modulating RNA editing and potentially treating associated pathologies. However, there are limited compounds that effectively inhibit ADAR2 reactions. This study therefore employed computational approaches to virtually screen natural compounds from the traditional Chinese medicine (TCM) library. The shortlisted compounds demonstrated a stronger binding affinity to the ADAR2 (<-9.5 kcal/mol) than the known inhibitor, 8-azanebularine (-6.8 kcal/mol). The topmost compounds were also observed to possess high binding affinity towards 5-HT2CR with binding energies ranging from -7.8 to -12.9 kcal/mol. Further subjecting the top ADAR2-ligand complexes to molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations revealed that five potential hit compounds comprising ZINC000014637370, ZINC000085593577, ZINC000042890265, ZINC000039183320, and ZINC000101100339 had favorable binding free energies of -174.911, -137.369, -117.236, -67.023, and -64.913 kJ/mol, respectively, with the human ADAR2 protein. Residues Lys350, Cys377, Glu396, Cys451, Arg455, Ser486, Gln488, and Arg510 were also predicted to be crucial in ligand recognition and binding. This finding will provide valuable insights into the molecular interactions between ADAR2 and small molecules, aiding in the design of future ADAR2 inhibitors with potential therapeutic applications. The potential lead compounds were also profiled to have insignificant toxicities. A structural similarity search via DrugBank revealed that ZINC000039183320 and ZINC000014637370 were similar to naringin and naringenin, which are known adenosine deaminase (ADA) inhibitors. These potential novel ADAR2 inhibitors identified herein may be beneficial in treating several neurological disorders, cancers, viral infections, and inflammatory disorders caused by ADAR2 after experimental validation.

Investigating Environmental Determinants of Soil-Transmitted Helminths Transmission using GPS Tracking and Metagenomics Technologies.

Background: The Global Health community aims to eliminate soil-transmitted helminth (STH) infections by 2030. Current preventive methods such as Mass Drug Administration, WASH practices, and health education needs to be complimented to halt transmission. We tracked the movement of hookworm-infected and non-infected persons and investigated soil factors in the places they frequented within an endemic community to further understand the role of human movement and sources of infections. Methods: 59 positive and negative participants wore GPS tracking devices for 10 consecutive days and their movement data captured in real time. The data was overlaid on the community map to determine where each group differentially spent most of their time. Soil samples were collected from these identified sites and other communal places. Physical and chemical properties were determined for each sample using standard methods and helminth eggs cultured into larvae using the Baermann technique. Bivariate and multivariate analyses were used to determine associations between larvae counts and soil factors. Helminth species were identified with metagenomic sequencing and their distributions mapped to sampling sites in the community. Results: The study found that there was no significant difference in the average larvae counts in soil between sites assessed by infected and non-infected participants (P=0.59). However, soil factors, such as pH, carbon and sandy-loamy texture were associated with high larvae counts (P<0.001) while nitrogen and clay content were associated with low counts(P<0.001). The dominant helminth species identified were Panagrolaimus superbus (an anhydrobiotic helminth), Parastrongyloides trichosuri (a parasite of small mammals), Trichuris trichuria (whipworm), and Ancylostoma caninum (dog hookworm). Notably, no Necator americanus was identified in any soil sample. Conclusion: This study provides important insights into the association between soil factors and soil-transmitted helminths. These findings contribute to our understanding of STH epidemiology and support evidence-based decision-making for elimination strategies.

Molecular Docking and Dynamics Simulation Studies Predict Potential Anti-ADAR2 Inhibitors: Implications for the Treatment of Cancer, Neurological, Immunological and Infectious Diseases.

Altered RNA editing has been linked to several neurodevelopmental disorders, including autism spectrum disorder (ASD) and intellectual disability, in addition to depression, schizophrenia, some cancers, viral infections and autoimmune disorders. The human ADAR2 is a potential therapeutic target for managing these various disorders due to its crucial role in adenosine to inosine editing. This study applied consensus scoring to rank potential ADAR2 inhibitors after performing molecular docking with AutoDock Vina and Glide (Maestro), using a library of 35,161 compounds obtained from traditional Chinese medicine. A total of 47 compounds were predicted to be good binders of the human ADAR2 and had insignificant toxicity concerns. Molecular dynamics (MD) simulations, including the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) procedure, also emphasized the binding of the shortlisted compounds. The potential compounds had plausible binding free energies ranging from -81.304 to -1068.26 kJ/mol from the MM/PBSA calculations. ZINC000085511995, a naphthoquinone had more negative binding free energy (-1068.26 kJ/mol) than inositol hexakisphosphate (IHP) [-873.873 kJ/mol], an agonist and a strong binder of ADAR2. The potential displacement of IHP by ZINC000085511995 in the IHP binding site of ADAR2 could be explored for possible deactivation of ADAR2. Bayesian-based biological activity prediction corroborates the neuropharmacological, antineoplastic and antiviral activity of the potential lead compounds. All the potential lead compounds, except ZINC000014612330 and ZINC000013462928, were predicted to be inhibitors of various deaminases. The potential lead compounds also had probability of activity (Pa) > 0.442 and probability of inactivity (Pi) < 0.116 values for treating acute neurologic disorders, except for ZINC000085996580 and ZINC000013462928. Pursuing these compounds for their anti-ADAR2 activities holds a promising future, especially against neurological disorders, some cancers and viral infections caused by RNA viruses. Molecular interaction, hydrogen bond and per-residue decomposition analyses predicted Arg400, Arg401, Lys519, Trp687, Glu689, and Lys690 as hot-spot residues in the ADAR2 IHP binding site. Most of the top compounds were observed to have naphthoquinone, indole, furanocoumarin or benzofuran moieties. Serotonin and tryptophan, which are beneficial in digestive regulation, improving sleep cycle and mood, are indole derivatives. These chemical series may have the potential to treat neurological disorders, prion diseases, some cancers, specific viral infections, metabolic disorders and eating disorders through the disruption of ADAR2 pathways. A total of nine potential lead compounds were shortlisted as plausible modulators of ADAR2.

Mycolactone: A Broad Spectrum Multitarget Antiviral Active in the Picomolar Range for COVID-19 Prevention and Cure.

We have previously shown computationally that Mycolactone (MLN), a toxin produced by Mycobacterium ulcerans, strongly binds to Munc18b and other proteins, presumably blocking degranulation and exocytosis of blood platelets and mast cells. We investigated the effect of MLN on endocytosis using similar approaches, and it bound strongly to the N-terminal of the clathrin protein and a novel SARS-CoV-2 fusion protein. Experimentally, we found 100% inhibition up to 60 nM and 84% average inhibition at 30 nM in SARS-CoV-2 live viral assays. MLN was also 10× more potent than remdesivir and molnupiravir. MLN's toxicity against human alveolar cell line A549, immortalized human fetal renal cell line HEK293, and human hepatoma cell line Huh7.1 were 17.12%, 40.30%, and 36.25%, respectively. The cytotoxicity IC50 breakpoint ratio versus anti-SARS-CoV-2 activity was more than 65-fold. The IC50 values against the alpha, delta, and Omicron variants were all below 0.020 µM, and 134.6 nM of MLN had 100% inhibition in an entry and spread assays. MLN is eclectic in its actions through its binding to Sec61, AT2R, and the novel fusion protein, making it a good drug candidate for treating and preventing COVID-19 and other similarly transmitted enveloped viruses and pathogens.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors.

The Ebola virus (EBOV) is still highly infectious and causes severe hemorrhagic fevers in primates. However, there are no regulatorily approved drugs against the Ebola virus disease (EVD). The highly virulent and lethal nature of EVD highlights the need to develop therapeutic agents. Viral protein 40 kDa (VP40), the most abundantly expressed protein during infection, coordinates the assembly, budding, and release of viral particles into the host cell. It also regulates viral transcription and RNA replication. This study sought to identify small molecules that could potentially inhibit the VP40 protein by targeting the N-terminal domain using an in silico approach. The statistical quality of AutoDock Vina's capacity to discriminate between inhibitors and decoys was determined, and an area under the curve of the receiver operating characteristic (AUC-ROC) curve of 0.791 was obtained. A total of 29,519 natural-product-derived compounds from Chinese and African sources as well as 2738 approved drugs were successfully screened against VP40. Using a threshold of -8 kcal/mol, a total of 7, 11, 163, and 30 compounds from the AfroDb, Northern African Natural Products Database (NANPDB), traditional Chinese medicine (TCM), and approved drugs libraries, respectively, were obtained after molecular docking. A biological activity prediction of the lead compounds suggested their potential antiviral properties. In addition, random-forest- and support-vector-machine-based algorithms predicted the compounds to be anti-Ebola with IC50 values in the micromolar range (less than 25 µM). A total of 42 natural-product-derived compounds were identified as potential EBOV inhibitors with desirable ADMET profiles, comprising 1, 2, and 39 compounds from NANPDB (2-hydroxyseneganolide), AfroDb (ZINC000034518176 and ZINC000095485942), and TCM, respectively. A total of 23 approved drugs, including doramectin, glecaprevir, velpatasvir, ledipasvir, avermectin B1, nafarelin acetate, danoprevir, eltrombopag, lanatoside C, and glycyrrhizin, among others, were also predicted to have potential anti-EBOV activity and can be further explored so that they may be repurposed for EVD treatment. Molecular dynamics simulations coupled with molecular mechanics Poisson-Boltzmann surface area calculations corroborated the stability and good binding affinities of the complexes (-46.97 to -118.9 kJ/mol). The potential lead compounds may have the potential to be developed as anti-EBOV drugs after experimental testing.

Multi-dimensional structural footprint identification for the design of potential scaffolds targeting METTL3 in cancer treatment from natural compounds.

CONTEXT: [Formula: see text]-adenosine-methyltransferase (METTL3) is the catalytic domain of the 'writer' proteins which is involved in the post modifications of [Formula: see text]-methyladinosine ([Formula: see text]). Though its activities are essential in many biological processes, it has been implicated in several types of cancer. Thus, drug developers and researchers are relentlessly in search of small molecule inhibitors that can ameliorate the oncogenic activities of METTL3. Currently, STM2457 is a potent, highly selective inhibitor of METTL3 but is yet to be approved. METHODS: In this study, we employed structure-based virtual screening through consensus docking by using AutoDock Vina in PyRx interface and Glide virtual screening workflow of Schrodinger Glide. Thermodynamics via MM-PBSA calculations was further used to rank the compounds based on their total free binding energies. All atom molecular dynamics simulations were performed using AMBER 18 package. FF14SB force fields and Antechamber were used to parameterize the protein and compounds respectively. Post analysis of generated trajectories was analyzed with CPPTRAJ and PTRAJ modules incorporated in the AMBER package while Discovery studio and UCSF Chimera were used for visualization, and origin data tool used to plot all graphs. RESULTS: Three compounds with total free binding energies higher than STM2457 were selected for extended molecular dynamics simulations. The compounds, SANCDB0370, SANCDB0867, and SANCDB1033, exhibited stability and deeper penetration into the hydrophobic core of the protein. They engaged in relatively stronger intermolecular interactions involving hydrogen bonds with resultant increase in stability, reduced flexibility, and decrease in the surface area of the protein available for solvent interactions suggesting an induced folding of the catalytic domain. Furthermore, in silico pharmacokinetics and physicochemical analysis of the compounds revealed good properties suggesting these compounds could serve as promising MEETL3 entry inhibitors upon modifications and optimizations as presented by natural compounds. Further biochemical testing and experimentations would aid in the discovery of effective inhibitors against the berserk activities of METTL3.

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling.

The recent outlook of leishmaniasis as a global public health concern coupled with the reportage of resistance and lack of efficacy of most antileishmanial drugs calls for a concerted effort to find new leads. The study combined In silico and in vitro approaches to identify novel potential synthetic small-molecule inhibitors targeting the Leishmania donovani sterol methyltransferase (LdSMT). The LdSMT enzyme in the ergosterol biosynthetic pathway is required for the parasite's membrane fluidity, distribution of membrane proteins, and control of the cell cycle. The lack of LdSMT homologue in the human host and its conserved nature among all Leishmania parasites makes it a viable target for future antileishmanial drugs. Initially, six known inhibitors of LdSMT with IC50 < 10 µM were used to generate a pharmacophore model with a score of 0.9144 using LigandScout. The validated model was used to screen a synthetic library of 95,630 compounds obtained from InterBioScreen limited. Twenty compounds with pharmacophore fit scores above 50 were docked against the modelled three-dimensional structure of LdSMT using AutoDock Vina. Consequently, nine compounds with binding energies ranging from -7.5 to -8.7 kcal/mol were identified as potential hit molecules. Three compounds comprising STOCK6S-06707, STOCK6S-84928, and STOCK6S-65920 with respective binding energies of -8.7, -8.2, and -8.0 kcal/mol, lower than 22,26-azasterol (-7.6 kcal/mol), a known LdSMT inhibitor, were selected as plausible lead molecules. Molecular dynamics simulation studies and molecular mechanics Poisson-Boltzmann surface area calculations showed that the residues Asp25 and Trp208 were critical for ligand binding. The compounds were also predicted to have antileishmanial activity with reasonable pharmacological and toxicity profiles. When the antileishmanial activity of the three hits was evaluated in vitro against the promastigotes of L. donovani, mean half-maximal inhibitory concentrations (IC50) of 21.9 ± 1.5 µM (STOCK6S-06707), 23.5 ± 1.1 µM (STOCK6S-84928), and 118.3 ± 5.8 µM (STOCK6S-65920) were obtained. Furthermore, STOCK6S-84928 and STOCK6S-65920 inhibited the growth of Trypanosoma brucei, with IC50 of 14.3 ± 2.0 µM and 18.1 ± 1.4 µM, respectively. The identified compounds could be optimised to develop potent antileishmanial therapeutic agents.

Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.

The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline.

Structure-Based Identification of Natural-Product-Derived Compounds with Potential to Inhibit HIV-1 Entry.

Broadly neutralizing antibodies (bNAbs) are potent in neutralizing a wide range of HIV strains. VRC01 is a CD4-binding-site (CD4-bs) class of bNAbs that binds to the conserved CD4-binding region of HIV-1 envelope (env) protein. Natural products that mimic VRC01 bNAbs by interacting with the conserved CD4-binding regions may serve as a new generation of HIV-1 entry inhibitors by being broadly reactive and potently neutralizing. This study aimed to identify compounds that mimic VRC01 by interacting with the CD4-bs of HIV-1 gp120 and thereby inhibiting viral entry into target cells. Libraries of purchasable natural products were virtually screened against clade A/E recombinant 93TH057 (PDB: 3NGB) and clade B (PDB ID: 3J70) HIV-1 env protein. Protein-ligand interaction profiling from molecular docking and dynamics simulations showed that the compounds had intermolecular hydrogen and hydrophobic interactions with conserved amino acid residues on the CD4-binding site of recombinant clade A/E and clade B HIV-1 gp120. Four potential lead compounds, NP-005114, NP-008297, NP-007422, and NP-007382, were used for cell-based antiviral infectivity inhibition assay using clade B (HXB2) env pseudotype virus (PV). The four compounds inhibited the entry of HIV HXB2 pseudotype viruses into target cells at 50% inhibitory concentrations (IC50) of 15.2 µM (9.7 µg/mL), 10.1 µM (7.5 µg/mL), 16.2 µM (12.7 µg/mL), and 21.6 µM (12.9 µg/mL), respectively. The interaction of these compounds with critical residues of the CD4-binding site of more than one clade of HIV gp120 and inhibition of HIV-1 entry into the target cell demonstrate the possibility of a new class of HIV entry inhibitors.

Computer-aided identification of potential inhibitors against Necator americanus glutathione S-transferase 3.

Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than -9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.

EBOLApred: A machine learning-based web application for predicting cell entry inhibitors of the Ebola virus.

Ebola virus disease (EVD) is a highly virulent and often lethal illness that affects humans through contact with the body fluid of infected persons. Glycoprotein and matrix protein VP40 play essential roles in the virus life cycle within the host. Whilst glycoprotein mediates the entry and fusion of the virus with the host cell membrane, VP40 is also responsible for viral particle assembly and budding. This study aimed at developing machine learning models to predict small molecules as possible anti-Ebola virus compounds capable of inhibiting the activities of GP and VP40 using Ebola virus (EBOV) cell entry inhibitors from the PubChem database as training data. Predictive models were developed using five algorithms comprising random forest (RF), support vector machine (SVM), naïve Bayes (NB), k-nearest neighbor (kNN), and logistic regression (LR). The models were evaluated using a 10-fold cross-validation technique and the algorithm with the best performance was the random forest model with an accuracy of 89 %, an F1 score of 0.9, and a receiver operating characteristic curve (ROC curve) showing the area under the curve (AUC) score of 0.95. LR and SVM models also showed plausible performances with overall accuracy values of 0.84 and 0.86, respectively. The models, RF, LR, and SVM were deployed as a web server known as EBOLApred accessible via http://197.255.126.13:8000/.

Consensus docking and MM-PBSA computations identify putative furin protease inhibitors for developing potential therapeutics against COVID-19.

The coronavirus disease 2019 (COVID-19) is a pandemic that has severely posed substantial health challenges and claimed millions of lives. Though vaccines have been produced to stem the spread of this disease, the death rate remains high since drugs used for treatment have therapeutic challenges. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease, has a slew of potential therapeutic targets. Among them is the furin protease, which has a cleavage site on the virus's spike protein. The cleavage site facilitates the entry of the virus into human cells via cell-cell fusion. This critical involvement of furin in the disease pathogenicity has made it a viable therapeutic strategy against the virus. This study employs the consensus docking approach using HYBRID and AutoDock Vina to virtually screen a pre-filtered library of 3942 natural product compounds of African origin against the human furin protease (PDB: 4RYD). Twenty of these compounds were selected as hits after meeting molecular docking cut-off of - 7 kcal.mol-1, pose alignment inspection, and having favorable furin-ligand interactions. An area under the curve (AUC) value of 0.72 was computed from the receiver operator characteristic (ROC) curve, and Boltzmann-enhanced discrimination of the ROC curve (BEDROC) value of 0.65 showed that AutoDock Vina was a reasonable tool for selecting actives for this target. Seven of these hits were proposed as potential leads having had bonding interactions with catalytic triad residues Ser368, His194, and Asp153, and other essential residues in the active site with plausible binding free energies between - 189 and - 95 kJ/mol from the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations as well as favorable ADME/Tox properties. The molecules were also predicted as antiviral, anti-inflammatory, membrane permeability inhibitors, RNA synthesis inhibitors, cytoprotective, and hepatoprotective with probable activity (Pa) above 0.5 and probable inactivity values below 0.1. Some of them also have anti-influenza activity. Influenza virus has many similarities with SARS-CoV-2 in their mode of entry into human cells as both are facilitated by the furin protease. Pinobanksin 3-(E)-caffeate, one of the potential leads is a propolis compound. Propolis compounds have shown inhibitory effects against ACE2, TMPRSS2, and PAK1 signaling pathways of SARS-CoV-2 in previous studies. Likewise, quercitrin is structurally similar to isoquercetin, which is currently in clinical trials as possible medication for COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02056-1.

Molecular Modelling and Atomistic Insights into the Binding Mechanism of MmpL3 Mtb.

Mycobacterial membrane proteins Large (MmpLs), which belong to the resistance, nodulation, and division (RND) protein superfamily, play critical roles in transporting polymers, lipids, and immunomodulators. MmpLs have become one of the important therapeutic drug targets to emerge in recent times. In this study, two homology modelling techniques, Modeller and SWISS-MODEL, were used in modelling the three-dimensional protein structure of the MmpL3 of Mycobacterium tuberculosis using that of M. smegmatis as template. MmpL3 inhibitors, namely BM212, NITD304, SPIRO, and NITD349, in addition to the co-crystalized ligands AU1235, ICA38, SQ109 and rimonabant, were screened against the modelled structure and the Mmpl3 of M. smegmatis using molecular docking techniques. Protein-ligand interactions were analysed using molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann surface area computations. Novel residues Gln32, Leu165, Ile414, and Phe35 were identified as critical for binding to M. tuberculosis MmpL3, and conformational dynamics upon inhibitor binding were discussed.

Density Functional Theory-Based Studies Predict Carbon Nanotubes as Effective Mycolactone Inhibitors.

Fullerenes, boron nitride nanotubes (BNNTs), and carbon nanotubes (CNTs) have all been extensively explored for biomedical purposes. This work describes the use of BNNTs and CNTs as mycolactone inhibitors. Density functional theory (DFT) has been used to investigate the chemical properties and interaction mechanisms of mycolactone with armchair BNNTs (5,5) and armchair CNTs (5,5). By examining the optimized structure and interaction energy, the intermolecular interactions between mycolactone and nanotubes were investigated. The findings indicate that mycolactone can be physically adsorbed on armchair CNTs in a stable condition, implying that armchair CNTs can be potential inhibitors of mycolactone. According to DOS plots and HOMO-LUMO orbital studies, the electronic characteristics of pure CNTs are not modified following mycolactone adsorption on the nanotubes. Because of mycolactone's large π-π interactions with CNTs, the estimated interaction energies indicate that mycolactone adsorption on CNTs is preferable to that on BNNTs. CNTs can be explored as potentially excellent inhibitors of mycolactone toxins in biological systems.

Targeting the Plasmodium falciparum's Thymidylate Monophosphate Kinase for the Identification of Novel Antimalarial Natural Compounds.

Recent reports of resistance to artemisinin-based combination drugs necessitate the need to discover novel antimalarial compounds. The present study was aimed at identifying novel antimalarial compounds from natural product libraries using computational methods. Plasmodium falciparum is highly dependent on the pyrimidine biosynthetic pathway, a de novo pathway responsible for the production of pyrimidines, and the parasite lacks the pyrimidine salvage enzymes. The P. falciparum thymidylate monophosphate kinase (PfTMPK) is an important protein necessary for rapid DNA replication; however, due to its broad substrate specificity, the protein is distinguished from its homologs, making it a suitable drug target. Compounds from AfroDB, a database of natural products originating from Africa, were screened virtually against PfTMPK after filtering the compounds for absorption, distribution, metabolism, excretion, and toxicity (ADMET)-acceptable compounds with FAF-Drugs4. Thirteen hits with lower binding energies than thymidine monophosphate were selected after docking. Among the thirteen compounds, ZINC13374323 and ZINC13365918 with binding energies of -9.4 and -8.9 kcal/mol, respectively, were selected as plausible lead compounds because they exhibited structural properties that ensure proper binding at the active site and inhibitory effect against PfTMPK. ZINC13374323 (also called aurantiamide acetate) is known to exhibit anti-inflammatory and antiviral activities, and ZINC13365918 exhibits antileishmanial activity. Furthermore, aurantiamide acetate, which is commercially available, is a constituent of Artemisia annua, the herb from which artemisinin was derived. The compound also shares interactions with several residues with a potent thymidine analog inhibitor of PfTMPK. The anti-plasmodial activity of aurantiamide acetate was evaluated in vitro, and the mean half-maximal inhibitory concentration (IC50) was 69.33 µM when synchronized P. falciparum 3D7 culture was used as compared to IC50 > 100 µM with asynchronized culture. The significance of our findings within the context of malaria treatment strategies and challenges is discussed.

Molecular Structure-Based Screening of the Constituents of Calotropis procera Identifies Potential Inhibitors of Diabetes Mellitus Target Alpha Glucosidase.

Diabetes mellitus is a disorder characterized by higher levels of blood glucose due to impaired insulin mechanisms. Alpha glucosidase is a critical drug target implicated in the mechanisms of diabetes mellitus and its inhibition controls hyperglycemia. Since the existing standard synthetic drugs have therapeutic limitations, it is imperative to identify new potent inhibitors of natural product origin which may slow carbohydrate digestion and absorption via alpha glucosidase. Since plant extracts from Calotropis procera have been extensively used in the treatment of diabetes mellitus, the present study used molecular docking and dynamics simulation techniques to screen its constituents against the receptor alpha glucosidase. Taraxasterol, syriogenin, isorhamnetin-3-O-robinobioside and calotoxin were identified as potential novel lead compounds with plausible binding energies of -40.2, -35.1, -34.3 and -34.3 kJ/mol against alpha glucosidase, respectively. The residues Trp481, Asp518, Leu677, Leu678 and Leu680 were identified as critical for binding and the compounds were predicted as alpha glucosidase inhibitors. Structurally similar compounds with Tanimoto coefficients greater than 0.7 were reported experimentally to be inhibitors of alpha glucosidase or antidiabetic. The structures of the molecules may serve as templates for the design of novel inhibitors and warrant in vitro assaying to corroborate their antidiabetic potential.

Therapeutic Anti-Depressant Potential of Microbial GABA Produced by Lactobacillus rhamnosus Strains for GABAergic Signaling Restoration and Inhibition of Addiction-Induced HPA Axis Hyperactivity.

The role of the microbiota-gut-brain (MGB) axis in mood regulation and depression treatment has gained attention in recent years, as evidenced by the growing number of animal and human studies that have reported the anti-depressive and associated gamma-aminobutyric acid-ergic (GABAergic) effects of probiotics developed from Lactobacillus rhamnosus bacterial strains in the gut microbiome. The depressive states attenuated by these probiotics in patients suffering from clinical depression also characterize the severe and relapse-inducing withdrawal phase of the addiction cycle, which has been found to arise from the intoxication-enabled hyperregulation of the hypothalamic-pituitary-adrenal (HPA) axis, the body's major stress response system, and a corresponding attenuation of its main inhibitory system, the gamma-aminobutyric acid (GABA) signaling system. Therefore, the use of probiotics in the treatment of general cases of depression provides hope for a novel therapeutic approach to withdrawal depression remediation. This review discusses potential therapeutic avenues by which probiotic application of Lactobacillus rhamnosus strains can be used to restore the central GABAergic activity responsible for attenuating the depression-inducing HPA axis hyperactivity in addiction withdrawal. Also, information is provided on brain GABAergic signaling from other known GABA-producing strains of gut microbiota.

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .

Prediction of antischistosomal small molecules using machine learning in the era of big data.

Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with different mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It first discusses the challenges of drug discovery in schistosomiasis. Explanations are then offered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artificial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classification in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classification. Databases specifically designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models. This shows the application of machine learning techniques for the discovery of novel antischistosomal small molecules via binary classification in the era of big data.

The emerging paradigm of calcium homeostasis as a new therapeutic target for protozoan parasites.

Calcium channels (CCs), a group of ubiquitously expressed membrane proteins, are involved in many pathophysiological processes of protozoan parasites. Our understanding of CCs in cell signaling, organelle function, cellular homeostasis, and cell cycle control has led to improved insights into their structure and functions. In this article, we discuss CCs characteristics of five major protozoan parasites Plasmodium, Leishmania, Toxoplasma, Trypanosoma, and Cryptosporidium. We provide a comprehensive review of current antiparasitic drugs and the potential of using CCs as new therapeutic targets. Interestingly, previous studies have demonstrated that human CC modulators can kill or sensitize parasites to antiparasitic drugs. Still, none of the parasite CCs, pumps, or transporters has been validated as drug targets. Information for this review draws from extensive data mining of genome sequences, chemical library screenings, and drug design studies. Parasitic resistance to currently approved therapeutics is a serious and emerging threat to both disease control and management efforts. In this article, we suggest that the disruption of calcium homeostasis may be an effective approach to develop new anti-parasite drug candidates and reduce parasite resistance.