MeFSAT: a curated natural product database specific to secondary metabolites of medicinal fungi

Molluscicidal Screening of Hypocreales Fungi from a Brazilian Cerrado Cave Against Biomphalaria glabrata Snails

Fungi play vital roles in ecosystems through parasitism, commensalism, and mutualism. Additionally, they are widely used in industry as bioactive compound producers and biological control agents. Biomphalaria glabrata is a freshwater snail often controlled with chemical molluscicides. However, developing effective alternatives to these chemical treatments is essential. This study evaluated the molluscicidal potential of culture supernatant from Hypocreales fungi isolated from a cave in the Brazilian Cerrado against the B. glabrata. The isolates were identified based on morphological features and ITS rDNA sequences. Fifteen filtrates of Hypocreales fungi were obtained and tested both pure and in different dilutions (10% and 50%) against newly hatched snails during 96 h of exposure. The fungal isolates were identified as belonging to the genera of Clonostachys (1), Cylindrocladiella (1), Fusarium (1), Gliocladiopsis (1), Keithomyces (1), Marquandomyces (1), Ovicillium (1), Pochonia (1), Purpureocillium (1), Sarcopodium (1), Sarocladium (1), Trichoderma (3), and Volutella (1). The results showed 93.33% (14) of the fungal filtrates induced significant mortality, indicating their molluscicidal activity, with Pochonia chlamydosporia FCCUFG 100 and Volutella aeria FCCUFG 107 causing 100% mortality in all dilutions. These results reveal the potential of Hypocreales fungi from a Brazilian Cerrado cave as a promising approach for snail control.

Haplotype-Phased Chromosome-Level Genome Assembly of Cryptoporus qinlingensis, a Typical Traditional Chinese Medicine Fungus

This study presents the first comprehensive genomic analysis of Cryptoporus qinlingensis, a classical folk medicine and newly identified macrofungus from the Qinling Mountains. Utilizing advanced sequencing technologies, including PacBio HiFi and Hi-C, we achieved a high-quality chromosome-level genome assembly. The genome, sized at 39.1 Mb, exhibits a heterozygosity of 0.21% and contains 21.2% repetitive sequences. Phylogenetic analysis revealed a recent divergence of C. qinlingensis from Dichomitus squalens approximately 212.26 million years ago (MYA), highlighting the rapid diversification within the Polyporaceae family. Comparative genomic studies indicate significant gene family contraction in C. qinlingensis, suggesting evolutionary adaptations. The identification of a tetrapolar mating system, along with the analysis of CAZymes and P450 genes, underscores the genomic complexity and ecological adaptability of this species. Furthermore, the discovery of 30 biosynthetic gene clusters (BGCs) related to secondary metabolites, including polyketide synthase (PKS), non-ribosomal peptide synthetase (NRPS), and terpene synthesis enzymes, opens new avenues for exploring bioactive compounds with potential medicinal applications. This research not only enriches our understanding of the Cryptoporus genus but also provides a valuable foundation for future studies aiming to harness the therapeutic potential of C. qinlingensis and to further explore its ecological and evolutionary significance.

Discovering broad-spectrum inhibitors for SARS-CoV-2 variants: a cheminformatics and biophysical approach targeting the main protease

The COVID-19 pandemic caused by SARS-CoV-2 still lacks effective antiviral drugs. Therefore, a thorough receptor-based virtual screening study was conducted to screen different natural and synthetic drug libraries, such as the Asinex Antiviral, Seaweed Metabolite Database, Medicinal Fungi Secondary Metabolite and Therapeutics Library, and Comprehensive Marine Natural Products Database comprising 6,827, 1,191, 1,830, and 45,000 compounds, respectively, against the main protease enzyme of SARS-CoV-2. Accordingly, three drug molecules (BBB-26580140, BDE-32007849, and LAS-51378804) are highlighted as the best binding molecules to the main protease S1 pocket. The docking binding energy scores of BBB-26580140, BDE-32007849, and LAS-51378804 were -13.02, -13.0, and -12.56 kcal/mol, respectively. Compared to the control Z1741970824 molecule with a binding energy score of -11.59 kcal/mol, the lead structures identified herein showed robust hydrophilic and van der Waals interactions with the enzyme active site residues, such as His41 and Cys145, and achieved highly stable binding modes. The simulations showed a stable structure of the main protease enzyme with the shortlisted leads in the pocket, and the network of binding interactions remained intact during the simulations. The overall molecular mechanics with generalized Born and surface area solvation binding energies of the BBB-26580140, BDE-32007849, LAS-51378804, and control molecules are -53.02, -56.85, -55.44, and -48.91 kcal/mol, respectively. Similarly, the net molecular mechanics Poisson-Boltzmann surface area binding energies of BBB-26580140, BDE-32007849, LAS-51378804, and control are -53.6, -57.61, -54.41, and -50.09 kcal/mol, respectively. The binding entropy energies of these systems showed lower free energies, indicating their stable nature. Furthermore, the binding energies were revalidated using the water swap method that considers the role of the water molecules in bridging the ligands to the enzyme active site residues. The compounds also revealed good ADMET properties and followed all major rules of drug-likeness. Thus, these compounds are predicted as promising leads and can be subjected to further experimental studies for evaluation of their biological activities.

Identification of Novel Drug Molecules Against NS3-Like Helicase Enzyme of Alongshan Virus

Alongshan virus (ALSV) is a novel tick-borne virus associated with human diseases. The ALSV is a segmented flavivirus from the family Flaviviridae. It is currently considered as tick-borne arbovirus. There is a high incidence of fever and headache among patients with ALSV infection, and some patients also present with fatigue, coma, depression, nausea, myalgia/arthralgia, and skin rashes. Neither a licensed vaccine nor a drug is currently available to treat ALSV. The development of new, practical, and innovative therapeutic approaches is needed to overcome the emergence of the pathogen. Research on drugs remains a complex, time-consuming, and expensive. The field of drug development has undergone a revolution due to the use of computational approaches, which provide several benefits that speed up and improve the process of developing novel drugs. The goal of this study is to identify novel drug-like molecules against NS3-like helicase enzyme of Alongshan virus. Using molecular docking, the binding potential of the top three ligands to the specified target was determined. Molecular dynamic simulations were used to identify the stabilities of the best-docked conformations followed by energy calculations and ADMET analysis. Three potential and promising compounds were identified by performing structure-based virtual screening of non-structural protein 3 (NS3) like helicase of Alongshan virus. The best-docked complexes identified through virtual screening were BDC-23169381, BDB-26412846, BDB-2641954. All these compounds had good pharmacokinetics characteristics and were identified as drug like.

Fungal secondary metabolites as a potential inhibitor of T315I- BCR::ABL1 mutant in chronic myeloid leukemia by molecular docking, molecular dynamics simulation and binding free energy exploration approaches

BACKGROUND

Chronic Myeloid Leukemia (CML) is particularly challenging to treat due to the T315I BCR::ABL1 mutation. Although fungal metabolites are known for their pharmaceutical potential, none are approved for CML. Our study screened approximately 2000 fungal secondary metabolites to discover inhibitors targeting the T315I- BCR::ABL1 mutant protein.

METHODS

We conducted comprehensive analyses to elucidate the interactions between the T315I-BCR::ABL1 mutant protein and selected fungal metabolites. These analyses included molecular docking, ADMET assessment, molecular dynamics simulations, principal components analysis, exploration of free energy landscapes, and per-residue decomposition.

RESULTS

We identified a range of binding affinities for fungal secondary metabolites, from -11.2 kcal/mol to -2.90 kcal/mol, with the co-crystal ponatinib showing a binding affinity of -9.9 kcal/mol. Notably, twenty seven fungal metabolites had affinities ≤ -10.0 kcal/mol, surpassing ponatinib. Eight compounds, including Phellifuropyranone A and Meshimakobnol B, showed favorable drug-likeness. Molecular dynamics parameters, including RMSD, RMSF, Rg, and SASA, confirmed that Phellifuropyranone A and Meshimakobnol B bind stably to the T315I-BCR::ABL1 mutant protein. Additionally, PCA, DCCM, and free energy landscapes analyses validated the consistency of the molecular dynamics parameters. MM/PBSA analysis indicated that Phellifuropyranone A (-22.88 ± 4.28 kcal/mol) and Meshimakobnol B (-25.86 ± 3.51 kcal/mol) bind similarly to ponatinib (-25.54 ± 6.31 kcal/mol). Per-residue decomposition explored residues MET290, VAL299, ILE315, and PHE359 as crucial for binding to the T315I-BCR::ABL1 mutant protein.

CONCLUSIONS

Phellifuropyranone A and Meshimakobnol B show significant potency as inhibitors of the T315I-BCR::ABL1 mutant protein, comparable to ponatinib. These compounds may serve as effective alternatives or synergistic agents with ponatinib, potentially overcoming drug resistance and improving treatment outcomes in Chronic Myeloid Leukemia.

Discovery of novel CDK4/6 inhibitors from fungal secondary metabolites

The development of targeted therapies for breast cancer, particularly those focusing on cyclin-dependent kinases 4/6 (CDK4/6), has significantly improved patient outcomes. However, the currently approved CDK4/6 inhibitors are associated with various side effects, underscoring the need for novel compounds with enhanced efficacy and safety profiles. This study aimed to identify potential CDK4/6 inhibitors from MeFSAT, a database of fungal secondary metabolites using an in-silico screening approach. The virtual screening process incorporated drug-likeness filters, ADME and toxicity predictions, consensus molecular docking, and 200 ns molecular dynamics simulations. Out of 411 initial compounds, two molecules demonstrated favorable binding interactions and stability with the CDK4/6 protein complex. The MTT assay showed that MSID000025 had dose-dependent cytotoxicity against MCF7 breast cancer cells. This suggests that MSID000025 could be a good candidate CDK4/6 inhibitor for treating breast cancer. Our study highlights the potential of fungal secondary metabolites as a source of novel compounds for drug discovery. It provides a framework for identifying CDK4/6 inhibitors with improved therapeutic properties.

Computational exploration of Ganoderma lucidum metabolites as potential anti-atherosclerotic agents: Insights from molecular docking and dynamics simulations

Ganoderma lucidum is a unique form of fungus utilized in Chinese medicine for various therapies as it exhibits a wide range of pharmacological activity. In this study, the purpose is to evaluate the possible drug-like qualities of the metabolites of G. lucidium as well as the impact that these metabolites have on the pathways involved in atherosclerosis. Throughout our research, a total of 17 compounds were chosen based on their drug-like properties. These compounds were then utilized in the subsequent networking and docking simulations. According to the findings, the compound ganodone has a maximum binding energy of -7.243 Kcal/mol. In terms of the binding energy, it has been discovered that the compound cianidanol has the lowest value. Based on the findings of the molecular docking investigations, it was determined that TNF, AKT1, SRC, and STAT3 exhibited a higher affinity for the complex. To determine this, molecular dynamics simulation was performed for about 100 nanoseconds. Following the completion of the GO functional analysis, it was discovered that the target genes were involved in the processes of protein binding, ATP binding, enzyme binding, and protein tyrosine kinase activity. Overall, the study results provide a view of possible metabolites that may have an impact on disease progression.

In silico identification of multi-target inhibitors from medicinal fungal metabolites against the base excision repair pathway proteins of African swine fever virus

African swine fever virus (ASFV) has emerged as a serious threat to the pork industry resulting in significant economic losses and heightened concerns about food security. With no known cure presently available, existing control measures center on animal quarantine and culling. Considering the severity and challenges posed by ASFV, it is imperative to discover new treatment strategies and implement additional measures to prevent its further spread. This study recognized the potential of 1830 fungal metabolites from medicinal fungi as antiviral compounds against base excision repair (BER) proteins of ASFV, specifically ASFVAP, ASFVPolX, and ASFVLig. A wide array of computer-aided drug discovery techniques were employed to carry out the virtual screening process: ADMET profiling revealed 319 molecules with excellent bioavailability and toxicity properties; consensus docking identified the 10 best-scoring ligands against all targets; molecular dynamics simulation elucidated the stability of the protein-ligand complexes; and MM/PB(GB)SA energy calculations predicted the binding energies of the compounds as well as the key residues integral to binding. Through in silico methods, we identified two theoretical lead candidates against ASFVAP, four against ASFVLig, and five against ASFVPolX. Two compounds, methyl ganoderate E and antcamphin M, exhibited potential multi-target inhibitory characteristics against ASFVPolX and ASFVLig, while compound cochlactone A showed promising antagonistic results against all three BER proteins. It is recommended to prioritize these hit compounds in future in vitro and in vivo studies to validate their potential as antiviral drugs against ASFV.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

The Cytotoxic Properties of Extreme Fungi's Bioactive Components-An Updated Metabolic and Omics Overview

Fungi are the most diverse living organisms on planet Earth, where their ubiquitous presence in various ecosystems offers vast potential for the research and discovery of new, naturally occurring medicinal products. Concerning human health, cancer remains one of the leading causes of mortality. While extensive research is being conducted on treatments and their efficacy in various stages of cancer, finding cytotoxic drugs that target tumor cells with no/less toxicity toward normal tissue is a significant challenge. In addition, traditional cancer treatments continue to suffer from chemical resistance. Fortunately, the cytotoxic properties of several natural products derived from various microorganisms, including fungi, are now well-established. The current review aims to extract and consolidate the findings of various scientific studies that identified fungi-derived bioactive metabolites with antitumor (anticancer) properties. The antitumor secondary metabolites identified from extremophilic and extremotolerant fungi are grouped according to their biological activity and type. It became evident that the significance of these compounds, with their medicinal properties and their potential application in cancer treatment, is tremendous. Furthermore, the utilization of omics tools, analysis, and genome mining technology to identify the novel metabolites for targeted treatments is discussed. Through this review, we tried to accentuate the invaluable importance of fungi grown in extreme environments and the necessity of innovative research in discovering naturally occurring bioactive compounds for the development of novel cancer treatments.

IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants

Compilation, curation, digitization, and exploration of the phytochemical space of Indian medicinal plants can expedite ongoing efforts toward natural product and traditional knowledge based drug discovery. To this end, we present IMPPAT 2.0, an enhanced and expanded database compiling manually curated information on 4010 Indian medicinal plants, 17,967 phytochemicals, and 1095 therapeutic uses. Notably, IMPPAT 2.0 compiles associations at the level of plant parts and provides a FAIR-compliant nonredundant in silico stereo-aware library of 17,967 phytochemicals from Indian medicinal plants. The phytochemical library has been annotated with several useful properties to enable easier exploration of the chemical space. We have also filtered a subset of 1335 drug-like phytochemicals of which majority have no similarity to existing approved drugs. Using cheminformatics, we have characterized the molecular complexity and molecular scaffold based structural diversity of the phytochemical space of Indian medicinal plants and performed a comparative analysis with other chemical libraries. Altogether, IMPPAT 2.0 is a manually curated extensive phytochemical atlas of Indian medicinal plants that is accessible at https://cb.imsc.res.in/imppat/.

Scaffold and Structural Diversity of the Secondary Metabolite Space of Medicinal Fungi

Medicinal fungi, including mushrooms, have well-documented therapeutic uses. In this study, we perform a cheminformatics-based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi and, moreover, perform a detailed comparison with approved drugs, other natural product libraries, and semi-synthetic libraries. We find that the secondary metabolite space of medicinal fungi has similar or higher scaffold diversity in comparison to other natural product libraries analyzed here. Notably, 94% of the scaffolds in the secondary metabolite space of medicinal fungi are not present in the approved drugs. Further, we find that the secondary metabolites, on the one hand, are structurally far from the approved drugs, while, on the other hand, they are close in terms of molecular properties to the approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product-based drug discovery.

Progress and Impact of Latin American Natural Product Databases

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.

Integrated Machine Learning and Chemoinformatics-Based Screening of Mycotic Compounds against Kinesin Spindle ProteinEg5 for Lung Cancer Therapy

Among the various types of cancer, lung cancer is the second most-diagnosed cancer worldwide. The kinesin spindle protein, Eg5, is a vital protein behind bipolar mitotic spindle establishment and maintenance during mitosis. Eg5 has been reported to contribute to cancer cell migration and angiogenesis impairment and has no role in resting, non-dividing cells. Thus, it could be considered as a vital target against several cancers, such as renal cancer, lung cancer, urothelial carcinoma, prostate cancer, squamous cell carcinoma, etc. In recent years, fungal secondary metabolites from the Indian Himalayan Region (IHR) have been identified as an important lead source in the drug development pipeline. Therefore, the present study aims to identify potential mycotic secondary metabolites against the Eg5 protein by applying integrated machine learning, chemoinformatics based in silico-screening methods and molecular dynamic simulation targeting lung cancer. Initially, a library of 1830 mycotic secondary metabolites was screened by a predictive machine-learning model developed based on the random forest algorithm with high sensitivity (1) and an ROC area of 0.99. Further, 319 out of 1830 compounds screened with active potential by the model were evaluated for their drug-likeness properties by applying four filters simultaneously, viz., Lipinski's rule, CMC-50 like rule, Veber rule, and Ghose filter. A total of 13 compounds passed from all the above filters were considered for molecular docking, functional group analysis, and cell line cytotoxicity prediction. Finally, four hit mycotic secondary metabolites found in fungi from the IHR were screened viz., (-)-Cochlactone-A, Phelligridin C, Sterenin E, and Cyathusal A. All compounds have efficient binding potential with Eg5, containing functional groups like aromatic rings, rings, carboxylic acid esters, and carbonyl and with cell line cytotoxicity against lung cancer cell lines, namely, MCF-7, NCI-H226, NCI-H522, A549, and NCI H187. Further, the molecular dynamics simulation study confirms the docked complex rigidity and stability by exploring root mean square deviations, root mean square fluctuations, and radius of gyration analysis from 100 ns simulation trajectories. The screened compounds could be used further to develop effective drugs against lung and other types of cancer.

Potential phytochemical inhibitors of SARS-CoV-2 helicase Nsp13: a molecular docking and dynamic simulation study

The SARS-CoV-2 helicase Nsp13 is a promising target for developing anti-COVID drugs. In the present study, we have identified potential natural product inhibitors of SARS-CoV-2 Nsp13 targeting the ATP-binding site using molecular docking and molecular dynamics (MD) simulations. MD simulation of the prepared crystal structure of SARS-CoV-2 Nsp13 was performed to generate an ensemble of structures of helicase Nsp13 capturing the conformational diversity of the ATP-binding site. A natural product library of more than 14,000 phytochemicals from Indian medicinal plants was used to perform virtual screening against the ensemble of Nsp13 structures. Subsequently, a two-stage filter, first based on protein-ligand docking binding energy value and second based on protein residues in the ligand-binding site and non-covalent interactions between the protein residues and the ligand in the best-docked pose, was used to identify 368 phytochemicals as potential inhibitors of SARS-CoV-2 helicase Nsp13. MD simulations of the top inhibitors complexed with protein were performed to confirm stable binding, and to compute MM-PBSA based binding energy. From among the 368 potential phytochemical inhibitors, the top identified potential inhibitors of SARS-CoV-2 helicase Nsp13 namely, Picrasidine M, (+)-Epiexcelsin, Isorhoeadine, Euphorbetin and Picrasidine N, can be taken up initially for experimental studies.

NeurotoxKb 1.0: Compilation, curation and exploration of a knowledgebase of environmental neurotoxicants specific to mammals

Exposure to environmental neurotoxicants is a significant concern due to their potential to cause permanent or irreversible damage to the human nervous system. Here, we present the first dedicated knowledgebase, NeurotoxKb 1.0, on environmental neurotoxicants specific to mammals. Using a detailed workflow, we have compiled 475 potential non-biogenic neurotoxicants from 835 published studies with evidence of neurotoxicity specific to mammals. A unique feature of NeurotoxKb 1.0 is the manual curation effort to compile and standardize the observed neurotoxic effects for the potential neurotoxicants from 835 published studies. For the 475 potential neurotoxicants, we have compiled diverse information such as chemical structures, environmental sources, chemical classification, physicochemical properties, molecular descriptors, predicted ADMET properties, and target human genes. To better understand the prospect of human exposure, we have explored the presence of potential neurotoxicants in external exposomes via two different analyses. By analyzing 55 chemical lists representing global regulations and guidelines, we reveal potential neurotoxicants both in regular use and produced in high volume. By analyzing human biospecimens, we reveal potential neurotoxicants detected in them. Lastly, a construction of the chemical similarity network and ensuing analysis revealed the diversity of the toxicological space of 475 potential neurotoxicants. NeurotoxKb 1.0 is accessible online at: https://cb.imsc.res.in/neurotoxkb/.

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

The disease outbreak of Coronavirus disease-19 (COVID-19), caused by the novel SARS-CoV-2 virus, remains a public health concern. COVID-19 is spreading rapidly with a high mortality rate due to unavailability of effective treatment or vaccine for the disease. The high rate of mutation and recombination in SARS-CoV2 makes it difficult for scientist to develop specific anti-CoV2 drugs and vaccines. SARS-CoV-2-Mpro cleaves the viral polyprotein to produce a variety of non-structural proteins, but in human host it also cleaves the nuclear transcription factor kappa B (NF-κB) essential modulator (NEMO), which suppresses the activation of the NF-κB pathway and weakens the immune response. Since the main protease (Mpro) is required for viral gene expression and replication, it is a promising target for antagonists to treat novel coronavirus disease and discovery of high resolution crystal structure of SARS-CoV-2-Mpro provide an opportunity for in silico identification of its possible inhibitors. In this study we intend to find novel and potential Mpro inhibitors from around 1830 chemically diverse and therapeutically important secondary metabolites available in the MeFSAT database by performing molecular docking against the Mpro structure of SARS-CoV-2 (PDB ID: 6LZE). After ADMET (absorption, distribution, metabolism, excretion, and toxicity) profile and binding energy calculation through MM-GBSA for top five hits, Sterenin M was proposed as a SARS-CoV2-Mpro inhibitor with validation of molecular dynamics (MD) simulation study. Sterenin M seems to have the potential to be a promising ligand against SARS-CoV-2, and thus it requires further validation by in vitro and in vivo studies.