Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors.

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

High-throughput virtual screening of Streptomyces spp. metabolites as antiviral inhibitors against the Nipah virus matrix protein.

Nipah virus (NiV) remains a significant global concern due to its impact on both the agricultural industry and human health, resulting in substantial economic and health consequences. Currently, there is no cure or commercially available vaccine for the virus. Therefore, it is crucial to prioritize the discovery of new and effective treatment options to prevent its continued spread. Streptomyces spp. are rich sources of metabolites known for their bioactivity against certain diseases; however, their potential as antiviral drugs against the Nipah virus remain unexplored. In this study, 6524 Streptomyces spp. metabolites were screened through in silico methods for their inhibitory effects against the Nipah virus matrix (NiV-M) protein, which assists in virion assembly of Nipah virus. Different computer-aided tools were utilized to carry out the virtual screening process: ADMET profiling revealed 913 compounds with excellent safety and efficacy profiles, molecular docking predicted the binding poses and associated docking scores of the ligands in their respective targets, MD simulations confirmed the binding stability of the top ten highest-scoring ligands in a 100 ns all-atom simulation, PCA elucidated simulation convergence, and MMPB(GB)SA calculations estimated the binding energies of the final candidate compounds and determined the key residues crucial for complex formation. Using in silico methods, we identified six metabolites targeting the main substrate-binding site and five targeting the dimerization site that exhibited excellent stability and strong binding affinity. We recommend testing these compounds in the next stages of drug development to confirm their effectiveness as therapeutic agents against Nipah virus.

Multitarget Compounds for Neglected Diseases: A Review.

Neglected diseases are a group of infectious diseases, many of them parasitic, that mainly affect the poorest populations with limited access to health services, especially those living in remote rural areas and slums. According to the World Health Organization (WHO), neglected diseases put the lives of more than 200 million people at risk, and treatment is made difficult by the occurrence of resistance to existing medications, as well as the high level of toxicity. In this way, the potential of multitarget compounds is highlighted, defined as compounds designed to modulate multiple targets of relevance to disease, with the overall goal of enhancing efficacy and/or improving safety. Thus, the objective of our study is to evaluate existing multi-target compound approaches for neglected diseases, with an emphasis on Leishmaniasis, Chagas Disease, and Arboviruses. A literature review was performed by searching the database "Web of Sciences". In relation to the diseases covered in this work, Leishmaniasis, individually, was the one that presented the largest number of articles (11) that dealt with the topic, which can be justi-fied by the high prevalence of this disease in the world, the second most common disease was Dengue, followed by Chagas disease, Chikungunya virus, and Zika virus. Furthermore, the multi-target potential of phenolic compounds was observed in all diseases under study, with the mecha-nisms related to the nucleus and transcription being the most reported mechanisms. From this per-spective, it is worth highlighting the effectiveness of approaches related to multitarget drugs in discovering new therapeutic agents for neglected diseases.

Cathepsin S (CTSS) in IgA nephropathy: an exploratory study on its role as a potential diagnostic biomarker and therapeutic target.

Introduction: IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear. Methods: We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning. Results: Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS. Conclusion: Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies.

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

Design, synthesis and biological evaluation of sulfamethazine derivatives as potent neuraminidase inhibitors.

Aim: The purpose of this study is to design and synthesize a new series of sulfamethazine derivatives as potent neuraminidase inhibitors. Materials & methods: A sulfamethazine lead compound, ZINC670537, was first identified by structure-based virtual screening technique, then some novel inhibitors X1-X10 based on ZINC670537 were designed and synthesized. Results: Compound X3 exerts the most good potency in inhibiting the wild-type H5N1 NA (IC50 = 6.74 µM) and the H274Y mutant NA (IC50 = 21.09 µM). 150-cavity occupation is very important in determining activities of these inhibitors. The sulfamethazine moiety also plays an important role. Conclusion: Compound X3 maybe regard as a good anti-influenza candidate to preform further study.

[Box: see text].

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a cocrystal prediction model based on Graph Neural Networks (CocrystalGNN) for the screening of cocrystals with NIF. And scoring 50 coformers using CocrystalGNN. To validate the reliability of the model, we used another prediction method, Molecular Electrostatic Potential Surface (MEPS), to verify the prediction results. Subsequently, we performed a second validation using experiments. The results indicate that our model achieved high performance. Ultimately, cocrystals of NIF were successfully obtained and all cocrystals exhibited better solubility and dissolution characteristics compared to the parent drug. This study lays a solid foundation for combining virtual prediction with experimental screening to discover novel water-insoluble drug cocrystals.

Evolving ω-amine transaminase AtATA guided by substrate-enzyme binding free energy for enhancing activity and stability against non-natural substrates.

In the field of chiral amine synthesis, ω-amine transaminase (ω-ATA) is one of the most established enzymes capable of asymmetric amination under optimal conditions. However, the applicability of ω-ATA toward more non-natural complex molecules remains limited due to its low transamination activity, thermostability, and narrow substrate scope. Here, by employing a combined approach of computational virtual screening strategy and combinatorial active-site saturation test/iterative saturation mutagenesis strategy, we have constructed the best variant M14C3-V5 (M14C3-V62A-V116S-E117I-L118I-V147F) with improved ω-ATA from Aspergillus terreus (AtATA) activity and thermostability toward non-natural substrate 1-acetylnaphthalene, which is the ketone precursor for producing the intermediate (R)-(+)-1-(1-naphthyl)ethylamine [(R)-NEA] of cinacalcet hydrochloride, showing activity enhancement of up to 3.4-fold compared to parent enzyme M14C3 (AtATA-F115L-M150C-H210N-M280C-V149A-L182F-L187F). The computational tools YASARA, Discovery Studio, Amber, and FoldX were applied for predicting mutation hotspots based on substrate-enzyme binding free energies and to show the possible mechanism with features related to AtATA structure, catalytic activity, and stability in silico analyses. M14C3-V5 achieved 71.8% conversion toward 50 mM 1-acetylnaphthalene in a 50 mL preparative-scale reaction for preparing (R)-NEA. Moreover, M14C3-V5 expanded the substrate scope toward aromatic ketone compounds. The generated virtual screening strategy based on the changes in binding free energies has successfully predicted the AtATA activity toward 1-acetylnaphthalene and related substrates. Together with experimental data, these approaches can serve as a gateway to explore desirable performances, expand enzyme-substrate scope, and accelerate biocatalysis.IMPORTANCEChiral amine is a crucial compound with many valuable applications. Their asymmetric synthesis employing ω-amine transaminases (ω-ATAs) is considered an attractive method. However, most ω-ATAs exhibit low activity and stability toward various non-natural substrates, which limits their industrial application. In this work, protein engineering strategy and computer-aided design are performed to evolve the activity and stability of ω-ATA from Aspergillus terreus toward non-natural substrates. After five rounds of mutations, the best variant, M14C3-V5, is obtained, showing better catalytic efficiency toward 1-acetylnaphthalene and higher thermostability than the original enzyme, M14C3. The robust combinational variant acquired displayed significant application value for pushing the asymmetric synthesis of aromatic chiral amines to a higher level.

Virtual screening of natural products to enhance melanogenosis.

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt-inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt-inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed.

In silico and in vitro studies for the identification of small molecular inhibitors from Euphorbia hirta Linn for rheumatoid arthritis: targeting TNF-α-mediated inflammation.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that is now potentially lethal and has a significant detrimental influence on people's daily lives by affecting bone joints. Inflammation plays a vital role in this type of autoimmune disorder. In rheumatoid arthritis, long-term production of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1 (IL-1) stimulates the immune system against cells in bone joints and helps to develop the pathogenesis of rheumatoid arthritis. So, while treating rheumatoid arthritis, we need to block these kinds of mechanisms. We employed soxhlet extraction, thin-layer chromatography (TLC), and gas chromatography-mass spectroscopy (GC-MS) to analyze the phytocompound information in E. hirta leaves. Furthermore, our research included in vitro investigations using Western blotting and mRNA expression analysis (TNF-α, IL-1ß, IL-6) to affirm the anti-inflammatory effectiveness of our extract. For identifying the lead-like molecules, virtual screening and molecular dynamics simulations were used. TLC results confirmed the presence of phytocompounds in E. hirta crude through spots. The structure elucidation of the phytocompounds was confirmed by the GC-MS chromatogram. The in vitro outcomes collectively underscore the inhibitory influence of E. hirta on cell proliferation and its capacity to attenuate the expression of TNF- α within THP-1 cells. The results of in silico methodologies confirmed six lead-like molecules. We could conclude that phytocompounds from ethanol leaf crude have effective lead-like molecules against the TNF-α.

Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design.

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments.

Computational Screening of Repurposed Drugs for HMG-CoA Synthase 2 in Alzheimer's Disease.

Background: HMGCS2 (mitochondrial 3-hydroxy-3-methylglutaryl-COA synthase 2) plays a pivotal role as a control enzyme in ketogenesis, and its association with the amyloid-ß protein precursor (AßPP) in mitochondria implicates a potential involvement in Alzheimer's disease (AD) pathophysiology. Objective: Our study aimed at identifying repurposed drugs using the DrugBank database capable of inhibiting HMGCS2 activity. Methods: Exploiting the power of drug repurposing in conjunction with virtual screening and molecular dynamic (MD) simulations against 'HMGCS2', we present new in-silico insight into structure-based drug repurposing. Results: The initial molecules were screened for their binding affinity to HMGCS2. Subsequent interaction analyses and extensive 300 ns MD simulations were conducted to explore the conformational dynamics and stability of HMGCS2 in complex with the screened molecules, particularly Penfluridol and Lurasidone. Conclusions: The study revealed that HMGCS2 forms stable protein-ligand complexes with Penfluridol and Lurasidone. Our findings indicate that Penfluridol and Lurasidone competitively bind to HMGCS2 and warrant their further exploration as potential repurposed molecules for anti-Alzheimer's drug development.

Structure-Based Virtual Screening, ADMET Properties Prediction and Molecular Dynamics Studies Reveal Potential Inhibitors of Mycoplasma pneumoniae HPrK/P.

Mycoplasma pneumoniae pneumonia (MPP) is a frequent cause of community-acquired pneumonia (CAP) in children. The incidence of childhood pneumonia caused by M. pneumoniae infection has been rapidly increasing worldwide. M. pneumoniae is naturally resistant to beta-lactam antibiotics due to its lack of a cell wall. Macrolides and related antibiotics are considered the optimal drugs for treating M. pneumoniae infection. However, clinical resistance to macrolides has become a global concern in recent years. Therefore, it is imperative to urgently identify new targets and develop new anti-M. pneumoniae drugs to treat MMP. Previous studies have shown that deficiencies in HPrK/P kinase or phosphorylase activity can seriously affect carbon metabolism, growth, morphology, and other cellular functions of M. pneumoniae. To identify potential drug development targets against M. pneumoniae, this study analyzed the sequence homology and 3D structure alignment of M. pneumoniae HPrK/P. Through sequence and structure analysis, we found that HPrK/P lacks homologous proteins in the human, while its functional motifs are highly conserved in bacteria. This renders it a promising candidate for drug development. Structure-based virtual screening was then used to discover potential inhibitors among 2614 FDA-approved drugs and 948 bioactive small molecules for M. pneumoniae HPrK/P. Finally, we identified three candidate drugs (Folic acid, Protokylol and Gluconolactone) as potential HPrK/P inhibitors through molecular docking, molecular dynamics (MDs) simulations, and ADMET predictions. These drugs offer new strategies for the treatment of MPP.

The Potential of Dutasteride for Treating Multidrug-Resistant Candida auris Infection.

Novel antifungal drugs are urgently needed to treat candidiasis caused by the emerging fungal multidrug-resistant pathogen Candida auris. In this study, the most cost-effective drug repurposing technology was adopted to identify an appropriate option among the 1615 clinically approved drugs with anti-C. auris activity. High-throughput virtual screening of 1,3-beta-glucanosyltransferase inhibitors was conducted, followed by an analysis of the stability of 1,3-beta-glucanosyltransferase drug complexes and 1,3-beta-glucanosyltransferase-dutasteride metabolite interactions and the confirmation of their activity in biofilm formation and planktonic growth. The analysis identified dutasteride, a drug with no prior antifungal indications, as a potential medication for anti-auris activity in seven clinical C. auris isolates from Saudi Arabian patients. Dutasteride was effective at inhibiting biofilm formation by C. auris while also causing a significant reduction in planktonic growth. Dutasteride treatment resulted in disruption of the cell membrane, the lysis of cells, and crushed surfaces on C. auris, and significant (p-value = 0.0057) shrinkage in the length of C. auris was noted at 100,000×. In conclusion, the use of repurposed dutasteride with anti-C. auris potential can enable rapid recovery in patients with difficult-to-treat candidiasis caused by C. auris and reduce the transmission of nosocomial infection.

TRAF6 Inhibitors from Marine Compound Library: Pharmacophore, Virtual Screening, Fragment Replacement, ADMET, and Molecular Dynamics.

TRAF6 is an E3 ubiquitin ligase that plays a crucial role in cell signaling. It is known that MMP is involved in tumor metastasis, and TRAF6 induces MMP-9 expression by binding to BSG. However, inhibiting TRAF6's ubiquitinase activity without disrupting the RING domain is a challenge that requires further research. To address this, we conducted computer-based drug screening to identify potential TRAF6 inhibitors. Using a ligand-receptor complex pharmacophore based on the inhibitor EGCG, known for its anti-tumor properties, we screened 52,765 marine compounds. After the molecular docking of 405 molecules with TRAF6, six compounds were selected for further analysis. By replacing fragments of non-binding compounds and conducting second docking, we identified two promising molecules, CMNPD9212-16 and CMNPD12791-8, with strong binding activity and favorable pharmacological properties. ADME and toxicity predictions confirmed their potential as TRAF6 inhibitors. Molecular dynamics simulations showed that CMNPD12791-8 maintained a stable structure with the target protein, comparable to EGCG. Therefore, CMNPD12791-8 holds promise as a potential inhibitor of TRAF6 for inhibiting tumor growth and metastasis.

Silk Gland Factor 1 Plays a Pivotal Role in Larval Settlement of the Fouling Mussel Mytilopsis sallei.

Most fouling organisms have planktonic larval and benthic adult stages. Larval settlement, the planktonic-benthic transition, is the critical point when biofouling begins. However, our understanding of the molecular mechanisms of larval settlement is limited. In our previous studies, we identified that the AMP-activated protein kinase-silk gland factor 1 (AMPK-SGF1) pathway was involved in triggering the larval settlement in the fouling mussel M. sallei. In this study, to further confirm the pivotal role of SGF1, multiple targeted binding compounds of SGF1 were obtained using high-throughput virtual screening. It was found that the targeted binding compounds, such as NAD+ and atorvastatin, could significantly induce and inhibit the larval settlement, respectively. Furthermore, the qRT-PCR showed that the expression of the foot proteins' genes was significantly increased after the exposure to 10 µM NAD+, while the gene expression was significantly suppressed after the exposure to 10 µM atorvastatin. Additionally, the production of the byssus threads of the adults was significantly increased after the exposure to 10-20 µM of NAD+, while the production of the byssus threads was significantly decreased after the exposure to 10-50 µM of atorvastatin. This work will deepen our understanding of SGF1 in triggering the larval settlement in mussels and will provide insights into the potential targets for developing novel antifouling agents.

Discovery of a dual-target DYRK2 and HDAC8 inhibitor for the treatment of hepatocellular carcinoma.

Dual-specificity tyrosine phosphorylation-regulated kinase 2 (DYRK2) and histone deacetylase 8 (HDAC8) have been shown to be associated with the development of several cancers. Here, we identified a dual-target DYRK2/HDAC8 inhibitor (DYC-1) through a combined virtual screening protocol. DYC-1 exhibited nanomolar inhibitory activity against both DYRK2 (IC50 = 5.27 ± 0.13 nM) and HDAC8 (IC50 = 8.06 ± 0.47 nM). Molecular dynamics simulations showed that DYC-1 had positive binding stability with DYRK2 and HDAC8. Importantly, the cytotoxicity assay indicated that DYC-1 exhibited superior antiproliferative activity against human liver cancer, especially SK-HEP-1 cells, and had no significant inhibition on normal liver cells. Moreover, DYC-1 showed a strong inhibitory effect on the growth of SK-HEP-1 xenograft tumors with no significant side effects. These data suggest that DYC-1 is a high-efficacy and low-toxic antitumor agent for the treatment of hepatocellular carcinoma.

Prediction of Pesticide Interactions with Proteins Involved in Human Reproduction by Using a Virtual Screening Approach: A Case Study of Famoxadone Binding CRBP-III and Izumo.

In recent years, the awareness that pesticides can have other effects apart from generic toxicity is growing. In particular, several pieces of evidence highlight their influence on human fertility. In this study, we investigated, by a virtual screening approach, the binding between pesticides and proteins present in human gametes or associated with reproduction, in order to identify new interactions that could affect human fertility. To this aim, we prepared ligand (pesticides) and receptor (proteins) 3D structure datasets from online structural databases (such as PubChem and RCSB), and performed a virtual screening analysis using Autodock Vina. In the comparison of the predicted interactions, we found that famoxadone was predicted to bind Cellular Retinol Binding Protein-III in the retinol-binding site with a better minimum energy value of -10.4 Kcal/mol and an RMSD of 3.77 with respect to retinol (-7.1 Kcal/mol). In addition to a similar network of interactions, famoxadone binding is more stabilized by additional hydrophobic patches including L20, V29, A33, F57, L117, and L118 amino acid residues and hydrogen bonds with Y19 and K40. These results support a possible competitive effect of famoxadone on retinol binding with impacts on the ability of developing the cardiac tissue, in accordance with the literature data on zebrafish embryos. Moreover, famoxadone binds, with a minimum energy value between -8.3 and -8.0 Kcal/mol, to the IZUMO Sperm-Egg Fusion Protein, interacting with a network of polar and hydrophobic amino acid residues in the cavity between the 4HB and Ig-like domains. This binding is more stabilized by a predicted hydrogen bond with the N185 residue of the protein. A hindrance in this position can probably affect the conformational change for JUNO binding, avoiding the gamete membrane fusion to form the zygote. This work opens new interesting perspectives of study on the effects of pesticides on fertility, extending the knowledge to other typologies of interaction which can affect different steps of the reproductive process.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro.

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11ß1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Accelerating Molecular Docking using Machine Learning Methods.

Virtual screening (VS) is one of the well-established approaches in drug discovery which speeds up the search for a bioactive molecule and, reduces costs and efforts associated with experiments. VS helps to narrow down the search space of chemical space and allows selecting fewer and more probable candidate compounds for experimental testing. Docking calculations are one of the commonly used and highly appreciated structure-based drug discovery methods. Databases for chemical structures of small molecules have been growing rapidly. However, at the moment virtual screening of large libraries via docking is not very common. In this work, we aim to accelerate docking studies by predicting docking scores without explicitly performing docking calculations. We experimented with an attention based long short-term memory (LSTM) neural network for an efficient prediction of docking scores as well as other machine learning models such as XGBoost. By using docking scores of a small number of ligands we trained our models and predicted docking scores of a few million molecules. Specifically, we tested our approaches on 11 datasets that were produced from in-house drug discovery studies. On average, by training models using only 7000 molecules we predicted docking scores of approximately 3.8 million molecules with R2 (coefficient of determination) of 0.77 and Spearman rank correlation coefficient of 0.85. We designed the system with ease of use in mind. All the user needs to provide is a csv file containing SMILES and their respective docking scores, the system then outputs a model that the user can use for the prediction of docking score for a new molecule.