Hidradenitis suppurativa associated telomere-methylome dysregulations in blood.

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic debilitating disease with a significant burden of both organic and psychological comorbidities. It has been shown that certain telomere-related genes (TRGs) affect a wide range of diseases, including HS and its associated comorbidities, but their exact role in HS pathogenesis is still unknown. OBJECTIVES: To determine whether TRG methylomes can be used as biomarkers in HS. METHODS: Using the Illumina HumanMethylation450 BeadChip array, we examined methylation variations associated with TRGs in HS cases and age-, sex- and ethnicity-matched healthy controls. The study utilized integrated bioinformatics statistical methods, such as a false discovery rate (FDR), the area under the receiver operating characteristic curve (AUC) and principal component analysis. RESULTS: There were a total of 585 different differentially methylated CpG sites identified in 585 TRGs associated with HS (474 hypomethylated and 111 hypermethylated) (FDR p-value < 0.05). A number of these CpGs have been identified as being involved in increased pain sensitivity including EPAS1, AHR, CSNK1D, DNMT1, IKBKAP, NOS3, PLCB1 and PRDM16 genes; GABRB3 as a potential alcohol addiction marker; DDB1, NSMCE2 and HNRNPA2B1 associated with cancers. Pathway analysis identified 67 statistically significant pathways, including DNA repair, telomere maintenance, mismatch repair and cell cycle control (p < 0.001). CONCLUSION: The disruption of TRGs leads to the shortening of telomeres, which is associated with HS progression, ageing, cellular senescence and an increased risk of various diseases, including cancer and associated comorbidities, such as metabolic syndrome, cardiovascular disease and inflammatory disorders. Further research is necessary to better understand the underlying mechanisms and establish causal links between TRGs and HS. The present study is the first effort to comprehend potential pathomechanisms of sporadic HS cases concentrating on PBMC methylome since ours.

MHC2AffyPred: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints.

Understanding how MHC class II (MHC-II) binding peptides with differing lengths exhibit specific interaction at the core and extended sites within the large MHC-II pocket is a very important aspect of immunological research for designing peptides. Certain efforts were made to generate peptide conformations amenable for MHC-II binding and calculate the binding energy of such complex formation but not directed toward developing a relationship between the peptide conformation in MHC-II structures and the binding affinity (BA) (IC50 ). We present here a machine-learning approach to calculate the BA of the peptides within the MHC-II pocket for HLA-DRA1, HLA-DRB1, HLA-DP, and HLA-DQ allotypes. Instead of generating ensembles of peptide conformations conventionally, the biased mode of conformations was created by considering the peptides in the crystal structures of pMHC-II complexes as the templates, followed by site-directed peptide docking. The structural interaction fingerprints generated from such docked pMHC-II structures along with the Moran autocorrelation descriptors were trained using a random forest regressor specific to each MHC-II peptide lengths (9-19). The entire workflow is automated using Linux shell and Perl scripts to promote the utilization of MHC2AffyPred program to any characterized MHC-II allotypes and is made for free access at https://github.com/SiddhiJani/MHC2AffyPred. The MHC2AffyPred attained better performance (correlation coefficient [CC] of .612-.898) than MHCII3D (.03-.594) and NetMHCIIpan-3.2 (.289-.692) programs in the HLA-DRA1, HLA-DRB1 types. Similarly, the MHC2AffyPred program achieved CC between .91 and .98 for HLA-DP and HLA-DQ peptides (13-mer to 17-mer). Further, a case study on MHC-II binding 15-mer peptides of severe acute respiratory syndrome coronavirus-2 showed very close competency in computing the IC50 values compared to the sequence-based NetMHCIIpan v3.2 and v4.0 programs with a correlation of .998 and .570, respectively.

Small RNA sequencing and identification of Andrographis paniculata miRNAs with potential cross­kingdom human gene targets.

Cross-species post-transcriptional regulatory potential of plant derived small non-coding microRNAs (miRNAs) has been well documented by plenteous studies. MicroRNAs are transferred to host cells via oral ingestion wherein they play a decisive role in regulation of host genes; thus, miRNAs have evolved as the nascent bioactive molecules imparting pharmacological values to traditionally used medicinal plants. The present study aims to investigate small RNA profiling in order to uncover the potential regulatory role of miRNAs derived from Andrographis paniculata, one of the most widely used herb by tribal communities for liver disorders and document the pharmacological properties of A. paniculata miRNAs. In this study, high-throughput sequencing method was used to generate raw data, ~ 60 million sequences were generated from A. paniculata leaves. Using computational tools and bioinformatics approach, analyses of 3,480,097 clean reads resulted in identification of 3440 known and 51 putative novel miRNAs regulating 1365 and 192 human genes respectively. Remarkably, the identified plausible novel miRNAs apa-miR-5, apa-miR-1, apa-miR-26, and apa-miR-30 are projected to target significant host genes including CDK6, IKBKB, TRAF3, CHD4, MECP2, and ADIPOQ. Subsequent annotations revealed probable involvement of the target genes in various pathways for instance p38-MAPK, AKT, AMPK, NF-Kß, ERK, WNT signalling, MYD88 dependant cascade, and pathways in cancer. Various diseases such as human papilloma virus infection, Alzheimer's, Non-alcoholic Fatty Liver, Alcoholic liver diseases, HepatoCellular Carcinoma (HCC), and numerous other cancers were predominantly found to be linked with target genes. Our findings postulate novel interpretations regarding modulation of human transcripts by A. paniculata miRNAs and exhibit the regulation of human diseases by plant-derived miRNAs. Though our study elucidates miRNAs as novel therapeutic agents, however, experimental validations for assessment of therapeutic potential of these miRNAs are still warranted.

MicroRNAs from Holarrhena pubescens stems: Identification by small RNA Sequencing and their Potential Contribution to Human Gene Targets.

Holarrhena pubescens is an effective medicinal plant from the Apocynaceae family, widely distributed over the Indian subcontinent and extensively used by Ayurveda and ethno-medicine systems without apparent side effects. We postulated that miRNAs, endogenous non-coding small RNAs that regulate gene expression at the post-transcriptional level, may, after ingestion into the human body, contribute to the medicinal properties of plants of this species by inducing regulated human gene expression to modulate. However, knowledge is scarce about miRNA in Holarrhena. In addition, to test the hypothesis on the potential pharmacological properties of miRNA, we performed a high-throughput sequencing analysis using the Next Generation Sequencing Illumina platform; 42,755,236 raw reads have been generated from H. pubescens stems from a library of small RNA isolated, identifying 687 known and 50 new miRNAs led. The novel H. pubescens miRNAs were predicted to regulate specific human genes, and subsequent annotations of gene functions suggested a possible role in various biological processes and signaling pathways, such as Wnt, MAPK, PI3K-Akt, and AMPK signaling pathways and endocytosis. The association of these putative targets with many diseases, including cancer, congenital malformations, nervous system disorders, and cystic fibrosis, has been demonstrated. The top hub proteins STAT3, MDM2, GSK3B, NANOG, IGF1, PRKCA, SNAP25, SRSF1, HTT, and SNCA show their interaction with human diseases, including cancer and cystic fibrosis. To our knowledge, this is the first report of uncovering H. pubescens miRNAs based on high-throughput sequencing and bioinformatics analysis. This study has provided new insight into a potential cross-species control of human gene expression. The potential for miRNA transfer should be evaluated as one possible mechanism of action to account for the beneficial properties of this valuable species.

Curcumin interferes with chitin synthesis in Aedes aegypti: a computational and experimental investigation.

Throughout history, vector-borne diseases have consistently posed significant challenges to human health. Among the strategies for vector control, chemical insecticides have seen widespread use since their inception. Nevertheless, their effectiveness is continually undermined by the steady growth of insecticide resistance within these vector populations. As such, the demand for more robust, efficient, and cost-effective natural insecticides has become increasingly pressing. One promising avenue of research focuses on chitin, a crucial structural component of mosquitoes' exoskeletons and other insects. Chitin not only provides protection and rigidity but also lends flexibility to the insect body. It undergoes substantial transformations during insect molting, a process known as ecdysis. Crucially, the production of chitin is facilitated by an enzyme known as chitin synthase, making it an attractive target for potential novel insecticides. Our recent study delved into the impacts of curcumin, a natural derivative of turmeric, on chitin synthesis and larval development in Aedes aegypti, a mosquito species known to transmit dengue and yellow fever. Our findings demonstrate that even sub-lethal amounts of curcumin can significantly reduce overall chitin content and disrupt the cuticle development in the 4th instar larvae of Aedes aegypti. Further to this, we utilized computational analyses to investigate how curcumin interacts with chitin synthase. Techniques such as molecular docking, pharmacophore feature mapping, and molecular dynamics (MD) simulations helped to illustrate that curcumin binds to the same site as polyoxin D, a recognized inhibitor of chitin synthase. These findings point to curcumin's potential as a natural, bioactive larvicide that targets chitin synthase in mosquitoes and potentially other insects.

Hidradenitis suppurativa presents a methylome dysregulation capable to explain the pro-inflammatory microenvironment: Are these DNA methylations potential therapeutic targets?

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, systemic, inflammatory skin condition with elusive pathogenesis that affects therapeutic intervention directly. OBJECTIVE: To characterize epigenetic variations in cytokines genes contributing to HS. METHODS: Epigenome-wide DNA methylation profiling with the Illumina Epic array was performed on blood DNA samples from 24 HS patients and 24 age- and sex-matched controls to explore DNA methylation changes in cytokine genes. RESULTS: We identified 170 cytokine genes including 27 hypermethylated CpG sites and 143 genes with hypomethylated sites respectively. Hypermethylated genes, including LIF, HLA-DRB1, HLA-G, MTOR, FADD, TGFB3, MALAT1 and CCL28; hypomethylated genes, including NCSTN, SMAD3, IGF1R, IL1F9, NOD2, NOD1, YY1, DLL1 and BCL2 may contribute to the pathogenesis of HS. These genes were enriched in the 117 different pathways (FDR p-values ≤ 0.05), including IL-4/IL-13 pathways and Wnt/ß-catenin signalling. CONCLUSIONS: The lack of wound healing, microbiome dysbiosis and increased tumour susceptibility are all sustained by these dysfunctional methylomes, hopefully, capable to be targeted in the next future. Since methylome describes and summarizes genetic and environmental contributions, these data may represent a further step towards a feasible precision medicine also for HS patients.

Genome-Wide Characterization of Fennel (Anethum foeniculum) MiRNome and Identification of its Potential Targets in Homo sapiens and Arabidopsis thaliana: An Inter and Intra-species Computational Scrutiny.

MicroRNAs could be promising biomarkers for various diseases, and small RNA drugs have already been FDA approved for clinical use. This area of research is rapidly expanding and has significant potential for the future. Fennel (Anethum foeniculum) is a highly esteemed spice plant with economic and medicinal benefits, making it an invaluable asset in the pharmaceutical industry. To characterize the fennel miRNAs and their Arabidopsis thaliana and Homo sapience targets with functional enrichment analysis and human disease association. A homology-based computational approach characterized the MiRnome of the Anethum foeniculum genome and assessed its impact on Arabidopsis thaliana and Homo sapience transcriptomes. In addition, functional enrichment analysis was evaluated for both species' targets. Moreover, PPI network analysis, hub gene identification, and MD simulation analysis of the top hub node with fennel miRNA were incorporated. We have identified 100 miRNAs of fennel and their target genes, which include 2536 genes in Homo sapiens and 1314 genes in Arabidopsis thaliana. Functional enrichment analysis reveals 56 Arabidopsis thaliana targets of fennel miRNAs showed involvement in metabolic pathways. Highly enriched human KEGG pathways were associated with several diseases, especially cancer. The protein-protein interaction network of human targets determined the top ten nodes; from them, seven hub nodes, namely MAPK1, PIK3R1, STAT3, EGFR, KRAS, CDC42, and SMAD4, have shown their involvement in the pancreatic cancer pathway. Based on the Blast algorithm, 21 fennel miRNAs are homologs to 16 human miRNAs were predicted; from them, the CSPP1 target was a common target for afo-miR11117a-3p and has-miR-6880-5p homologs miRNAs. Our results are the first to report the 100 fennel miRNAs, and predictions for their endogenous and human target genes provide a basis for further understanding of Anethum foeniculum miRNAs and the biological processes and diseases with which they are associated.

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates. PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies high-affinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%. The PharmRF scoring model, scripts, and related resources can be accessed at https://github.com/Prasanth-Kumar87/PharmRF.

Assessing the Protective Effect of Moringa oleifera Extract against Bone Metastasis: An In Vitro Simulated Digestion Approach.

Moringa oleifera possesses numerous advantageous effects like anti-microbial, antioxidant, and anti-inflammatory, leaves contain a high multiplicity of the bioactive compound; however, little is identified about its bioaccessibility. The objective of this study was to assess the bioefficacy, bioaccessible and anticancer activity of Moringa oleifera in a PC3 cell line before and after simulated in vitro digestion. Digested and non-digested extracts were prepared and evaluated for total polyphenols, flavonoids, and total antioxidant capacity by spectrophotometric analysis and LCMS analysis. Cell viability, apoptosis, colony formation, cell cycle, Glutathione level, and gene expression study were tested with Moringa oleifera (MO) and digested Moringa oleifera (DMO). Results revealed that total polyphenols, total flavonoids, and TAC were significantly (P < 0.05) reduced after in vitro digestion. Furthermore, biological activity against the PC3 cell line showed that DMO extracts significant cytotoxic and reduced cell vitality compared to the MO. In addition, DMO extract had a noteworthy effect in apoptosis and inhibiting the colony formation ability; while cell cycle was blocked in S phase by both extracts but significant effect showed in DMO. These studies have increased understanding of the influence of in vitro simulation digestion on the biological activity effect of M. oleifera against prostate cancer bone metastasis.Supplemental data for this article is available online at https://doi.org/10.1080/01635581.2021.1933099 .

Repurposing the antibacterial drugs for inhibition of SARS-CoV2-PLpro using molecular docking, MD simulation and binding energy calculation.

Papain-like protease (nsp-3; non-structural protein) of novel corona virus is an ideal target for developing drugs as it plays multiple important functions for viral growth and replication. For instance, role of nsp-3 has been recognized in cleavage of viral polyprotein; furthermore, in infected host it weakens the immune system via downregulating the production of type I interferon. This downregulation is promoted by removal of ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from interferon-responsive factor 3 (IRF3) protein. Among known inhibitors of SARS-CoV-PLpro GRL0617 is by far the most effective inhibitor. As PLpro of SARS-CoV2 is having more than 80% similarity with SARS-CoV-PLpro, GRL0617 is reported to be effective even against SARS-CoV2. Owing to this similarity, certain key amino acids remain the same/conserved in both proteins. Among conserved amino acids Tyr268 for SARS-CoV2 and Tyr269 for SARS-CoV produce important hydrophobic interactions with aromatic rings of GRL0617. Here, in this study antibacterial compounds were collected from ZINC database, and they were filtered to select compounds that are having similar structural features as GRL0617. This filtered library of compound was then docked with SARS-CoV and CoV2-PLpro. Five hits were noted that were able to interact with Tyr268 (SARS-CoV2) and Tyr269 (SARS-CoV). Further, best hit 2-(2-((benzofuran-2-carboxamido)methyl)-5-methoxy-1H-indol-1-yl)acetic acid (ZINC44459905) was studied using molecular dynamic simulation where stability of protein-ligand complex as well as stability of produced interactions was noted.

Identifying structural-functional analogue of GRL0617, the only well-established inhibitor for papain-like protease (PLpro) of SARS-CoV2 from the pool of fungal metabolites using docking and molecular dynamics simulation.

The non-structural protein (nsp)-3 of SARS-CoV2 coronavirus is sought to be an essential target protein which is also named as papain-like protease (PLpro). This protease cleaves the viral polyprotein, but importantly in human host it also removes ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from interferon responsive factor 3 (IRF3) protein which ultimately downregulates the production of type I interferon leading to weakening of immune response. GRL0617 is the most potent known inhibitor for PLpro that was initially developed for SARS outbreak of 2003. The PLpro of SARS-CoV and CoV2 share 83% sequence identity but interestingly have several identical conserved amino acids that suggests GRL0617 to be an effective inhibitor for PLpro of SARS-CoV2. GRL0617 is a naphthalene-based molecule and interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). To identify PLpro inhibitors, we prepared a library of secondary metabolites from fungi with aromatic nature and docked them with PLpro of SARS-CoV and SARS-CoV2. We found six hits which interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). More surprisingly the top hit, Fonsecin, has naphthalene moiety in its structure, which recruits Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro) and has binding energy at par with control (GRL0617). Molecular dynamics (MD) simulation showed Fonsecin to interact with Tyr268 of SARS-CoV2-PLpro more efficiently than control (GRL0617) and interacting with a greater number of amino acids in the binding cleft of PLpro.

Reckoning apigenin and kaempferol as a potential multi-targeted inhibitor of EGFR/HER2-MEK pathway of metastatic colorectal cancer identified using rigorous computational workflow.

In the past two decades, the treatment of metastatic colorectal cancer (mCRC) has been revolutionized as multiple cytotoxic, biological, and targeted drugs are being approved. Unfortunately, tumors treated with single targeted agents or therapeutics usually develop resistance. According to pathway-oriented screens, mCRC cells evade EGFR inhibition by HER2 amplification and/or activating Kras-MEK downstream signaling. Therefore, treating mCRC patients with dual EGFR/HER2 inhibitors, MEK inhibitors, or the combination of the two drugs envisaged to prevent the resistance development which eventually improves the overall survival rate. In the present study, we aimed to screen potential phytochemical lead compounds that could multi-target EGFR, HER2, and MEK1 (Mitogen-activated protein kinase kinase) using a computer-aided drug design approach that includes molecular docking, endpoint binding free energy calculation using MM-GBSA, ADMET, and molecular dynamics (MD) simulations. Docking studies revealed that, unlike all other ligands, apigenin and kaempferol exhibit the highest docking score against all three targets. Details of ADMET analysis, MM/GBSA, and MD simulations helped us to conclusively determine apigenin and kaempferol as potentially an inhibitor of EGFR, HER2, and MEK1 apigenin and kaempferol against mCRC at a systemic level. Additionally, both apigenin and kaempferol elicited antiangiogenic properties in a dose-dependent manner. Collectively, these findings provide the rationale for drug development aimed at preventing CRC rather than intercepting resistance.

Meticulous assessment of natural compounds from NPASS database for identifying analogue of GRL0617, the only known inhibitor for SARS-CoV2 papain-like protease (PLpro) using rigorous computational workflow.

The latest global outbreak of 2019 respiratory coronavirus disease (COVID-19) is triggered by the inception of novel coronavirus SARS-CoV2. If recent events are of any indicators of the epidemics of past, it is undeniable to state a fact that the SARS-CoV2 viral infection is highly transmissible with respect to its previously related SARS-CoV's. Papain-like protease (PLpro) is an enzyme that is required by the virus itself for replicating into the host system; and it does so by processing its polyproteins into a functional replicase complex. PLpro is also known for downregulating the genes responsible for producing interferons, an essential family of molecules produced in response to viral infection, thus making this protein an indispensable drug target. In this study, PLpro inhibitors were identified through high throughput structure-based virtual screening approach from NPASS natural product library possessing ~ 35,000 compounds. Top five hits were scrutinised based on structural aromaticity and ability to interact with a key active site residue of PLpro, Tyr268. For second level of screening, the MM-GBSA End-Point Binding Free Energy Calculation of the docked complexes was performed, which identified Caesalpiniaphenol A as the best hit. Caesalpiniaphenol A not only possess a double ring aromatic moiety but also has lowest minimum binding energy, which is at par with the control GRL0617, the only known inhibitor of SARS-CoV2 PLpro. Details of the Molecular Dynamics (MD) simulation and ADMET analysis helped to conclusively determine Caesalpiniaphenol A as potentially an inhibitor of SARS-CoV2 PLpro.

A Comprehensive in vitro and in silico Assessment on Inhibition of CYP51B and Ergosterol Biosynthesis by Eugenol in Rhizopus oryzae.

Mucormycosis, also known as Zygomycosis, is a disease caused by invasive fungi, predominantly Rhizopus species belonging to the Order of Mucorales. Seeing from the chemistry perspective, heterocyclic compounds with an "azole" moiety are widely employed as antifungal agent for minimising the effect of mucormycosis as a prescribed treatment. These azoles serve as non-competitive inhibitors of fungal CYP51B by predominantly binding to its heme moiety, rendering its inhibition. However, long-term usage and abuse of azoles as antifungal medicines has resulted in drug resistance among certain fungal pathogens. Hence, there is an unmet need to find alternative therapeutic compounds. In present study, we used various in vitro tests to investigate the antifungal activity of eugenol against R. oryzae/R. arrhizus, including ergosterol quantification to test inhibition of ergosterol production mediated antifungal action. The minimum inhibitory concentration (MIC) value obtained for eugenol was 512 µg/ml with reduced ergosterol concentration of 77.11 ± 3.25% at MIC/2 concentration. Further, the molecular interactions of eugenol with fungal CYP51B were meticulously studied making use of proteomics in silico study including molecular docking and molecular dynamics simulations that showed eugenol to be strongly interacting with heme in an identical fashion to that shown by azole drugs (in this case, clotrimazole was evaluated). This is the first of a kind study showing the simulation study of eugenol with CYP51B of fungi. This inhibition results in ergosterol synthesis and is also studied and compared with keeping clotrimazole as a reference.

Unravelling the antifungal mode of action of curcumin by potential inhibition of CYP51B: A computational study validated in vitro on mucormycosis agent, Rhizopus oryzae.

Like human, fungi too are known to share lot of structural similarities amongst their CYPs (Cytochrome P450 super family of enzymes) which allows antifungal 'azole' compounds to interact with CYPs of human. Clotrimazole, an 'azole' antifungal drug, is a known inhibitor of fungal CYP named CYP51B. Curcumin, a phytochemical obtained from Curcuma longa has the ability to interact with several different human CYPs to induce inhibition. The sequence and the structural similarities amongst both human and fungal CYPs suggest a strong possibility for curcumin to interact with fungal CYP51B to behave like an antifungal agent. To test this hypothesis a study was designed involving mucormycosis agent, Rhizopus oryzae. The ability of curcumin to interact with fungal CYP51B was analysed computationally through molecular docking, MM-GBSA and Molecular Dynamics (MD) simulation assessment. Further, interaction profile for fungal CYP51B-curcumin was compared with human CYP3A4-curcumin, as there are published evidence describing curcumin as an inhibitor of human CYPs. Additionally, to validate in silico findings, an in vitro assay was performed to examine the antifungal potentials of curcumin on the R. oryzae. Conclusive results allow us to determine a plausible mode of action of curcumin to act as an antifungal against a mucormycosis agent.

Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation.

The pandemic outbreak of the Corona viral infection has become a critical global health issue. Biophysical and structural evidence shows that spike protein possesses a high binding affinity towards host angiotensin-converting enzyme 2 and viral hemagglutinin-acetylesterase (HE) glycoprotein receptor. We selected HE as a target in this study to identify potential inhibitors using a combination of various computational approaches such as molecular docking, ADMET analysis, dynamics simulations and binding free energy calculations. Virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin as potential HE inhibitors with better binding energy. Furthermore, molecular dynamics simulations for 100 ns time scale revealed that most of the key HE contacts were retained throughout the simulations trajectories. Binding free energy calculations using MM/PBSA approach ranked the top-five potential NPACT compounds which can act as effective HE inhibitors.

Energetic contributions of amino acid residues and its cross-talk to delineate ligand-binding mechanism.

Receptor-based QSAR approaches can enumerate the energetic contributions of amino acid residues toward ligand binding only when experimental binding affinity is associated. The structural data of protein-ligand complexes are witnessing a tremendous growth in the Protein Data Bank deposited with a few entries on binding affinity. We present here a new approach to compute the Energetic CONTributions of Amino acid residues and its possible Cross-Talk (ECONTACT) to study ligand binding using per-residue energy decomposition, molecular dynamics simulations and rescoring method without the need for experimental binding affinity. This approach recognizes potential cross-talks among amino acid residues imparting a nonadditive effect to the binding affinity with evidence of correlative motions in the dynamics simulations. The protein-ligand interaction energies deduced from multiple structures are decomposed into per-residue energy terms, which are employed as variables to principal component analysis and generated cross-terms. Out of 16 cross-talks derived from eight datasets of protein-ligand systems, the ECONTACT approach is able to associate 10 potential cross-talks with site-directed mutagenesis, free energy, and dynamics simulations data strongly. We modeled these key determinants of ligand binding using joint probability density function (jPDF) to identify cross-talks in protein structures. The top two cross-talks identified by ECONTACT approach corroborated with the experimental findings. Furthermore, virtual screening exercise using ECONTACT models better discriminated known inhibitors from decoy molecules. This approach proposes the jPDF metric to estimate the probability of observing cross-talks in any protein-ligand complex. The source code and related resources to perform ECONTACT modeling is available freely at https://www.gujaratuniversity.ac.in/econtact/.

A multiparametric organ toxicity predictor for drug discovery.

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs.A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints.

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

A novel algorithm to differentiate between primary lung tumors and distant liver metastasis in lung cancers using an exosome based multi gene biomarker panel.

The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patients with LCLM. An 8-gene panel identified in our previous study was validated in CTC, cfRNA and exosomes isolated from primary lung cancer with & without metastasis. Further multivariate analysis including PCA & ROC was performed to determine the sensitivity and specificity of the biomarker panel. Model validation cohort (n = 79) was used to verify the stability of the constructed predictive model. Further, clinic-pathological factors, survival analysis and immune infiltration correlations were also performed. In comparison to our previous tissue data, exosomes demonstrated a good discriminative value with an AUC of 0.7247, specificity (72.48%) and sensitivity (96.87%) for the 8-gene panel. Further individual gene patterns led us to a 5- gene panel that showed an AUC of 0.9488 (p = < 0.001) and 0.9924 (p = < 0.001) respectively for tissue and exosomes. Additionally, on validating the model in a larger cohort a risk score was obtained (RS > 0.2) for prediction of liver metastasis with an accuracy of 95%. Survival analysis and immune filtration markers suggested that four exosomal markers were independently associated with poor overall survival. We report a novel blood-based exosomal biomarker panel for early diagnosis, monitoring of therapeutic response, and prognostic evaluation of patients with LCLM.