Potential protein kinase inhibitors that target G-quadruplex DNA structures in the human telomeric regions.

Telomeric regions contain Guanine-rich sequences arranged in a planar manner and connected by Hoogsteen hydrogen bonds that can fold into G-quadruplex (G4) DNA structures, and can be stabilized by monovalent metal cations. The presence of G4 DNA holds significance in cancer-related processes, especially due to their regulatory potential at transcriptional and translational levels of oncogene and tumor suppressor genes. The objective of this current research is to explore the evolving realm of FDA-approved protein kinase inhibitors, with a specific emphasis on their capacity to stabilize the G4 DNA structures formed at the human telomeric regions. This involves investigating the possibility of repurposing FDA-approved protein kinase inhibitors as a novel approach for targeting multiple cancer types. In this context, we have selected 16 telomeric G4 DNA structures as targets and 71 FDA-approved small-molecule protein kinase inhibitors as ligands. To investigate their binding affinities, molecular docking of human telomeric G4 DNA with nuclear protein kinase inhibitors and their corresponding co-crystalized ligands were performed. We found that Ponatinib and Lapatinib interact with all the selected G4 targets, the binding free energy calculations, and molecular dynamic simulations confirm their binding efficacy and stability. Thus, it is hypothesized that Ponatinib and Lapatinib may stabilize human telomeric G4 DNA in addition to their ability to inhibit BCR-ABL and the other members of the EGFR family. As a result, we also hypothesize that the stabilization of G4 DNA might represent an additional underlying mechanism contributing to their efficacy in exerting anti-cancer effects.

Targeting AFP-RARß complex formation: a potential strategy for treating AFP-positive hepatocellular carcinoma.

Alpha-fetoprotein (AFP) is a glycoprotein primarily expressed during embryogenesis, with declining levels postnatally. Elevated AFP levels correlate with pathological conditions such as liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Recent investigations underscore AFP's intracellular role in HCC progression, wherein it forms complexes with proteins like Phosphatase and tensin homolog (PTEN), Caspase 3 (CASP3), and Retinoic acid receptors and Retinoid X receptors (RAR/RXR). RAR and RXR regulate gene expression linked to cell death and tumorigenesis in normal physiology. AFP impedes RAR/RXR dimerization, nuclear translocation, and function, promoting gene expression favoring cancer progression in HCC that provoked us to target AFP as a drug candidate. Despite extensive studies, inhibitors targeting AFP to disrupt complex formation and activities remain scarce. In this study, employing protein-protein docking, amino acid residues involved in AFP-RARß interaction were identified, guiding the definition of AFP's active site for potential inhibitor screening. Currently, kinase inhibitors play a significant role in cancer treatment and, the present study explores the potential of repurposing FDA-approved protein kinase inhibitors to target AFP. Molecular docking with kinase inhibitors revealed Lapatinib as a candidate drug of the AFP-RARß complex. Molecular dynamics simulations and binding energy calculations, employing Mechanic/Poisson-Boltzmann Surface Area (MM-PBSA), confirmed Lapatinib's stability with AFP. The study suggests Lapatinib's potential in disrupting the AFP-RARß complex, providing a promising avenue for treating molecularly stratified AFP-positive HCC or its early stages.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

HNCDrugResDb: a platform for deciphering drug resistance in head and neck cancers.

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named "HNCDrugResDb" was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

An Omics Strategy for Translational Bioinformatics and Rapid COVID-19 Drug Repurposing.

As COVID-19 continues to evolve around the world, there are unmet needs for rapid discovery, repurposing, and development of antiviral drugs. COVID-19 drug development is relevant for acute/pandemic context as well as the endemic disease with SARS-CoV-2 going forward. In the present study, the transcriptome data of the SARS-CoV-2-infected human lung cell lines were used to identify the signature genes for COVID-19 infection. A set of 14 genes was considered as gene signatures from the SARS-CoV-2-infected human bronchial epithelial cells and human alveolar epithelial cell lines. With a translational bioinformatics approach based on reversal of differentially expressed gene signatures, we found that four Food and Drug Administration-approved drugs offer potential for repurposing in a context of COVID-19: Mitomycin, 4-Guanidino-Benzoic Acid, Etretinate, and Staurosporine. We suggest that these drugs warrant further consideration for possible repurposing for the treatment of COVID-19. In summary, the present study underlines the ways in which an omics approach can be harnessed toward translational bioinformatics and rapid COVID-19 drug repurposing.

In-silico screening and identification of potential drug-like compounds for dengue-associated thrombocytopenia from Carica papaya leaf extracts.

Dengue virus is a mosquito-borne pathogen that causes a variety of illnesses ranging from mild fever to severe and fatal dengue haemorrhagic fever or dengue shock syndrome. One of the major clinical manifestations of severe dengue infection is thrombocytopenia. The dengue non-structural protein 1 (NS1) is the primary protein that stimulates immune cells via toll-like receptor 4 (TLR4), induces platelets, and promotes aggregation, which could result in thrombocytopenia. The leaf extracts of Carica papaya seem to have therapeutic benefits in managing thrombocytopenia associated with dengue. The present study focuses on understanding the underlying mechanism of the use of papaya leaf extracts in treating thrombocytopenia. We have identified 124 phytocompounds that are present in the papaya leaf extract. The pharmacokinetics, molecular docking, binding free energy calculations, and molecular dynamic simulations were performed to investigate the drug-like properties, binding affinities, and interaction of phytocompounds with NS1 protein as well as the interactions of NS1 with TLR4. Three phytocompounds were found to bind with the ASN130, a crucial amino acid residue in the active site of the NS1 protein. Thus, we conclude that Rutin, Myricetin 3-rhamnoside, or Kaempferol 3-(2''-rhamnosylrutinoside) may serve as promising molecules by ameliorating thrombocytopenia in dengue-infected patients by interfering the interaction of NS1 with TLR4. These molecules can serve as drugs in the management of dengue-associated thrombocytopenia after verifying their effectiveness and assessing the drug potency, through additional in-vitro assays.Communicated by Ramaswamy H. Sarma.

In-silico studies of Brassica oleracea active compounds and their role in thyroid peroxidase activity.

Cabbage, a leafy vegetable that is widely consumed across the globe, holds a significant place within the Brassica family. For almost a century, its potential anti-thyroid effects have captured attention. The presence of compounds such as thiocyanate and goitrin in cabbage has been extensively investigated for their ability to impede sodium-iodide symporter and thyroid peroxidase (TPO) activities. The present study is focused on uncovering the active constituents in cabbage that could interact with TPO, while also examining their stability under cooking temperatures. Employing molecular docking and molecular dynamic simulation techniques, we quantified the binding strength of phytochemicals present in cabbage with the target. Out of the 60 compounds identified in cabbage leaves, only 18 exhibited docking scores surpassing those of the commercially available anti-thyroid drug, methimazole. These chosen compounds were studied for binding free energy and pharmacokinetic properties. A specific compound, gamma-Terpinene, classified as a monoterpene, emerged as noteworthy due to its alignment with all criteria and the highest observed binding free energy compared to others. Furthermore, we explored the stability of gamma-Terpinene at 373.15K (cooking temperature) and observed its susceptibility to degradation. This might contribute to the relatively diminished anti-thyroid effects of cabbage when consumed in cooked form. Consequently, our findings suggest that the consumption of cooked cabbage could be more conducive to maintaining normal thyroid function, as opposed to its raw counterpart.Communicated by Ramaswamy H. Sarma.

Identification of potential lead compounds against BACE1 through in-silico screening of phytochemicals of Medhya rasayana plants for Alzheimer's disease management.

Alzheimer's disease is a progressive and irreversible neurodegenerative disease that accounts for 70-80% of dementia in the elderly. According to recent clinical data, the incidence of the disease is exponentially increasing with age. Beta-site amyloid precursor protein cleaving enzyme1 (BACE1) is an important molecule involved in the pathogenesis of Alzheimer's disease due to its early role in the amyloid cascade. Cleavage of amyloid precursor protein by BACE1 is the rate-limiting step leading to the production and aggregation of amyloid-beta plaques. A number of natural products are being identified as non-competitive BACE1 inhibitors. In Ayurveda, Medhya rasayana is a group of medicinal herbs, specifically used for managing neurological disorders and is known to be effective in improving cognitivity and intellect. This study aimed to analyze the pharmacological activity of bio-active compounds in Medhya rasayana plants against BACE1, employing structure-based docking approach. 11 compounds out of 876 were identified as potential hits, based on docking scores, binding energies, and interactions with the critical residues of BACE1. Possible neurological activities of these compounds were predicted using PASS server. Out of the 11 compounds screened, two compounds, 'Convolidine' from the plant Convolvulus pleuricaulis Choisy and 'N-(4-hydroxybutyl) phthalimide' from Glycyrrhiza glabra satisfied the pharmacological parameters of Lipinski rule of filtering and ADMET prediction. The binding stability of these compounds against BACE1 was confirmed by molecular dynamic simulation and post dynamic MM/GBSA calculations. Detailed analysis of the interaction with the critical amino acids in the active site revealed the possible inhibitory potential of these compounds of medicinal plant origin against BACE1.

Multiple G-quadruplex binding ligand induced transcriptomic map of cancer cell lines.

The G-quadruplexes (G4s) are a class of DNA secondary structures with guanine rich DNA sequences that can fold into four stranded non-canonical structures. At the genomic level, their pivotal role is well established in DNA replication, telomerase functions, constitution of topologically associating domains, and the regulation of gene expression. Genome instability mediated by altered G4 formation and assembly has been associated with multiple disorders including cancers and neurodegenerative disorders. Multiple tools have also been developed to predict the potential G4 regions in genomes and the whole genome G4 maps are also being derived through sequencing approaches. Enrichment of G4s in the cis-regulatory elements of genes associated with tumorigenesis has accelerated the quest for identification of G4-DNA binding ligands (G4DBLs) that can selectively bind and regulate the expression of such specific genes. In this context, the analysis of G4DBL responsive transcriptome in diverse cancer cell lines is inevitable for assessment of the specificity of novel G4DBLs. Towards this, we assembled the transcripts differentially regulated by different G4DBLs and have also identified a core set of genes regulated in diverse cancer cell lines in response to 3 or more of these ligands. With the mode of action of G4DBLs towards topology shifts, folding, or disruption of G4 structure being currently visualized, we believe that this dataset will serve as a platform for assembly of G4DBL responsive transcriptome for comparative analysis of G4DBLs in multiple cancer cells based on the expression of specific cis-regulatory G4 associated genes in the future.

Cyclophilin A Impairs Efferocytosis and Accelerates Atherosclerosis by Overexpressing CD 47 and Down-Regulating Calreticulin.

Impairment of efferocytosis in apoptotic macrophages is a known determinant of the severity of atherosclerosis and the vulnerability of plaques to rupture. The precise mechanisms involved in impaired efferocytosis are unclear. Given the well-recognized role of the inflammatory cytokine cyclophilin A (Cyp A) in modulating several atherogenic mechanisms in high-glucose primed monocytes, we investigated the role of Cyp A in macrophage efferocytosis. The efficiency of efferocytosis in RAW 264.7 macrophages grown in vitro and primed with cyclophilin A was assessed using flow cytometry and confocal assays. Cholesterol content in cells was measured using cell-based cholesterol efflux assay. Proteomic analysis and bioinformatics tools were employed to decipher the link between cyclophilin A and the known ligand receptors involved in efferocytosis. Cyclophilin A was found to impair efferocytosis in apoptotic macrophages by reducing ABCA1-mediated cholesterol efflux in foam cells derived from macrophages. Cyclophilin A-primed macrophages showed an increase in expression of the don't-eat-me signal CD 47 and a decrease in the expression of the eat-me signal, calreticulin. Phagocytosis was restored upon silencing of cyclophilin A. New Zealand white rabbits were fed a high-fat diet, and lesions in their aortae were analyzed histologically for evidence of atherosclerosis and the expression of Cyp A, CD 47 and calreticulin, the ligand receptor involved in efferocytosis. Gene and protein expressions in aortae and macrophages were analyzed by real-time PCR and Western blotting. Cyclophilin A, via its effects on the expression of CD 47 and calreticulin, impairs efferocytosis in apoptotic macrophages. Together with its impact on cholesterol efflux from macrophages, these effects can amplify other mechanisms of Cyp A in accelerating the progression of atherosclerosis.

Viscosity prediction of Pongamia pinnata (Karanja) oil by molecular dynamics simulation using GAFF and OPLS force field.

The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.

Structure based virtual screening of inhibitors for binding at the allosteric site of HCV RNA dependent RNA polymerase.

Hepatitis C Virus (HCV) infection is a serious cause of chronic liver disease worldwide with more than 170 million infected individuals at a risk of developing significant morbidity and mortality. There is no effective treatment or prevention till date for HCV infection. We describe the computed binding of 10 compounds to the allosteric binding site of RNA dependent RNA polymerase enzyme. These compounds were identified from the ZINC database through virtual screening followed by ADMET evaluation.