Elucidating the functional impact of G137V and G144R variants in Maroteaux Lamy's Syndrome by Molecular Dynamics Simulation.

Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of - 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.

CDK regulators-Cell cycle progression or apoptosis-Scenarios in normal cells and cancerous cells.

Serine/threonine kinases called cyclin-dependent kinases (CDKs) interact with cyclins and CDK inhibitors (CKIs) to control the catalytic activity. CDKs are essential controllers of RNA transcription and cell cycle advancement. The ubiquitous overactivity of the cell cycle CDKs is caused by a number of genetic and epigenetic processes in human cancer, and their suppression can result in both cell cycle arrest and apoptosis. This review focused on CDKs, describing their kinase activity, their role in phosphorylation inhibition, and CDK inhibitory proteins (CIP/KIP, INK 4, RPIC). We next compared the role of different CDKs, mainly p21, p27, p57, p16, p15, p18, and p19, in the cell cycle and apoptosis in cancer cells with respect to normal cells. The current work also draws attention to the use of CDKIs as therapeutics, overcoming the pharmacokinetic barriers of pan-CDK inhibitors, analyze new chemical classes that are effective at attacking the CDKs that control the cell cycle (cdk4/6 or cdk2). It also discusses CDKI's drawbacks and its combination therapy against cancer patients. These findings collectively demonstrate the complexity of cancer cell cycles and the need for targeted therapeutic intervention. In order to slow the progression of the disease or enhance clinical outcomes, new medicines may be discovered by researching the relationship between cell death and cell proliferation.

A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information.

This study systematically reviews the Artificial Intelligence (AI) methods developed to resolve the critical process of COVID-19 gene data analysis, including diagnosis, prognosis, biomarker discovery, drug responsiveness, and vaccine efficacy. This systematic review follows the guidelines of Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA). We searched PubMed, Embase, Web of Science, and Scopus databases to identify the relevant articles from January 2020 to June 2022. It includes the published studies of AI-based COVID-19 gene modeling extracted through relevant keyword searches in academic databases. This study included 48 articles discussing AI-based genetic studies for several objectives. Ten articles confer about the COVID-19 gene modeling with computational tools, and five articles evaluated ML-based diagnosis with observed accuracy of 97% on SARS-CoV-2 classification. Gene-based prognosis study reviewed three articles and found host biomarkers detecting COVID-19 progression with 90% accuracy. Twelve manuscripts reviewed the prediction models with various genome analysis studies, nine articles examined the gene-based in silico drug discovery, and another nine investigated the AI-based vaccine development models. This study compiled the novel coronavirus gene biomarkers and targeted drugs identified through ML approaches from published clinical studies. This review provided sufficient evidence to delineate the potential of AI in analyzing complex gene information for COVID-19 modeling on multiple aspects like diagnosis, drug discovery, and disease dynamics. AI models entrenched a substantial positive impact by enhancing the efficiency of the healthcare system during the COVID-19 pandemic.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

Deciphering the effect of mutations in MMAA protein causing methylmalonic acidemia-A computational approach.

Methylmalonic acidemia (MMA) is a rare genetic disorder affecting multiple body systems. We aimed to investigate the pathogenic mutations in MMAA that are associated with isolated methylmalonic acidemia to identify the structural behavior of MMAA upon mutation. The algorithms such as PredictSNP, iStable, ConSurf, and Align GVGD were employed to analyze the consequence of the mutations. Molecular docking was carried out for the native MMAA, L89P, G274D, and R359G to interpret its interactions with the GDP substrate. The docked complexes were simulated for 200ns aiding GROMACS in apprehending the behavior of MMAA upon mutation and GDP binding. After simulation, cα disruptions were observed using the RMSF plot, which indicated that several regions of mutant MMAAs have highly fluctuated. The gyration and H-bond plots were used to understand the compactness and intermolecular interaction with the GDP molecule. The MDS analysis showed that the mutations L89P, G274D, and R359G are highly unstable even after GDP binding, with the least compactness, fewer H-bonds, and larger conformational cα motions. Our study provided structural and dynamic insights into MMAA protein, which further helps to characterize these mutants and provide potential treatment strategies for MMA patients.

Understanding the activating mechanism of the immune system against COVID-19 by Traditional Indian Medicine: Network pharmacology approach.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmissions are occurring rapidly; it is raising the alarm around the globe. Though vaccines are currently available, the evolution and mutations in the SARS-CoV-2 threaten available vaccines' significance. The drugs are still undergoing clinical trials, and certain medications are approved for "emergency use" or as an "off-label" drug during the pandemic. These drugs have been effective yet accommodating side effects, which also can be lethal. Complementary and alternative medicine is highly demanded since it embraces a holistic approach. Since ancient times, natural products have been used as drugs to treat various diseases in the medical field and are still widely practiced. Medicinal plants contain many active compounds that serve as the key to an effective drug design. The Kabasura kudineer and Nilavembu kudineer are the two most widely approved formulations to treat COVID-19. However, the mechanism of these formulations is not well known. The proposed study used a network pharmacology approach to understand the immune-boosting mechanism by the Kabasura kudineer, Nilavembu kudineer, and JACOM in treating COVID-19. The plants and phytochemical chemical compounds in the Kabasura kudineer, Nilavembu kudineer, and JACOM were obtained from the literature. The Swiss target prediction algorithm was used to predict the targets for these phytochemical compounds. The common genes for the COVID-19 infection and the drug targets were identified. The gene-gene interaction network was constructed to understand the interactions between these common genes and enrichment analyses to determine the biological process, molecular functions, cellular functions, pathways involved, etc. Finally, virtual screening and molecular docking studies were performed to identify the most potential targets and significant phytochemical compounds to treat the COVID-19. The present study identified potential targets as ACE, Cathepsin L, Cathepsin B, Cathepsin K, DPP4, EGFR, HDAC2, IL6, RIPK1, and VEGFA. Similarly, betulinic acid, 5″-(2⁗-Hydroxybenzyl) uvarinol, antofine, (S)-1'-methyloctyl caffeate, (Z)-3-phenyl-2-propenal, 7-oxo-10α-cucurbitadienol, and PLX-4720 collectively to be potential treatment agents for COVID-19.