Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment

Frontiers in Chemistry, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 9, 2025

Treatment of type 2 diabetes (T2D) remains a significant challenge because its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) promising antidiabetic target plays key role regulation, especially glucose homeostasis energy expenditure. TGR5 agonists are attractive candidates for T2D therapy their ability improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), dynamics simulations (MDS) explore novel small molecules as potential agonists. Bioactivity data known were obtained from the ChEMBL database. The dataset was cleaned descriptors based on Lipinski's rule five selected input features ML model, which built using Random Forest algorithm. optimized model screen COCONUT database predict features. 6,656 compounds predicted docked within active site calculate binding energies. four top-scoring with lowest energies activities compared those co-crystallized ligand. A 100 ns MDS assess stability TGR5. Molecular results showed had stronger affinity than cocrystallized revealed stable pocket. combination ML, MD, provides powerful approach predicting can be optimised treatment.

Language: Английский

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Multiple Sclerosis Stages and their Differentially Expressed Genes: A Bioinformatics Analysis

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 23, 2024

Abstract Multiple Sclerosis (MS) is an inflammatory, chronic, autoimmune, and demyelinating disease of the central nervous system. MS a heterogeneous with three main clinical forms, affecting progression therefore treatment disease. Thus, finding key genes microRNAs (miRNA) associated stages analyzing their interactions important to better understand molecular mechanism underlying occurrence evolution MS. Based on publicly available datasets mRNA miRNA expression profiles, differentially expressed (DEGs) miRNAs (DEMs) between patients different healthy controls relapsing remitting phases RRMS were determined using Deseq2 GEO2R tools. We then analyzed miRNA-mRNA regulatory gene ontology for DEGs. interactions, we identified potential biomarkers RRMS, 13 upregulated regulators 30 downregulated 17 32 genes. also 9 12 as SPMS. Our study findings highlight some protein-coding that are involved in

Language: Английский

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Expression Level Analysis of ACE2 Receptor Gene in African-American and Non-African-American COVID-19 Patients

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 12, 2023

Abstract Background The COVID-19 pandemic caused by SARS-CoV-2 has spread rapidly across the continents. While incidence of been reported to be higher among African-American individuals, rate mortality lower compared that non-African-Americans. ACE2 is involved in as uses enzyme enter host cells. Although difference can explained many factors such low accessibility health insurance community, little known about expression patients non-African-American patients. variable genes contribute this observed phenomenon. Methodology In study, transcriptomes from and were retrieved sequence read archive analyzed for gene expression. HISAT2 was used align reads human reference genome, HTseq-count get raw counts. EdgeR utilized differential analysis, enrichR employed enrichment analysis. Results datasets included 14 33 transcriptome sequences descent, respectively. There 24,092 differentially expressed genes, with 7,718 upregulated (log fold change > 1 FDR 0.05) 16,374 downregulated −1 0.05). mRNA level found considerably cohort (p-value = 0.0242, p-adjusted value 0.038). Conclusion downregulation could indicate a correlation severity community.

Language: Английский

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Differential Expression Analysis of miRNAs and mRNAs in Epilepsy Uncovers Potential Biomarkers

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 14, 2023

Abstract Epilepsy is a neurological disease defined by episodes of synchronous convulsions. Recently, miRNAs have been proven as promising biomarkers for multiple ailments like tumors and neurodegenerative disorders; their role in epilepsy still unclear. This study aimed to understand the involvement detect potential treatment epilepsy. RNA transcripts, miRNA from brain tissue plasma small extracellular vesicle samples epileptogenic patients 6 different studies downloaded NCBI sequence read archive (SRA) were analyzed with particular interest genes that might be involved Alignment transcripts hg38 was done using HISAT2 raw counts generated HTseq-count. identified miRDeep2. EdgeR GEO2 used identify DEGs both mRNA datasets. Finally, TargetScan web tool predict potentially significantly expressed target genes. Analysis these datasets revealed associated miRNAs. SIX4 KCTD7 under-expressed zones compared irritative zone. CABP1, SLC20A1 SLC35G1 tissues. Hsa-miR-27a-3p regulator CABP1 expression, hsa-let-7b-5p regulates while hsa-miR-15a-5p hsa-miR-195-5p are regulators SLC35G1. These observations highlight importance novel Understanding controlling regulatory interactions may help define therapies would also better miRNA-mediated gene regulation

Language: Английский

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Targeting aldose reductase using natural African compounds as promising agents for managing diabetic complications

Frontiers in Bioinformatics, Journal Year: 2025, Volume and Issue: 5

Published: Feb. 6, 2025

Background Diabetes remains a leading cause of morbidity and mortality due to various complications induced by hyperglycemia. Inhibiting Aldose Reductase (AR), an enzyme that converts glucose sorbitol, has been studied prevent long-term diabetic consequences. Unfortunately, drugs targeting AR have demonstrated toxicity, adverse reactions, lack specificity. This study aims explore African indigenous compounds with high specificity as potential inhibitors for pharmacological intervention. Methodology A total 7,344 from the AfroDB, EANPDB, NANPDB databases were obtained pre-filtered using Lipinski rule five generate compound library virtual screening against Reductase. The top 20 highest binding affinity selected. Subsequently, in silico analyses such protein-ligand interaction, physicochemical pharmacokinetic profiling (ADMET), molecular dynamics simulation coupled free energy calculations performed identify lead low toxicity. Results Five natural compounds, namely, (+)-pipoxide, Zinc000095485961, Naamidine A, (−)-pipoxide, 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside, identified aldose reductase. Molecular docking results showed these exhibited energies ranging −12.3 −10.7 kcal/mol, which better than standard (zopolrestat, epalrestat, IDD594, tolrestat, sorbinil) used this study. ADMET interaction revealed interacted key inhibiting residues through hydrogen hydrophobic interactions favorable toxicity profiles. Prediction biological activity highlighted Zinc000095485961 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside having significant inhibitory simulations MM-PBSA analysis confirmed bound stability less conformational change AR-inhibitor complex. Conclusion 5 belong region: (+)-Pipoxide, (−)-Pipoxide, 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside. These molecules reductase, polyol pathway, can be developed therapeutic agents manage complications. However, we recommend vitro vivo studies confirm our findings.

Language: Английский

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Neural networks optimization via Gauss–Newton based QR factorization on SARS-CoV-2 variant classification

Systems and Soft Computing, Journal Year: 2025, Volume and Issue: 7, P. 200195 - 200195

Published: Feb. 14, 2025

Language: Английский

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Efficient and easy gene expression and genetic variation data analysis and visualization using exvar

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 10, 2025

Abstract RNA sequencing data manipulation workflows are complex and require various skills tools. This creates the need for user-friendly integrated genomic analysis visualization We developed a novel R package using multiple Cran Bioconductor packages to perform gene expression genetic variant calling from data. Multiple public datasets were analyzed validate pipeline all supported species. The package, named “exvar”, includes functions three shiny apps as functions. Also, it could be used analyze several species’ exvar is available in project’s GitHub repository ( https://github.com/omicscodeathon/exvar ).

Language: Английский

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Prostruc: an open-source tool for 3D structure prediction using homology modeling

Frontiers in Chemistry, Journal Year: 2024, Volume and Issue: 12

Published: Nov. 29, 2024

Introduction Homology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise resources, presenting barrier many researchers. Methods Prostruc Python-based homology tool designed simplify structure prediction through an intuitive, automated pipeline. Integrating Biopython sequence alignment, BLAST template identification, ProMod3 generation, streamlines complex workflows into user-friendly interface. The enables researchers input sequences, identify homologous templates from databases such as Protein Data Bank (PDB), generate high-quality 3D with minimal expertise. implements two-stage vSquarealidation process: first, it uses TM-align comparison, assessing Root Mean Deviations (RMSD) TM scores against reference models. Second, evaluates model quality via QMEANDisCo ensure high accuracy. Results top five models are selected these metrics provided user. stands out by offering scalability, flexibility, ease use. It accessible cloud-based web interface or Python package local use, ensuring adaptability across research environments. Benchmarking like SWISS-MODEL,I-TASSER Phyre2 demonstrates Prostruc's competitive performance in terms accuracy job runtime, while its open-source nature encourages community-driven innovation. Discussion positioned advancement modeling, making more scientific community.

Language: Английский

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Machine learning and molecular docking prediction of potential inhibitors against dengue virus

Frontiers in Chemistry, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 24, 2024

Introduction Dengue Fever continues to pose a global threat due the widespread distribution of its vector mosquitoes, Aedes aegypti and albopictus . While WHO-approved vaccine, Dengvaxia, antiviral treatments like Balapiravir Celgosivir are available, challenges such as drug resistance, reduced efficacy, high treatment costs persist. This study aims identify novel potential inhibitors virus (DENV) using an integrative discovery approach encompassing machine learning molecular docking techniques. Method Utilizing dataset 21,250 bioactive compounds from PubChem (AID: 651640), alongside total 1,444 descriptors generated PaDEL, we trained various models Support Vector Machine, Random Forest, k-nearest neighbors, Logistic Regression, Gaussian Naïve Bayes. The top-performing model was used predict active compounds, followed by performed AutoDock Vina. detailed interactions, toxicity, stability, conformational changes selected were assessed through protein-ligand interaction studies, dynamics (MD) simulations, binding free energy calculations. Results We implemented robust three-dataset splitting strategy, employing Regression algorithm, which achieved accuracy 94%. successfully predicted 18 known DENV inhibitors, with 11 identified active, paving way for further exploration 2683 new ZINC EANPDB databases. Subsequent studies on NS2B/NS3 protease, enzyme essential in viral replication. ZINC95485940, ZINC38628344, 2′,4′-dihydroxychalcone ZINC14441502 demonstrated affinity −8.1, −8.5, −8.6, −8.0 kcal/mol, respectively, exhibiting stable interactions His51, Ser135, Leu128, Pro132, Ser131, Tyr161, Asp75 within site, critical residues involved inhibition. Molecular simulations coupled MMPBSA elucidated making it promising candidate development. Conclusion Overall, this approach, combining learning, docking, highlights strength utility computational tools discovery. It suggests pathway rapid identification development drugs against DENV. These silico findings provide strong foundation future experimental validations in-vitro aimed at fighting

Language: Английский

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XCNN-SC: Explainable CNN for SARS-CoV-2 variants classification and mutation detection

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 167, P. 107606 - 107606

Published: Oct. 19, 2023

Language: Английский

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