Computational biology and chemistry最新文献

Lung cancer is the leading cause of mortality in both men and women due to genetic and epigenetic modifications. Our study focuses on fabricating phenmiazine ring leads by a functional group-based drug design to inhibit p53 -7A1W and MDM2-7AU9 proteins responsible for cancer cell growth. One hundred molecules are designed and allowed to bind inside the active site of 7A1W and 7AU9 protein using a glide dock platform and subjected to find MMGBSA. The stability and interaction were confirmed by MD simulation analysis at 100 ns and DFTB chemical stability study. The result gave the best binding energy of -8.16 kcal/mol for aminobenzoic acid substituted molecule and the MD simulation head map illustrates that majorly 9 amino acids form hydrophobic and h-bond interactions. DFTB analysis reveals the energy gaps of 0.0508 signifying stability and lower chemical reactivity of the Phenmiazine ring derivatives. These findings conclude that the Phenmiazine ring derivative will be a better lead molecule to eradicate lung cancer.

Cyclooxygenase-2 (COX-2), a key enzyme in the inflammatory pathway, is the target for various nonsteroidal anti-inflammatory drugs (NSAIDs) and selective inhibitors known as coxibs. This study focuses on the development of novel imidazole derivatives as COX-2 inhibitors, utilizing a Structure-Activity Relationship (SAR) approach to enhance binding affinity and selectivity. Molecular docking was performed using Autodock Vina, revealing binding energies of -6.928, -7.187, and -7.244 kJ/mol for compounds 5b, 5d, and 5e, respectively. Molecular dynamics simulations using GROMACS provided insights into the stability and conformational changes of the protein-ligand complexes. Key metrics such as RMSD, RMSF, Rg, SASA, and hydrogen bond analysis were employed to assess the interactions. The binding free energy of the inhibitors was estimated using the MMPBSA method, highlighting compound 5b (N-[(3-benzyl-2-methylsulfonylimidazol-4-yl)methyl]-4-methoxyaniline) with the lowest binding energy of -162.014 kcal/mol. ADMET analysis revealed that compound 5b exhibited the most favorable pharmacokinetic properties and safety profile. Overall, this investigation underscores the potential of these novel imidazole derivatives as effective COX-2 inhibitors, with compound 5b emerging as the most promising candidate for further development.

Background: Non-alcoholic fatty liver disease (NAFLD) involves abnormal fat accumulation in the liver, mainly as triglycerides. It ranges from steatosis to non-alcoholic steatohepatitis (NASH), which can lead to inflammation, cellular damage, liver fibrosis, cirrhosis, or hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are crucial for regulating gene expression across various conditions. LncRNAs are emerging as potential putative diagnostic markers for NAFLD-associated HCC.

Methods: We used two human and two mouse datasets from the Gene Expression Omnibus to analyze the expression profiles of mRNAs and lncRNAs. We created a network linking lncRNAs, miRNAs, and mRNAs to investigate the relationships among these RNA types. Additionally, we identified NAFLD-related lncRNAs from existing literature. We then quantified the expression levels of four specific lncRNAs, including PVT1, DUBR, SNHG17, and SNHG14, in the serum of 92 Egyptian participants using qPCR. Finally, we performed a Receiver Operating Characteristic analysis to evaluate the diagnostic potential of the candidate lncRNAs.

Results: Our data suggests that maternally expressed gene 3 (MEG3), H19, and DPPA2 Upstream Binding RNA (DUBR) were significantly upregulated, and plasmacytoma variant translocation 1 (PVT1) was markedly downregulated. PVT1 showed the highest diagnostic accuracy for both NAFLD and NASH. The combined panels of PVT1 +H19 for NAFLD and PVT1 +H19 +DUBR for NASH demonstrated high diagnostic potential. Uniquely, PVT1 can distinguish between NAFLD and NASH. PVT1 exhibited strong diagnostic potential for NAFLD and NASH, individually and in combination with other lncRNAs.

Conclusion: Our study identifies four lncRNAs as putative biomarkers with high specificity and accuracy, individually or combined, for differentiating between NAFLD and NASH. Healthy volunteers with PVT1 possess the highest diagnostic accuracy and significantly discriminate between NAFLD and NASH.

Drug resistance poses a major obstacle to the efficient treatment of colorectal cancer (CRC), which is one of the cancers that kill people most often in the United States. Advanced colorectal cancer patients frequently pass away from the illness, even with advancements in chemotherapy and targeted therapies. Developing new biomarkers and therapeutic targets is essential to enhancing prognosis and therapy effectiveness. My goal in this study was to use bioinformatics analysis of microarray data to find possible biomarkers and treatment targets for colorectal cancer. Using an ArrayExpress database, I examined a dataset on colon cancer to find genes that were differentially expressed (DEGs) in tumor versus healthy tissues. Integration of advanced bioinformatics tools provided robust insights into the identification and analysis of EGFR as a key player. STRING and Cytoscape enabled the construction and visualization of protein-protein interaction networks, highlighting EGFR as a hub gene due to its centrality and interaction profile. Functional enrichment analysis through DAVID revealed EGFR's involvement in critical biological pathways, as identified in GO and KEGG analyses. This underscores the power of combining computational tools to uncover significant biomarkers like EGFR. Autodock Vina screening of the NCI diversity dataset identified two potential EGFR inhibitors, ZINC13597410 and ZINC04896472. MD simulation data revealed that ZINC04896472 could be potential anticancer inhibitor. These findings serve as a basis for the creation of novel therapeutic approaches that target EGFR and other discovered pathways in CRC. The suggested strategy may improve the efficacy of CRC therapy and advance personalized medicine.

Endometriosis is an inflammatory disease, involving immune cell infiltration and production of inflammatory mediators. Ferroptosis has recently been recognized as a mode of controlled cell death and the iron overload and peroxidative environment prevailing in the ectopic endometrium facilitates the occurrence of ferroptosis. In the current investigation, gene expression data was obtained from the dataset GSE7305.The variation in infiltration of immune cells amongst the samples with endometriosis and normal tissue was analysed using the CIBERSORTx tool which revealed higher infiltration of T cells gamma delta, macrophages M2, B cells naïve, T cells CD4 memory resting cells, plasma cells, T cells CD8 and mast cells activated in the tissue samples with endometriosis. An overlap of the differentially expressed genes (DEGs) and ferroptosis related genes revealed 32 ferroptosis related DEGs (FR-DEGs). GO and KEGG pathway analysis showed the FR-DEGs to be enriched in ferroptosis. The PPI network of the FR-DEGs was constructed and TP53, HMOX1, CAV1, CDKN1A, CD44, EPAS1, SLC2A1, MAP3K5, GCLC and FANCD2 were identified as the hub genes. Pearson correlation revealed significant correlation between the hub genes and infiltrating immune cells in endometriosis, thereby suggesting existence of a regulatory crosstalk between immune responses and ferroptosis in endometriosis. Hub gene- miRNA network analysis revealed that 7 of the 10 hub genes were targets of 3 miRNAs -hsa-miR-20a-5p, hsa-miR-16-5p and hsa-miR-17-5p, thereby providing further insight into the regulatory mechanisms underlying disease progression. Predictive analysis and cross validation studies revealed TP53 and CDKN1A as common targets of hsa-miR-16-5p, hsa-miR-17-5p, and hsa-miR-20a-5p, thereby revealing their regulatory roles in ferroptosis and immune modulatory pathways relevant to endometriosis. The present study indicates an important role of both immune dysregulation and ferroptosis in the pathogenesis of endometriosis and identifies ferroptosis related hub genes and their miRNA regulators as favourable novel targets for further studies and therapeutic interventions.

Janus kinases (JAKs) are a family of intracellular tyrosine kinases that play a crucial role in signal transduction pathways. JAK2 has been implicated in the pathogenesis of leukemia, making it a promising target for research aimed at reducing the risk of this disease. This study examined the potential of mimosamycin as a JAK2 inhibitor using both in vitro and in silico approaches. We performed a kinase assay to measure the IC₅₀ of mimosamycin for JAK2 inhibition, which was found to be 22.52 ± 0.87 nM. Additionally, we utilized molecular docking, molecular dynamics simulations, and free energy calculations to investigate the inhibitory mechanism at the atomic level. Our findings revealed that mimosamycin interacts with JAK2 at several key regions: the hinge-conserved region (M929, Y931, L932, and G935), the G loop (L855 and V863), and the catalytic loop (L983). To enhance the binding affinity of mimosamycin toward JAK2, we designed derivatives with propanenitrile and cyclopentane substitutions on the naphthoquinone core structure. Notably, these newly designed analogs exhibited promising binding patterns against JAK2. These insights could aid in the rational development of novel JAK2 inhibitors, with potential applications in the treatment of leukemia and related diseases.

A major threat to world health is the high death rate from gastrointestinal (GI) cancer, especially in Asia, South America, and Europe. The new approaches are needed because of the complexity and heterogeneity of gastrointestinal (GI) cancer, which has made the development of effective treatments difficult. To investigate the potential of peptide-based therapies that target the P21 Activated Kinase 1 (PAK1) in GI cancer, we are using the DBsORF database to predict peptides from the genomes of two bacterial strains: Lactobacillus plantarum and Pediococcus pentosaceus. Energy minimization is then applied for stability after the three-dimensional (3D) structures of these peptides are modeled using the Swiss Model tool. ToxinPred is used for toxicity analysis to verify the safety profiles of the identified peptides. The three-dimensional structure of the target protein PAK1 is taken out of the Protein Data Bank (PDB) and ready for computer analyses. To identify the top-performing peptides for each strain that have good PAK1 binding properties, molecular docking analysis is performed using the ClusPro server. The peptide repertoires of L.plantarum and P. pentosaceus are distinct, and some candidates display low toxicity; for instance, VOIOYA_1513 from P. pentosaceus and BVNTGZ_2921 from L. plantarum demonstrate high binding energies and stable interactions with PAK1. Once the binding energies, hydrogen bonds, and non-bonded contacts have been evaluated, promising peptide candidates are selected. Understanding the dynamics of the peptide-PAK1 complexes is achieved through molecular dynamics simulations performed with the Groningen machine for molecular simulation (Gromacs). Trajectory analysis measures like Radius of Gyration (Rg), Root Mean Square Deviation (RMSD), and Root Mean Square Fluctuation (RMSF) provide insight into the stability and fluctuations of the structure during a 100 ns simulation. Molecular dynamics simulations validate the stability of these complexes, suggesting that, subject to further experimental validation, they could be promising therapeutic candidates. Future research projects and drug development initiatives will benefit from the detailed computational approach, which offers information about the design and evaluation of peptide-based treatments that target PAK1 in GI cancer.

The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E. coli is a major challenge with whole genome sequencing (WGS) and next-generation sequencing (NGS) data. To address this issue, we suggest ARGai 1.0 a deep learning architecture enhanced with generative adversarial networks (GANs). We mitigate data scarcity difficulties by augmenting limited experimental datasets with synthetic data generated by GANs. Our in-silico method (augmentation with feature selection) improves the identification of resistance genes in E. coli by using feature extraction techniques to identify valuable features from actual and GAN-generated data. Employing comprehensive validation, we exhibit the effectiveness of our ARGai 1.0 in precisely identifying the informative and resistant genes. In addition, our ARGai 1.0 identifies the resistant strains with a classification accuracy of 98.96 % on Deep Convolutional Generative Adversarial Network (DCGAN) augmented data. Additionally, ARGai 1.0 achieves more than 98 % of sensitivity and specificity. We also benchmark our ARGai 1.0 with several state-of-the-art AI models for resistant strain classification. In the fight against antibiotic resistance, ARGai 1.0 offers a promising avenue for computational genomics. With implications for research and clinical practice, this work shows the potential of deep networks with GAN augmentation as a practical and successful method for gene identification in E. coli.

Background: Feature rankings are crucial in bioinformatics but can be distorted by influential points (IPs), which are often overlooked. This study aims to investigate the impact of IPs on feature rankings and propose IPs detection method METHOD: We use a leave-one-out approach to assess each case's influence on feature rankings by comparing rank changes after its removal. The rank changes are measured by a novel rank comparison method that involves using adaptive top-prioritized weights that are adjustable to the distribution of rank changes. Our IP detection method was evaluated on several public datasets.

Results: Our method identified potential IPs in several TCGA gene expression datasets, revealing that IPs can severely distort feature rankings. These rank changes can ultimately affect subsequent analyses such as enriched pathways, suggesting the necessity of IPs detection when deriving feature rankings.

Conclusions: IPs significantly impact feature rankings and subsequent analyses; routine IP detection is necessary yet underutilized. Our method is available in the R package findIPs.

The current study focuses on the potential of second-generation antihistamines, which exhibit fewer side effects compared to first-generation drugs, to block the Histamine H₁ receptor (H₁R) and mitigate allergic responses. We screened several derivatives of second-generation drugs taking Desloratadine (Deslo) and Acrivastine (Acra) as seed compounds. We performed molecular docking, drug-likeness, quantum chemical calculations, UV-visible and infrared spectroscopy, molecular electrostatic potential (MEP) mapping for understanding drug derivatives potential as efficient drugs and molecular dynamics (MD). The results depicted that among all Deslo1 showed best binding energy of -8.6 kcal/mol and best inhibition constant too. Moreover, LEU157 formed a conventional hydrogen bond with a ligand at distance of 2.51 Å in Deslo1. Deslo2 showed 95.2 % intestinal absorption which is quite good. None of the drugs showed any toxicity. The residues from catalytic site like Phe 116, Leu 154 and Leu 157 showed reasonably small fluctuations owing to their interactions with respective ligands. The RMSDs of Acra1 and Deslo2 mostly stay within 1Å range. For MD simulations best docked compounds (Acra1, Acra2, Deslo1 and Deslo2) were chosen and carried for 120 ns (120 ×10⁶ fs). MD simulations trajectory is analyzed for the assessment of some important parameters like RMSD, RMSF, SASA, and RG. Moreover, ADMET analysis are performed to confirm their drug-like properties. The molecular geometries of Acra2 are optimized in gas phase as well as water solvent environments to simulate aqueous like conditions for optimized geometries. Significant differences are observed in the bond lengths and angles especially for polar functional groups, due to the solvation of hydrogen-bond donors and acceptors. The current study identify new therapeutic candidates for managing allergic rhinitis, which may evoke the scientific interests of scientists through in-vivo testing of hit drugs that were not explored previously.