Computational Biology and Chemistry最新文献

Abscisic acid (ABA) is a crucial plant hormone that is naturally produced in various mammalian tissues and holds significant potential as a therapeutic molecule in humans. ABA is selected for this study due to its known roles in essential human metabolic processes, such as glucose homeostasis, immune responses, cardiovascular system, and inflammation regulation. Despite its known importance, the molecular mechanism underlying ABA's action remain largely unexplored. This study employed computational techniques to identify potential human ABA receptors. We screened 64 candidate molecules using online servers and performed molecular docking to assess binding affinity and interaction types with ABA. The stability and dynamics of the best complexes were investigated using molecular dynamics simulation over a 100 ns time period. Root mean square fluctuations (RMSF), root mean square deviation (RMSD), solvent-accessible surface area (SASA), radius of gyration (Rg), free energy landscape (FEL), and principal component analysis (PCA) were analyzed. Next, the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method was employed to calculate the binding energies of the complexes based on the simulated data. Our study successfully pinpointed four key receptors responsible for ABA signaling (androgen receptor, glucocorticoid receptor, mineralocorticoid receptor, and retinoic acid receptor beta) that have a strong affinity for binding with ABA and remained structurally stable throughout the simulations. The simulations with Hydralazine as an unrelated ligand were conducted to validate the specificity of the identified receptors for ABA. The findings of this study can contribute to further experimental validation and a better understanding of how ABA functions in humans.

DNA methylation at the N6 position of adenine (N6-methyladenine, 6 mA), which refers to the attachment of a methyl group to the N6 site of the adenine (A) of DNA, is an important epigenetic modification in prokaryotic and eukaryotic genomes. Accurately predicting the 6 mA binding sites can provide crucial insights into gene regulation, DNA repair, disease development and so on. Wet experiments are commonly used for analyzing 6 mA binding sites. However, they suffer from high cost and expensive time. Therefore, various deep learning methods have been widely used to predict 6 mA binding sites recently. In this study, we develop a framework based on multi-scale DNA language model named "iDNA6mA-MDL". "iDNA6mA-MDL" integrates multiple kmers and the nucleotide property and frequency method for feature embedding, which can capture a full range of DNA sequence context information. At the prediction stage, it also leverages DNABERT to compensate for the incomplete capture of global DNA information. Experiments show that our framework obtains average AUC of 0.981 on a classic 6 mA rice gene dataset, going beyond all existing advanced models under fivefold cross-validations. Moreover, "iDNA6mA-MDL" outperforms most of the popular state-of-the-art methods on another 11 6 mA datasets, demonstrating its effectiveness in 6 mA binding sites prediction.

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is the primary cause of dementia. It is characterised by the gradual loss of brain cells, which results in memory loss and cognitive dysfunction. One of the hallmarks of AD is an abnormally upregulated glutaminyl-peptide cyclotransferase (QPCT or QC) enzyme. Not only AD, but QC has also been implicated with pathological conditions like Huntington's disease (HD), melanomas, carcinomas, atherosclerosis, and septic arthritis. Therefore, the inhibition of QC emerged as a potential strategy for preventing multiple pathological conditions. Considering this, we screened a library of 153,536 imidazole-based compounds against a doubly mutant (Y115E-Y117E) QC target. Molecular docking based virtual screening and absorption, distribution, metabolism, excretion/toxicity (ADME/T) predictions identified five compounds, namely 118981836, 136459842, 139388116, 139388226, and 139958725. Furthermore, molecular dynamics (MD) simulations of 500 ns were conducted to investigate the behaviour of the identified compounds with the target receptor. The results were compared to the co-ligand by analysing RMSD, RMSF, and SASA parameters. To our knowledge, this is the first computational study that employed a protein with double mutation to identify new imidazole-based QC-inhibitors.

Triple negative breast cancer (TNBC) presents a significant global health concern due to its aggressive nature, high mortality rate and limited treatment options, highlighting the urgent need for targeted therapies. Beauvericin, a bioactive fungal secondary metabolite, possess significant anticancer potential, although its molecular targets in cancer cells remain unexplored. This study has investigated possible molecular targets of beauvericin and its therapeutic insights in TNBC cells. In silico studies using molecular docking and MD simulation predicted the molecular targets of beauvericin. The identified targets included MRP-1 (ABCC1), HDAC-1, HDAC-2, LCK and SYK with average binding energy of −90.1, −44.3, −72.1, −105 and −60.8 KJ/mol, respectively, implying its multifaceted roles in reversing drug resistance, inhibiting epigenetic modulators and oncogenic tyrosine kinases. Beauvericin has significantly reduced the viability of MDA-MB-231 and MDA-MB-468 cells, with IC₅₀ concentrations of 4.4 and 3.9 µM, while concurrently elevating the intracellular ROS by 9.0 and 7.9 folds, respectively. Subsequent reduction of mitochondrial transmembrane potential in TNBC cells, has confirmed the induction of oxidative stress, leading to apoptotic cell death, as observed by flow cytometric analyses. Beauvericin has also arrested cell cycle at G1-phase and impaired the spheroid formation and clonal expansion abilities of TNBC cells. The viability of spheroids was reduced upon beauvericin treatment, exhibiting IC₅₀ concentrations of 10.3 and 6.2 µM in MDA-MB-468 and MDA-MB-231 cells, respectively. In conclusion, beauvericin has demonstrated promising therapeutic potential against TNBC cells through possible inhibition of MRP-1 (ABCC1), HDAC-1, HDAC-2, LCK and SYK.

Coronary heart disease (CHD), a multifactorial cardiovascular condition, arises from the accumulation of atherosclerotic plaque in the coronary arteries, resulting in compromised blood flow to the heart and complications such as angina, myocardial infarction, or heart failure. Addressing global prevalence, risk factors, and genetics is crucial for effective management. The current study aims to identify molecular biomarkers for CHD by scrutinizing the expression patterns of differentially expressed genes (DEGs), utilizing various bioinformatic tools. In this investigation, a total of 24 samples underwent examination using the GEO2R tool. These included eight samples from individuals before treatment (GSM5434123–30), eight samples from patients after Dan-Lou tablet treatment (GSM5434131–38), and eight samples from healthy control subjects (GSM5434139–46). A suite of bioinformatics tools was used to detect enriched genes within the network, namely, Cytoscape (v3.10.1) and Molecular Complex Detection (MCODE). Functional analysis of the DEGs was conducted via clusterProfiler, a R-based package, and ClueGO. 182 and 174 DEGs corresponding to untreated and treated patient sample groups were functionally annotated for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. ARF6 gene dysregulation was implicated in the myeloid cell apoptotic process (GO:0033028), regulation of actin cytoskeleton (hsa:04810), and other vital cellular functions. The myeloid cell apoptotic process (GO:0033028) was also observed to be regulated by the differential expression of the STAT5B gene. Additionally, STAT5B was found to be associated with the regulation of erythrocyte differentiation (GO:0045646). Providing targeted therapy based on the patient's idiosyncratic gene expression profiles could lead to the curing of various disorders in the near future.

α-Synuclein (α-syn) is an intrinsically disordered protein, linked genetically and neuropathologically to Parkinson's disease where this protein aggregates within the brain. Hence, identifying compounds capable of impeding α-syn aggregation puts forward a promising approach for the development of disease-modifying therapies. Herein, we investigated the efficacy of Ribavirin, an FDA-approved compound, in curtailing α-syn amyloid transformation, employing an array of bioinformatic tools and systematic analysis using biophysical techniques. Ribavirin shows a dose dependent anti-aggregation propensity where it effectively subdued the formation of mature fibrillar aggregates of α-syn, where even at the lowest concentration there was a 69 % reduction in the ThT maxima. Ribavirin averts the formation of mature fibrillar aggregates by interacting with the NAC domain of α-syn. Ribavirin redirects the amyloid transformation of α-syn by emanating aggregates of lower order with reduced cross β-sheet signature and revokes the formation of on-pathway amyloids. Collectively, our study puts forward the novel potency of Ribavirin as a promising molecule for therapeutic intervention in Parkinson’s disease.

Research suggests curcumin's safety and efficacy, prompting interest in its use for treating and preventing various human diseases. The current study aimed to predict drag ability of methyl substituted curcumin derivatives (BL1 to BL4) using SwissADME and Density Functional Theory (DFT) approaches. The curcumin derivatives investigated mostly adhere to Lipinski's rule of five, with molecular properties including MW, F. Csp3, nHBA, nHBD, and TPSA falling within acceptable limits. The compounds demonstrating high lipophilicity while poor water solubility. The pharmacokinetic evaluation revealed favorable gastrointestinal absorption and blood-brain barrier permeation while none were identified as substrates for P-glycoprotein, however, revealed inhibitory actions against various cytochrome P450 enzymes. Additionally, all derivatives exhibited a consistent bioavailability score of 0.55. Similarly, the DFT computations of the compounds of the curcumin derivatives were conducted at B3LYP/6–311 G** level to predict and then assess the key electronic characteristics underlying the bioactivity. Accordingly, the BL4 molecule (ΔE_gap= 4.105 eV) would prefer to interact with the external molecular system more than the other molecules due to having the biggest energy gap. The ΔN_max (2.328 eV) and Δε_back-donat. (-0.446 eV) scores implied that BL1 would have more charge transfer capability and the lowest stability via back donation among the compounds. In short, the derivative (BL1 to BL4) exhibited strong extrinsic therapeutic properties and therefore stand eligible for further in vitro and in vivo studies.

The development of analytical methods for Genome-wide Association Studies (GWAS) has outpaced the evolution of simulation techniques and pipelines. This disparity underscores the importance of innovative simulation methods that can keep pace with the rapidly increasing scale of GWAS. The median sample size of GWAS over the past ten years has exceeded 50,000 individuals, a trend that emphasizes the need for simulation tools capable of generating data on a similar or larger scale. This paper introduces a novel method, the small-group originating (SGO) model, utilizing the SLiM software for simulating individual-level GWAS data. Our standardized protocol facilitates the generation of tens of thousands of pseudo-individuals with millions of variants from small (30−90) open-access datasets.

SGO stands out, especially when compared to the widely-used resampling method in HapGen, showcasing superior simulation efficiency for large sample sizes (> 13,000) of unrelated individuals. This capability is particularly relevant given the current trajectory towards larger GWAS, necessitating tools that can simulate datasets reflective of this growth. Additionally, SGO provides customization options and can model dynamic life cycles and mating across generations, positioning it as a highly promising alternative for GWAS simulations.

In a case study, sensitivity analyses of chromosome-level principal component analysis and kinship coefficient estimation were conducted. The results highlighted the poor robustness of chromosome-level quality control (QC) indexes and the uneven distribution of population structure across chromosomes and ancestries, advocating for the caution against relying solely on chromosome-level QC statistics.

With its flexible and efficient approach to generating pseudo GWAS data, our standardized SGO protocol emerges as a crucial asset for method development, power analysis, and benchmarking in GWAS research. It is especially vital in the context of accommodating the demands for large-scale simulations, aligning with the current and future scale of GWAS.

Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface (PES) of these molecular torsions is extremely important and precious. Machine learning (ML), including deep learning (DL), is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level ML to predict the PESs of the L-3,4-dihydroxyphenylalanine (Levodopa) antiparkinsonian drug molecule. The electronic energies and structural parameters calculated by density functional theory (DFT) using the wB97X method and all possible Pople's basis sets indicated the 6–31G(d) basis set, when used with the wB97X functional, exhibits behavior similar to that of the ANI-1x model. The vibrational frequencies investigation showed a linear correlation between DFT and ML data. All ANI-1x calculations were completed quickly in a very short computing time. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies.

Objectives

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

Materials and methods

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

Results

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

Conclusion

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.