Computational Biology and Chemistry最新文献

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems. We find that the spontaneous SNM rates in our data are correlated with many genomic features in prokaryotes and unicellular eukaryotes irrespective of their sample sizes. On the other hand, only the number of coding genes was correlated with the spontaneous SNM rates in multicellular eukaryotes primarily contributed by vertebrates data. Considering local features, we notice that local GC content and CpG content significantly were correlated with the spontaneous SNM rates in the unicellular eukaryotes, while local repeat fraction is an important feature in prokaryotes and certain specific uni- and multi-cellular eukaryotes. Such predictive features of the spontaneous SNM rates often support non-linear models as the best fit compared to the linear model. We also observe that the strand asymmetry in prokaryotes plays an important role in determining the spontaneous SNM rates but the SNM spectrum does not.

Persistent exposure to low-dose of cadmium is strongly linked to both the development and prognosis of non-small cell lung cancer (NSCLC), yet the precise molecular mechanism behind this relationship remains uncertain. In this study, cadmium-related pathogenic genes (CRPGs) in NSCLC were identified via differential expression analysis. NSCLC patient clusters related to CRPGs were constructed through univariate Cox and K-means clustering algorithms. Multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to determine the prognosis. Sixteen CRPGs showed a significant association with NSCLC. We found biological and prognostic differences between patients in clusters A and B. A predictive prognostic risk model for NSCLC revealed that FAM83H, MSMO1, and SNAI1 are central. Hence, the 3 hub genes were named. To further elucidate the role of CRPGs in NSCLC, A549 cells were exposed to CdCl₂. The mRNA and protein expression levels of the 3 hub genes and cell invasion were detected. Moreover, 10 μM CdCl₂ may increase the protein expression of 3 hub genes and enhance the invasive ability of A549 cells. This risk model may have established a theoretical foundation for investigating the mechanisms, treatment, and prognosis of NSCLC.

DNA methylation is an important epigenetic modification involved in gene regulation. Advances in the next generation sequencing technology have enabled the retrieval of DNA methylation information at single-base-resolution. However, due to the sequencing process and the limited amount of isolated DNA, DNA-methylation-data are often noisy and sparse, which complicates the identification of differentially methylated regions (DMRs), especially when few replicates are available. We present a varying-coefficient model for detecting DMRs by using single-base-resolved methylation information. The model simultaneously smooths the methylation profiles and allows detection of DMRs, while accounting for additional covariates. The proposed model takes into account possible overdispersion by using a beta-binomial distribution. The overdispersion itself can be modeled as a function of the genomic region and explanatory variables. We illustrate the properties of the proposed model by applying it to two real-life case studies.

Cell-penetrating peptides have attracted much attention for their ability to break through cell membrane barriers, which can improve drug bioavailability, reduce side effects, and promote the development of gene therapy. Traditional wet-lab prediction methods are time-consuming and costly, and computational methods provide a short-time and low-cost alternative. Still, the accuracy and reliability need to be further improved. To solve this problem, this study proposes a feature fusion-based prediction model, where the protein pre-trained language models ProtBERT and ESM-2 are used as feature extractors, and the extracted features from both are fused to obtain a more comprehensive and effective feature representation, which is then predicted by linear mapping. Validated by many experiments on public datasets, the method has an AUC value as high as 0.983 and shows high accuracy and reliability in cell-penetrating peptide prediction.

A new series of 2H-chromene-based sulfonamide derivatives 3-12 has been synthesized and characterized using different spectroscopic techniques. The synthesized 2H-chromenes were synthesized by reacting activated methylene with 5-(piperidin-1-ylsulfonyl)salicylaldehyde through one-step condensation followed by intramolecular cyclization. Virtual screening of the designed molecules on α-glucosidase enzymes (PDB: 3W37 and 3A4A) exhibited good binding affinity suggesting that these derivatives may be potential α-glucosidase inhibitors. In-vitro α-glucosidase activity was conducted firstly at 100 µg/mL, and the results demonstrated good inhibitory potency with values ranging from 90.6% to 96.3% compared to IP = 95.8% for Acarbose. Furthermore, the IC₅₀ values were determined, and the designed derivatives exhibited inhibitory potency less than 11 µg/mL. Surprisingly, two chromene derivatives 6 and 10 showed the highest potency with IC₅₀ values of 0.975 ± 0.04 and 0.584 ± 0.02 µg/mL, respectively, compared to Acarbose (IC₅₀ = 0.805 ± 0.03 µg/mL). Moreover, our work was extended to evaluate the in-vitro α-amylase and PPAR-γ activity as additional targets for diabetic activity. The results exhibited moderate activity on α-amylase and potency as PPAR-γ agonist making it a multiplet antidiabetic target. The most active 2H-chromenes 6 and 10 exhibited significant activity to PPAR-γ with IC₅₀ values of 3.453 ± 0.14 and 4.653 ± 0.04 µg/mL compared to Pioglitazone (IC₅₀ = 4.884±0.29 µg/mL) indicating that these derivatives improve insulin sensitivity by stimulating the production of small insulin-sensitive adipocytes. In-silico ADME profile analysis indicated compliance with Lipinski's and Veber's rules with excellent oral bioavailability properties. Finally, the docking simulation was conducted to explain the expected binding mode and binding affinity.

Emerging as a promising drug target for Alzheimer's disease (AD) therapy, glycogen synthase kinase 3β (GSK-3β) has garnered attention. This study sought to rigorously scrutinize a compendium of natural compounds retrieved from the ZINC database through pharmacodynamic experiments, employing a 1 H-indazole-3-carboxamide (INDZ) scaffold, to identify compounds capable of inhibiting the GSK-3β protein. Utilizing a multi-step approach, the study involved pharmacophore analysis, followed by molecular docking to select five promising ligands for further investigation. Subsequently, ESMACS simulations were employed to assess the stability of the ligand-protein interactions. Evaluation of the binding modes and free energy of the ligands revealed that five compounds (2a-6a) exhibited crucial interactions with the active site residues. Furthermore, various methodologies, including hydrogen bond and clustering analyses, were utilized to ascertain their inhibitory potential and elucidate the factors contributing to ligand binding in the protein's active site. The findings from MMPBSA/GBSA analysis indicated that these five selected small molecules closely approached the IC50 value of the reference ligand (OH8), yielding energy values of −34.85, −32.58, −31.71, and −30.39 kcal/mol, respectively. Additionally, an assessment of the interactions using hydrogen bond and dynamic analyses delineated the effective binding of the ligands with the binding pockets in the protein. Through computational analysis, we obtained valuable insights into the molecular mechanisms of GSK-3β, aiding in the development of more potent inhibitors.

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC₅₀ between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC₅₀ = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

The number of mitotic cells is an important indicator of grading invasive breast cancer. It is very challenging for pathologists to identify and count mitotic cells in pathological sections with naked eyes under the microscope. Therefore, many computational models for the automatic identification of mitotic cells based on machine learning, especially deep learning, have been proposed. However, converging to the local optimal solution is one of the main problems in model training. In this paper, we proposed a novel multilevel iterative training strategy to address the problem. To evaluate the proposed training strategy, we constructed the mitotic cell classification model with ResNet50 and trained the model with different training strategies. The results showed that the models trained with the proposed training strategy performed better than those trained with the conventional strategy in the independent test set, illustrating the effectiveness of the new training strategy. Furthermore, after training with our proposed strategy, the ResNet50 model with Adam optimizer has achieved 89.26% F1 score on the public MITOSI14 dataset, which is higher than that of the state-of-the-art methods reported in the literature.

Trastuzumab resistance presents a significant challenge in the treatment of HER2+ breast cancer, necessitating the investigation of combination therapies to overcome this resistance. Honokiol, a compound with broad anticancer activity, has shown promise in this regard. This study aims to discover the effect of honokiol in increasing trastuzumab sensitivity in HER2+ trastuzumab-resistant breast cancer cells HCC1954 and the underline mechanisms behind. A bioinformatics study performed to explore the most potential target hub gene for honokiol in HER2+ breast cancer. Honokiol, trastuzumab and combined treatment cytotoxicity activity was then evaluated in both parental HCC1954 and trastuzumab resistance (TR-HCC1954) cells using MTT assay. The expression levels of these hub genes were then analyzed using qRT-PCR and those that could not be analyzed were subjected to molecular docking to determine their potential. Honokiol showed a potent cytotoxicity activity with an IC₅₀ of 41.05 μM and 69.61 μM in parental HCC1954 and TR-HCC1954 cell line respectively. Furthermore, the combination of honokiol and trastuzumab resulted in significant differences in cytotoxicity in TR-HCC1954 cells at specific concentrations. Molecular docking and the qRT-PCR showed that the potential ERα identified from the bioinformatics analysis was affected by the treatment. Our results show that honokiol has the potential to increase the sensitivity of trastuzumab in HER2+ trastuzumab resistant breast cancer cell line HCC1954 by affecting regulating estrogen receptor signaling. Further research is necessary to validate these findings.

Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.