Computational Biology and Chemistry最新文献

Bladder cancer (BC) is the 10th most common tumour with a high incidence and recurrence rate worldwide; however, the current therapies present limitations as, regularly, not all patients benefit from treatment. Therefore, the search for new, active marine phenolic acids with anti-tumour properties is imperative. In this study, we subjected marine phenolic acids to in silico investigations such as network pharmacology, molecular docking, and molecular dynamics simulation (MD) to identify a plausible pathway and the lead compound that inhibits BC. According to the network pharmacology analysis, eight hub genes (PLAU, MMP2, ITGB3, MAPK1, PTPN11, ESR1, TLR4, MMP9) were found and linked to the enrichment of hsa05205: proteoglycans in cancer, and four hub genes (MMP1, MMP2, MAPK1, MMP9) were involved in the enrichment of hsa05219: BC. Subsequently, molecular docking studies showed that the marine phenolic acids exhibit a strong binding affinity for the target protein, matrix metalloproteinase-9 (MPP9). Among these 14 marine phenolic acids, chicoric acid showed the highest binding affinity of −67.1445 kcal/mol and formed hydrogen bonds with the residues of Ala189, Gln227, Leu188, His226, Ala242, Arg249, Ala191, and Gly186 in the active site of the MPP9 protein. Then, molecular dynamics simulation revealed that chicoric acid formed a stable protein-ligand complex with RMSD and RMSF values of 0.72 nm and 0.53 nm, respectively. Furthermore, the PCA method was employed to understand the dynamical behaviour in the conformational space of MPP9 protein bound to chicoric acid, and the results showed the good conformational space behaviour of MPP9 protein. Moreover, chicoric acid showed a free binding energy value of −32.62 kcal/mol, which indicated it could be a BC inhibitor. Overall, chicoric acid demonstrated potential anti-BC activity through MPP9 protein inhibition.

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q² _CV = 0.813, and Q² _EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC₅₀ values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC₅₀ = 3.54, Binding energy = −8.86 kcal/mol), bergenin (pIC₅₀ = 2.69, Binding energy = −13.90 kcal/mol), and DB07272 (pIC₅₀ = 3.97, Binding energy = −25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Accumulation of senescent cells is a recognized feature in hepatocellular carcinoma (HCC), but their specific types and prognostic implications remain under investigation. This study aimed to delineate senescent cell types and their senescent patterns in HCC using publicly available bulk and single-cell mRNA sequencing data. Through gene expression and gene set enrichment analysis, we identified distinct senescent patterns within HCC samples. Notably, unconventional T cells, specifically natural killer T cells and γδT cells, were found to be the predominant senescent cell types. These cells exhibited enriched pathways related to DNA damage, senescence and the negative regulation of lymphocyte activation. Furthermore, we observed upregulation of the mTOR signaling pathway, which correlated positively with the expression of senescence-associated genes. This suggests a potential regulatory role for mTOR in the senescence of HCC. Strikingly, patients with elevated expression of senescence markers, including p16^INK4A, p21, and GLB1, demonstrated significantly reduced overall survival rates. Our findings indicate that immunosenescence in unconventional T cells may play a role in HCC progression. The potential therapeutic implications of targeting the mTOR pathway or eliminating senescent unconventional T cells warrant further exploration to improve HCC patient outcomes.

Alzheimer’s disease (AD) is characterized by neuronal loss due to hyperphosphorylated proteins induced by oxidative stress. AD remains a formidable challenge in the medical field, as current treatments focusing on single biomarkers have yielded limited success. Hence, there’s a burgeoning interest in investigating novel compounds that can target mechanisms, offering alternative therapeutic approaches. The aim of this study is to investigate the effects of allocryptopine, an isoquinoline alkaloid, on mechanisms related to AD in order to develop alternative treatment strategies. In this study, the in vitro AD cell model was obtained by inducing nerve growth factor (NGF)-differentiated PC12 (dPC12) cells to oxidative stress with H₂O₂, and also the effect mechanism of different allocryptopine concentrations on the in vitro AD cell model was studied. The treatments’ antioxidative effects at the ROS level and their regulation of the cell cycle were assessed through flow cytometry, while their anti-apoptotic effects were evaluated using both flow cytometry and qRT-PCR. Additionally, the phosphorylation levels of Akt, GSK-3β, and tau proteins were analyzed via western blot, and the interactions between Akt, GSK-3β, CDK5 proteins, and allocryptopine were demonstrated through molecular docking. Our study’s conclusive results revealed that allocryptopine effectively suppressed intracellular ROS levels, while simultaneously enhancing the Akt/GSK-3β signaling pathway by increasing p-Akt and p-GSK-3β proteins. This mechanism played a critical role in inhibiting neural cell apoptosis and preventing tau hyperphosphorylation. Moreover, allocryptopine demonstrated its ability to regulate the G1/S cell cycle progression, leading to cell cycle arrest in the G1 phase, and facilitating cellular repair mechanisms, potentially contributing to the suppression of neural apoptosis. The in silico results of allocryptopine were shown to docking with the cyclin-dependent kinase 5 (CDK 5) playing a role in tau phosphorylation Akt and GSK-3β from target proteins. Therefore, the in silico study results supported the in vitro results. The results showed that allocryptopine can protect dPC12 cells from oxidative stress-induced apoptosis and hyperphosphorylation of the tau protein by regulating the Akt/GSK-3β signaling pathway. Based on these findings, it can be suggested that allocryptopine, with its ability to target biomarkers and its significant effects on AD-associated mechanisms, holds promise as a potential candidate for drug development in the treatment of AD. Further research and clinical trials are recommended in the future.

Anticancer peptides(ACPs) have attracted significant interest as a novel method of treating cancer due to their ability to selectively kill cancer cells without damaging normal cells. Many artificial intelligence-based methods have demonstrated impressive performance in predicting ACPs. Nevertheless, the limitations of existing methods in feature engineering include handcrafted features driven by prior knowledge, insufficient feature extraction, and inefficient feature fusion. In this study, we propose a model based on a pretrained model, and dual-channel attentional feature fusion(DAFF), called ACP-PDAFF. Firstly, to reduce the heavy dependence on expert knowledge-based handcrafted features, binary profile features (BPF) and physicochemical properties features(PCPF) are used as inputs to the transformer model. Secondly, aimed at learning more diverse feature informations of ACPs, a pretrained model ProtBert is utilized. Thirdly, for better fusion of different feature channels, DAFF is employed. Finally, to evaluate the performance of the model, we compare it with other methods on five benchmark datasets, including ACP-Mixed-80 dataset, Main and Alternate datasets of AntiCP 2.0, LEE and Independet dataset, and ACPred-Fuse dataset. And the accuracies obtained by ACP-PDAFF are 0.86, 0.80, 0.94, 0.97 and 0.95 on five datasets, respectively, higher than existing methods by 1% to 12%. Therefore, by learning rich feature informations and effectively fusing different feature channels, ACD-PDAFF achieves outstanding performance. Our code and the datasets are available at https://github.com/wongsing/ACP-PDAFF.

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q² = 0.521, R² = 0.933, R²_prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f ($-$35.1 to $-$44.55 kcal/mol) showed higher binding free energy than that of compound 14 ($-$33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors.

Long non-coding RNAs (lncRNAs) play crucial roles in the regulation of gene expression and maintenance of genomic integrity through various interactions with DNA, RNA, and proteins. The availability of large-scale sequence data from various high-throughput platforms has opened possibilities to identify, predict, and functionally annotate lncRNAs. As a result, there is a growing demand for an integrative computational framework capable of identifying known lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding), a user-friendly, integrative, open-source, scalable, and modular computational framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC employs six stringent filtration steps to identify novel lncRNAs, performs differential expression analysis of mRNA and lncRNA transcripts, and predicts regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked ETENLNC against six existing tools and optimized it for desktop workstations and high-performance computing environments using data from three different species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.

COVID-19, caused by the SARS-COV-2 virus, induces numerous immunological reactions linked to the severity of the clinical condition of those infected. The surface Spike protein (S protein) present in Sars-CoV-2 is responsible for the infection of host cells. This protein presents a high rate of mutations, which can increase virus transmissibility, infectivity, and immune evasion. Therefore, we propose to evaluate, using immunoinformatic techniques, the predicted epitopes for the S protein of seven variants of Sars-CoV-2. MHC class I and II epitopes were predicted and further assessed for their immunogenicity, interferon-gamma (IFN-γ) inducing capacity, and antigenicity. For B cells, linear and structural epitopes were predicted. For class I MHC epitopes, 40 epitopes were found for the clades of Wuhan, Clade 2, Clade 3, and 20AEU.1, Gamma, and Delta, in addition to 38 epitopes for Alpha and 44 for Omicron. For MHC II, there were differentially predicted epitopes for all variants and eight equally predicted epitopes. These were evaluated for differences in the MHC II alleles to which they would bind. Regarding B cell epitopes, 16 were found in the Wuhan variant, 14 in 22AEU.1 and in Clade 3, 15 in Clade 2, 11 in Alpha and Delta, 13 in Gamma, and 9 in Omicron. When compared, there was a reduction in the number of predicted epitopes concerning the Spike protein, mainly in the Delta and Omicron variants. These findings corroborate the need for updates seen today in bivalent mRNA vaccines against COVID-19 to promote a targeted immune response to the main circulating variant, Omicron, leading to more robust protection against this virus and avoiding cases of reinfection. When analyzing the specific epitopes for the RBD region of the spike protein, the Omicron variant did not present a B lymphocyte epitope from position 390, whereas the epitope at position 493 for MHC was predicted only for the Alpha, Gamma, and Omicron variants.

The bioinformatic analysis of cannabinoid receptors (CBRs) CB₁ and CB₂ reveals a detailed picture of their structure, evolution, and physiological significance within the endocannabinoid system (ECS). The study highlights the evolutionary conservation of these receptors evidenced by sequence alignments across diverse species including humans, amphibians, and fish. Both CBRs share a structural hallmark of seven transmembrane (TM) helices, characteristic of class A G-protein-coupled receptors (GPCRs), which are critical for their signalling functions. The study reports a similarity of 44.58 % between both CBR sequences, which suggests that while their evolutionary paths and physiological roles may differ, there is considerable conservation in their structures. Pathway databases like KEGG, Reactome, and WikiPathways were employed to determine the involvement of the receptors in various signalling pathways. The pathway analyses integrated within this study offer a detailed view of the CBRs interactions within a complex network of cannabinoid-related signalling pathways. High-resolution crystal structures (PDB ID: 5U09 for CB₁ and 5ZTY for CB₂) provided accurate structural information, showing the binding pocket volume and surface area of the receptors, essential for ligand interaction. The comparison between these receptors' natural sequences and their engineered pseudo-CBRs (p-CBRs) showed a high degree of sequence identity, confirming the validity of using p-CBRs in receptor-ligand interaction studies. This comprehensive analysis enhances the understanding of the structural and functional dynamics of cannabinoid receptors, highlighting their physiological roles and their potential as therapeutic targets within the ECS.