Computational Biology and Chemistry最新文献

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model’s execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

Coronary artery disease poses a significant threat to human health. In clinical settings, coronary angiography remains the gold standard for diagnosing coronary heart disease. A crucial aspect of this diagnosis involves detecting arterial narrowings. Categorizing these narrowings can provide insight into whether patients should receive vascular revascularization treatment. The majority of current deep learning methods for analyzing coronary angiography are mostly confined to the theoretical research domain, with limited studies offering direct practical support to clinical practitioners. This paper proposes an integrated deep-learning model for the localization and classification of narrowings in coronary angiography images. The experimentation employed 1606 coronary angiography images obtained from 132 patients, resulting in an accuracy of 88.9 %, a recall rate of 85.4 %, an F1 score of 0.871, and a MAP value of 0.875 for vascular stenosis detection. Furthermore, we developed the "Hemadostenosis" web platform (http://bioinfor.imu.edu.cn/hemadostenosis) using Django, a highly mature HTTP framework. Users are able to submit coronary angiography image data for assessment via a visual interface. Subsequently, the system sends the images to a trained convolutional neural network model to localize and categorize the narrowings. Finally, the visualized outcomes are displayed to users and are downloadable. Our proposed approach pioneers the recognition and categorization of arterial narrowings in vascular angiography, offering practical support to clinical practitioners in their learning and diagnostic processes.

Background

The etiology of intervertebral disc degeneration (IVDD), a prevalent degenerative disease in the elderly, remains to be fully elucidated. The objective of this study was to identify immune infiltration and oxidative stress (OS) biomarkers in IVDD, aiming to provide further insights into the intricate pathogenesis of IVDD.

Methods

The Gene Expression microarrays were obtained from the Gene Expression Omnibus (GEO) database. We conducted enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Subsequently, the R language packages CIBERSORT, MCPcounter, and WGCNA were employed to compare immune infiltration levels between IVDD samples and control samples. A protein-protein interaction (PPI) network was constructed using the Search Tools for the Retrieval of Interacting Genes (STRING) database to identify significant gene clusters. To identify hub genes, we employed Cytoscape's Molecular Complex Detection (MCODE) plug-in. The mRNA levels of hub genes in the cell model were validated by qPCR, while Western blotting was used to validate their protein levels.

Results

The GSE70362 dataset from the GEO database identified a total of 1799 genes that were differentially expressed. Among these, 43 genes were found to be differentially expressed and also associated with OS. The differentially expressed genes associated with OS and the immune-related module genes identified through WGCNA were further intersected, resulting in the identification of 10 key genes that were differentially expressed and played crucial roles in both immune response and OS. Subsequently, we validated four diagnostic markers (PPIA, MAP3K5, PXN, and JAK2) using the GSE122429 external dataset. In a cellular model of OS in NP cells, we have identified the upregulation of PPIA and PXN genes, which could serve as novel markers for IVDD.

Conclusion

The study successfully identified and validated differentially expressed genes associated with oxidative stress and immune infiltration in IVDD samples compared to normal ones. Notably, the newly discovered biomarkers PPIA and PXN have not been previously reported in IVDD-related research.

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Colorectal cancer (CRC) poses a significant global health challenge, characterized by substantial prevalence variations across regions. This study delves into the therapeutic potential of rutin, a polyphenol abundant in fruits, for treating CRC. The primary objectives encompass identifying molecular targets and pathways influenced by rutin through an integrated approach combining bioinformatic analysis and experimental validation. Employing Gene Set Enrichment Analysis (GSEA), the study focused on identifying potential differentially expressed genes (DEGs) associated with CRC, specifically those involved in regulating reactive oxygen species, metabolic reprogramming, cell cycle regulation, and apoptosis. Utilizing diverse databases such as GEO2R, CTD, and Gene Cards, the investigation revealed a set of 16 targets. A pharmacological network analysis was subsequently conducted using STITCH and Cytoscape, pinpointing six highly upregulated genes within the rutin network, including TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Gene Ontology (GO) analysis predicted functional categories, shedding light on rutin's potential impact on antioxidant properties. KEGG pathway analysis enriched crucial pathways like metabolic and ROS signaling pathways, HIF1a, and mTOR signaling. Diagnostic assessments were performed using UALCAN and GEPIA databases, evaluating mRNA expression levels and overall survival for the identified targets. Molecular docking studies confirmed robust binding associations between rutin and biomolecules such as TP53, PCNA, CDK4, CCNEB1, CDKN1A, and LDHA. Experimental validation included inhibiting colorectal cell HT-29 growth and promoting cell growth with NAC through MTT assay. Flow cytometric analysis also observed rutin-induced G1 phase arrest and cell death in HT-29 cells. RT-PCR demonstrated reduced expression levels of target biomolecules in HT-29 cells treated with rutin. This comprehensive study underscores rutin's potential as a promising therapeutic avenue for CRC, combining computational insights with robust experimental evidence to provide a holistic understanding of its efficacy.

In this study, the potential of borophene (BOR) as a drug delivery system for resveratrol (RVT) was explored to evaluate its efficacy in cancer treatment. The excited, electronic, and geometric states of RVT, BOR, and the borophene-adsorbed resveratrol complex (BOR@RVT) were calculated to assess BOR's suitability as a drug carrier. Noncovalent interaction (NCI) plots indicated a weak force of attraction between BOR and RVT, which facilitates the offloading of RVT at the target site. Frontier molecular orbital (FMO) analysis showed that during electron excitation from Highest Occupied Molecular Orbital (HOMO) to Lowest Unoccupied Molecular Orbital (LUMO), charge transfer occurs from RVT to BOR. This was further confirmed by charge decomposition analysis (CDA). Calculations for the excited state of BOR@RVT revealed a red shift in the maximum absorption wavelength (λmax), indicating a photoinduced electron transfer (PET) process across various excited states. PET analysis demonstrated fluorescence quenching due to this interaction. Our findings suggest that BOR holds significant potential as a drug delivery vehicle for cancer treatment, offering a promising platform for the development of advanced drug delivery systems.

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug – Cell line pairs. The proposed method yielded higher Pearson’s correlation coefficient ($r_p$) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.

Metisa plana is a widespread insect pest infesting oil palm plantations in Malaysia. Farnesyl acetate (FA), a juvenile hormone analogue, has been reported to exert in vitro and in vivo insecticidal activity against other insect pests. However, the insecticidal mechanism of FA on M. plana remains unclear. Therefore, this study aims to elucidate responsive genes in M. plana in response to FA treatment. The RNA-sequencing reads of FA-treated M. plana were de novo-assembled with existing raw reads from non-treated third instar larvae, and 55,807 transcripts were functionally annotated to multiple protein databases. Several insecticide detoxification-related genes were differentially regulated among the 321 differentially expressed transcripts. Cytochrome P450 monooxygenase, carboxylesterase, and ATP-binding cassette protein were upregulated, while peptidoglycan recognition protein was downregulated. Innate immune response genes, such as glutathione S-transferases, acetylcholinesterase, and heat shock protein, were also identified in the transcriptome. The findings signify that changes occurred in the insect’s receptor and signaling, metabolic detoxification of insecticides, and immune responses upon FA treatment on M. plana. This valuable information on FA toxicity may be used to formulate more effective biorational insecticides for better M. plana pest management strategies in oil palm plantations.