Computational biology and chemistry最新文献

Classification and prediction of variants associated with hearing loss using sequence information in the vicinity of mutation sites. 利用突变位点附近的序列信息，对与听力损失相关的变异进行分类和预测。

Computational biology and chemistry Pub Date : 2024-12-14 DOI: 10.1016/j.compbiolchem.2024.108321

Xiao Liu, Li Teng, Jing Sun

{"title":"Classification and prediction of variants associated with hearing loss using sequence information in the vicinity of mutation sites.","authors":"Xiao Liu, Li Teng, Jing Sun","doi":"10.1016/j.compbiolchem.2024.108321","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108321","url":null,"abstract":"Hearing impairment is a major global health problem, affecting more than 5 % of the world's population at various ages, from neonates to the elderly. Among the common genetic variations in humans, single nucleotide variations and small insertions or deletions predominate. The study of hearing loss resulting from these variations is proving invaluable in the analysis and diagnosis of hearing disorders. The identification of pathogenic mutations is frequently a lengthy and laborious process. Existing computational prediction tools have been developed primarily for common diseases and genome-wide analyses, with less focus on deafness. This study proposes a novel approach that focuses on the regions surrounding mutation sites. Mutation sites associated with deafness and their flanking regions of different lengths were extracted from relevant databases and combined into seven distinct segments of different lengths. The information-theoretic features of these segments were computed. Five machine learning algorithms were then used for training, resulting in the construction of a model capable of classifying and predicting deafness-related mutations. For fragments encompassing the 250 bp regions upstream and downstream of the mutations, the average AUC of the five classifiers on the independent test set is 0.89 and the average ACC is 0.85, indicating that the model has a high recognition rate of the pathogenic deafness mutation site. An ensemble approach was also applied to predict variants of uncertain significance (VUS) that may be associated with deafness. These variants were then scored and ranked to assess their likelihood of contributing to the condition.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108321"},"PeriodicalIF":0.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a centrosome amplification-associated signature in kidney renal clear cell carcinoma based on multiple machine learning models.

Computational biology and chemistry Pub Date : 2024-12-12 DOI: 10.1016/j.compbiolchem.2024.108317

Zhen Song, Chunlei Xue, Hui Wang, Lijian Gao, Haibin Song, Yuanyuan Yang

{"title":"Development of a centrosome amplification-associated signature in kidney renal clear cell carcinoma based on multiple machine learning models.","authors":"Zhen Song, Chunlei Xue, Hui Wang, Lijian Gao, Haibin Song, Yuanyuan Yang","doi":"10.1016/j.compbiolchem.2024.108317","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108317","url":null,"abstract":"Background: Centrosome amplification (CA) has been shown to be capable of initiating tumorigenesis with metastatic potential and enhancing cell invasion. We were interested in discovering how centrosome amplification-associated signature affects the prediction of prognosis and response to therapy in kidney renal clear cell carcinoma (KIRC).Methods and materials: The TCGA-KIRC dataset was used to construct a centrosome amplification-associated signature using the random survival forest analysis and Cox regression analysis, and the ICGC and GEO datasets were employed for signature validation. Mutation and immune landscapes were outlined and the response to immunotherapy was evaluated. The expression of the screened hub gene was profiled by analyzing single-cell RNA sequencing from GSE159115.Results: In the TCGA-KIRC cohort, 22 centrosome amplification-associated prognostic genes were discovered. According to the optimal consistency index (0.91), the random survival forest algorithm was selected to determine 7 hub prognostic genes, which were used to construct a centrosome amplification-associated prognostic index (CAAPI). It was discovered that it is connected to high mortality rates, high mutation rates, immunosuppressive cell infiltration, and immune dysfunction. For patients in the high CAAPI group, immunotherapy was not as effective. Single-cell RNA sequencing revealed a high expression of CDK5RAP3 in the tumor cells.Conclusion: Centrosome amplification played a significant role in regulating tumor microenvironment and responding to immunotherapy, emphasizing its crucial importance in the development and treatment of KIRC. Patients with KIRC may benefit from using CAAPI as a biomarker to predict individual prognosis and assess a response to immunotherapy.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108317"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In-silico identification and validation of Silibinin as a dual inhibitor for ENO1 and GLUT4 to curtail EMT signaling and TNBC progression.

Computational biology and chemistry Pub Date : 2024-12-12 DOI: 10.1016/j.compbiolchem.2024.108312

Dheepika Venkatesh, Shilpi Sarkar, Thirukumaran Kandasamy, Siddhartha Sankar Ghosh

{"title":"In-silico identification and validation of Silibinin as a dual inhibitor for ENO1 and GLUT4 to curtail EMT signaling and TNBC progression.","authors":"Dheepika Venkatesh, Shilpi Sarkar, Thirukumaran Kandasamy, Siddhartha Sankar Ghosh","doi":"10.1016/j.compbiolchem.2024.108312","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108312","url":null,"abstract":"The aberrant metabolic reprogramming endows TNBC cells with sufficient ATP and lactate required for survival and metastasis. Hence, the intervention of the metabolic network represents a promising avenue to alleviate the Warburg effect in TNBC cells to impair their invasive and metastatic potential. Multitudinous in-silico analysis identified Enolase1 (ENO1) and the surface transporter protein, GLUT4 to be the potential targets for the abrogation of the metabolic network. The expression profiles of ENO1 and GLUT4 genes showed anomalous expression in various cancers, including breast cancer. Subsequently, the functional and physiological interactions of the target proteins were analyzed from the protein-protein interaction network. The pathway enrichment analysis identified the prime cancer signaling pathways in which these proteins are involved. Further, docking results bestowed Silibinin as the concurrent inhibitor of ENO1 and GLUT4. Moreover, the stable interaction of Silibinin with both proteins deciphered the binding free energies values of -48.86 and -104.31 KJ/mol from MMPBSA analysis and MD simulation, respectively. Furthermore, the cell viability, ROS assay, and live-dead imaging underscored the pronounced cytotoxicity of Silibinin, illuminating its capacity to incur apoptosis within TNBC cells. Additionally, glycolysis assay and gene expression analysis demonstrated the silibinin-mediated inhibition of the glycolysis pathway. Eventually, a lipidomic reprogramming towards fatty acid metabolism was established from the elevated lipid droplet accumulation, exogenous fatty acid uptake and de-novo lipogenesis. Nevertheless, repression of EMT and Wnt pathway progression by Silibinin was perceived from the gene expression studies. Overall, the current study highlights the tweaking of intricate signaling crosstalk between glycolysis and the Wnt pathway in TNBC cells through inhibiting ENO1 and GLUT4.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108312"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142848718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving binding affinity prediction by emphasizing local features of drug and protein.

Computational biology and chemistry Pub Date : 2024-12-11 DOI: 10.1016/j.compbiolchem.2024.108310

Daejin Choi, Sangjun Park

{"title":"Improving binding affinity prediction by emphasizing local features of drug and protein.","authors":"Daejin Choi, Sangjun Park","doi":"10.1016/j.compbiolchem.2024.108310","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108310","url":null,"abstract":"Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite much effort to improve accuracy of binding affinity prediction, the prior work considered only macro-level features that can represent the characteristics of the whole architecture of a drug and a target protein, and the features from local structure of the drug and the protein tend to be lost. In this paper, we propose a deep learning model that can comprehensively extract the local features of both a drug and a target protein for accurate binding affinity prediction. The proposed model consists of two components named as Multi-Stream CNN and Multi-Stream GCN, each of which is responsible for capturing micro-level characteristics or local features from subsequences of a target protein sequence and subgraph of a drug molecule, respectively. Having multiple streams consisting of different numbers of layers, both the components can compute and preserve the local features with a stream consisting of a single layer. Our evaluation with two popular datasets, Davis and KIBA, demonstrates that the proposed model outperforms all the baseline models using the global features, implying that local features play significant roles of binding affinity prediction.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108310"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring immune gene expression and potential regulatory mechanisms in anaplastic thyroid carcinoma using a combination of single-cell and bulk RNA sequencing data. 结合单细胞和大容量 RNA 测序数据探索甲状腺无节细胞癌的免疫基因表达和潜在调控机制

Computational biology and chemistry Pub Date : 2024-12-07 DOI: 10.1016/j.compbiolchem.2024.108311

Kehui Zhou, Shijia Zhang, Jinbiao Shang, Xiabin Lan

{"title":"Exploring immune gene expression and potential regulatory mechanisms in anaplastic thyroid carcinoma using a combination of single-cell and bulk RNA sequencing data.","authors":"Kehui Zhou, Shijia Zhang, Jinbiao Shang, Xiabin Lan","doi":"10.1016/j.compbiolchem.2024.108311","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108311","url":null,"abstract":"Thyroid cancer includes papillary thyroid carcinoma (PTC) and anaplastic thyroid carcinoma (ATC). While PTC has an excellent prognosis, ATC has a dismal prognosis, necessitating the identification of novel targets in ATC to aid in ATC diagnosis and treatment. Therefore, we analyzed ATC single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data from the Gene Expression Omnibus (GEO), retrieved immune-related genes from the ImmPort database, and identified differentially expressed immune genes within single-cell subgroups. The AUCell package in R was used to calculate activity scores for single-cell subgroups and identify active cell populations. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on differentially expressed genes (DEGs) in active cell populations. Then, we integrated thyroid-cancer scRNA-seq and bulk RNA-seq data to identify overlapping DEGs. Relevant transcription factors (TFs) were retrieved from the TRRUST database. A protein-protein interaction (PPI) network for key TFs was created using the STRING database. Simultaneously, drugs associated with key TFs were obtained from DGIdb. ScRNA-seq cluster analysis showed that T/natural killer (NK) cells were more distributed in ATC and that thyrocytes cells were more distributed in PTC. We obtained 264 differential immune genes (DIGs) from the IMMPORT database and integrated scRNA-seq cluster analysis to identify the active cell T/NK cells and myeloid cells. Integrated bulk RNA-seq analysis obtained common immune genes (CIGs) such as TMSB4X, NFKB1, TNFRSF1B, and B2M. The nine CIG-related TFs (CEBPB, SPI1, NFKB1, RUNX1, NFE2L2, REL, CIITA, KLF6, and CEBPD) in myeloid cells and three TFs (NFKB1, FOXO1, and NR3C1) in T/NK cells were obtained from the TRRUST database. The key genes we identified represent potential targets for treating ATC.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108311"},"PeriodicalIF":0.0,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A knowledge graph approach to drug repurposing for Alzheimer's, Parkinson's and Glioma using drug-disease-gene associations.

Computational biology and chemistry Pub Date : 2024-12-05 DOI: 10.1016/j.compbiolchem.2024.108302

Ruchira Selote, Richa Makhijani

{"title":"A knowledge graph approach to drug repurposing for Alzheimer's, Parkinson's and Glioma using drug-disease-gene associations.","authors":"Ruchira Selote, Richa Makhijani","doi":"10.1016/j.compbiolchem.2024.108302","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108302","url":null,"abstract":"Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network. In our research, we aimed at finding drug candidates which can be repurposed under neurodegenerative diseases and glioma. We collected association data between drugs, diseases and genes from public resources and primarily examined the data related to Alzheimer's, Parkinson's and Glioma diseases. We created a Knowledge Graph using neo4j by integrating all these datasets and applied scalable feature learning algorithm known as node2vec to create node embeddings. These embeddings were later used to predict the unknown associations between disease and their candidate drugs by finding cosine similarity between disease and drug nodes embedding. We obtained a definitive set of candidate drugs for repurposing. These results were validated from the literature and CodReS online tool to rank the candidate drugs. Additionally, we verified the status of candidate drugs from pharmaceutical knowledge databases to confirm their significance.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108302"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, synthesis, structural characterization, cytotoxicity and computational studies of Usnic acid derivative as potential anti-breast cancer agent against MCF7 and T47D cell lines.

Computational biology and chemistry Pub Date : 2024-12-02 DOI: 10.1016/j.compbiolchem.2024.108303

Miah Roney, Kelvin Khai Voon Wong, Md Nazim Uddin, Kamal Rullah, Abdi Wira Septama, Lucia Dwi Antika, Mohd Fadhlizil Fasihi Mohd Aluwi

{"title":"Design, synthesis, structural characterization, cytotoxicity and computational studies of Usnic acid derivative as potential anti-breast cancer agent against MCF7 and T47D cell lines.","authors":"Miah Roney, Kelvin Khai Voon Wong, Md Nazim Uddin, Kamal Rullah, Abdi Wira Septama, Lucia Dwi Antika, Mohd Fadhlizil Fasihi Mohd Aluwi","doi":"10.1016/j.compbiolchem.2024.108303","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108303","url":null,"abstract":"Development of novel inhibitors is necessary to counteract the rising prevalence of breast cancer (BC) in women in recent years, as evidenced by the side-effect profiles of a few clinically approved inhibitors. In this study, the usnic acid derivative (UA1) was synthesized due to the effectiveness of usnic acid (UA) against BC cell line. Furthermore, the structure of synthesized compound was determined using FT-IR, 1H NMR, 13C NMR, HSQC, and HMBC spectroscopic techniques. The anticancer potential of UA1 was assessed using the MTT assay on two different cell lines of BC including MCF7 and T47D. To ascertain the binding affinity and stability of the docking complex, further procedures included the in silico molecular docking, molecular dynamic simulation, principal component analysis, and binding free energy experiments. The cytotoxicity results show that the UA1 exhibits strong antitumor activities and comparable effects against BC cell lines with the IC50 values of 9.21 µM for MCF7 cell and 14.8 µM for T47D cell, respectively, where the positive control cisplatin showed the IC50 values of 8.95 µM for MCF7 cell and 10.9 µM for T47D cell. Additionally, the molecular docking results of UA1 showed that it interacts strongly into the active site of target protein. Molecular dynamics simulation results also revealed that the docking complex was formed stability with the RMSD and RMSF values of 0.50 nm and 0.19 nm, respectively. According to the PCA analysis, the target protein displays good conformational space behaviour when bound with UA1. Furthermore, the UA1 showed the free binding energy value of -18.52 kcal/mol with the target protein, which indicating that UA1 may prevent BC.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108303"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning approaches for predicting craniofacial anomalies with graph neural networks.

Computational biology and chemistry Pub Date : 2024-12-02 DOI: 10.1016/j.compbiolchem.2024.108294

Colten Alme, Harun Pirim, Yusuf Akbulut

引用次数: 0

Editorial: The 21st Asian Pacific Bioinformatics Conference 2023. 社论：第 21 届亚太生物信息学大会 2023。

Computational biology and chemistry Pub Date : 2024-12-01 Epub Date: 2024-10-09 DOI: 10.1016/j.compbiolchem.2024.108233

Min Li, Yi-Ping Phoebe Chen

引用次数: 0

Evaluation of 1-vinyl-3-alkyl imidazolium-based ionic liquid monomers towards antibacterial activity: An in-silico & in-vitro study.

Computational biology and chemistry Pub Date : 2024-11-29 DOI: 10.1016/j.compbiolchem.2024.108288

Itishree Panda, Sangeeta Raut, Sangram Keshari Samal, Santosh Kumar Behera, Sanghamitra Pradhan

{"title":"Evaluation of 1-vinyl-3-alkyl imidazolium-based ionic liquid monomers towards antibacterial activity: An in-silico & in-vitro study.","authors":"Itishree Panda, Sangeeta Raut, Sangram Keshari Samal, Santosh Kumar Behera, Sanghamitra Pradhan","doi":"10.1016/j.compbiolchem.2024.108288","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2024.108288","url":null,"abstract":"In this study 1-vinyl-3-alkyl imidazolium-based ionic liquid monomers (ILs) with different alkyl chain lengths {R = hexyl (A), octyl (B) and decyl (C)} have been synthesized for antibacterial applications. The prepared ILs have been characterized using UV, FT-IR and NMR spectroscopy. The antibacterial activities of the synthesized ILs against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) have been examined by measuring their minimal inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs). The results exhibit that these ILs have admirable antibacterial activities with MIC values range from < 1.2 to 12.2 μM for S. aureus and < 2.4 to 12.2 μM for E. coli. A notable dependence of antibacterial and antibiofilm efficacy on the alkyl chain length (ILC> ILB > ILA) has been observed. From in-silico evaluation, the binding energies of β-lactamase protein of S. aureus (PDB ID: 1GHP) are found to be -4.4, -4.6, -4.7 kcal/mol for IL A, IL B, and IL C. For dihydrofolate reductase (DHFR) of S. aureus and E. coli the binding energies -4.6, -4.5, -5.3 kcal/mol and -5.3, -5.4, -5.6 kcal/mol have been noted for IL A, IL B, and IL C respectively. MD simulations (100 ns) have been performed to predict the stability and understand the binding mechanism of the docked complexes.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"115 ","pages":"108288"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0