Computational Biology and Chemistry最新文献_第5页

DMNAG: Prediction of disease-metabolite associations based on Neighborhood Aggregation Graph Transformer DMNAG：基于邻域聚集图转换器的疾病代谢物关联预测。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-28 DOI: 10.1016/j.compbiolchem.2024.108320

Pengli Lu, Jiajie Gao, Wenzhi Liu

{"title":"DMNAG: Prediction of disease-metabolite associations based on Neighborhood Aggregation Graph Transformer","authors":"Pengli Lu, Jiajie Gao, Wenzhi Liu","doi":"10.1016/j.compbiolchem.2024.108320","DOIUrl":"10.1016/j.compbiolchem.2024.108320","url":null,"abstract":"<div><div>The metabolic level within an organism typically reflects its health status. Studying the relationship between human diseases and metabolites helps enhance medical professionals’ ability for early disease diagnosis and risk prediction. However, traditional biological experimental methods often require substantial resources and manpower, and there is still room for improvement in the performance of existing predictive models. To tackle these, we propose a novel method based on the Neighborhood Aggregation Graph Transformer (NAGphormer) to predict potential associations between diseases and metabolites (DMNAG), aiming to provide guidance for biological experiments and improve experimental efficiency. First, we calculated the Gaussian kernel similarity of diseases and the physicochemical similarity of metabolites, and combined them with known associations to construct a bipartite heterogeneous network. We then calculated the semantic similarity of diseases and the Mol2vec similarity of metabolites, using them respectively as the similarity feature vectors for the disease nodes and metabolite nodes. Meanwhile, we calculate the positional information features of nodes and combine them with similarity features as the initial features of the nodes. Next, we input the bipartite heterogeneous network and node initial features into the Hop2Token module to capture multihop neighborhood information between nodes. Finally, we input the multi-hop features of nodes into the Transformer model for training and obtain the edge prediction probabilities through the decoder. Through experiments, our model achieved an AUC value of 0.9801 and an AUPR value of 0.9818 in five-fold cross-validation. In case studies, most DMNAG-predicted associations have been validated, showcasing the model’s reliability and superiority.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108320"},"PeriodicalIF":2.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana ANNInter：一个探索拟南芥ncRNA-ncRNA相互作用的平台。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-27 DOI: 10.1016/j.compbiolchem.2024.108328

AT Vivek , Namrata Sahu , Garima Kalakoti, Shailesh Kumar

{"title":"ANNInter: A platform to explore ncRNA-ncRNA interactome of Arabidopsis thaliana","authors":"AT Vivek , Namrata Sahu , Garima Kalakoti, Shailesh Kumar","doi":"10.1016/j.compbiolchem.2024.108328","DOIUrl":"10.1016/j.compbiolchem.2024.108328","url":null,"abstract":"<div><div>Eukaryotic transcriptomes are remarkably complex, encompassing not only protein-coding RNAs but also an expanding repertoire of noncoding RNAs (ncRNAs). In plants, ncRNA-ncRNA interactions (NNIs) have emerged as pivotal regulators of gene expression, orchestrating development and adaptive responses to stress. Despite their critical roles, the functional significance of NNIs remains poorly understood, largely due to a lack of comprehensive resources. Here, we present ANNInter, a comprehensive platform that integrates computational predictions with experimental datasets to systematically identify and analyze NNIs. The current version catalogs over 90,000 interactions spanning eight categories of sRNA-to-longer ncRNAs, each extensively annotated with interaction types, identification methods, and functional descriptions. The integrated schema and advanced visualization framework in ANNInter enable users to explore intricate interaction networks, providing system-wide insights into ncRNA-mediated regulation. These interaction data provide unparalleled opportunities to uncover the regulatory roles of NNIs in key biological processes such as growth regulation, stress adaptation, and cellular signaling. By providing an extensive, curated repository of computational and degradome-based interaction data, ANNInter will provide a platform to the study of ncRNA biology, elucidating the complex mechanisms of NNIs and supporting the concept of competing endogenous RNAs (ceRNAs) in gene regulation. The platform is freely accessible at <span><span>https://www.nipgr.ac.in/ANNInter/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108328"},"PeriodicalIF":2.6,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach 肺腺癌和鳞状细胞癌转录组的比较研究揭示潜在的候选生物标志物：一种可解释的人工智能方法。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-27 DOI: 10.1016/j.compbiolchem.2024.108333

Ankur Datta, George Priya Doss. C

{"title":"Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach","authors":"Ankur Datta, George Priya Doss. C","doi":"10.1016/j.compbiolchem.2024.108333","DOIUrl":"10.1016/j.compbiolchem.2024.108333","url":null,"abstract":"<div><div>Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC samples relative to adjacent healthy tissues using an explainable artificial intelligence (XAI) framework. The LASSO algorithm was employed to identify the top gene features in the LUAD and LUSC datasets. An ensemble-based extreme gradient boosting (XGBoost) machine learning (ML) algorithm was trained and interpreted using SHapley Additive exPlanations (SHAP), with top features undergoing biological annotation through survival and functional enrichment analyses. The XAI-based SHAP module addresses the opaque nature of ML models. Notably, 35 and 33 genes were identified for LUAD and LUSC, respectively, using the LASSO algorithm. Performance metrics such as average accuracy and Matthew’s correlation coefficient were evaluated. The XGBoost model demonstrated an average accuracy of 99.1 % for LUAD and 98.6 % for LUSC. The <em>SFTPC</em> gene emerged as the most significant feature across both NSCLC subtypes. For LUAD, genes such as <em>STX11</em>, <em>CLEC3B</em>, <em>EMP2</em>, and <em>LYVE1</em> significantly influenced the XAI-SHAP framework. Conversely, <em>GKN2</em>, <em>OGN</em>, <em>SLC39A8</em>, and <em>MMRN1</em> were identified for LUSC. Survival analysis and functional validation of these genes highlighted the physiological functions observed to be dysregulated in the NSCLC subtypes. These identified genes have the potential to enhance current medical diagnostics and therapeutics.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108333"},"PeriodicalIF":2.6,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Serum levels of PSA and VEGF2 as the prognosis markers for bone metastasis of prostate cancer: A retrospective study 血清PSA和VEGF2水平作为前列腺癌骨转移预后指标的回顾性研究

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-27 DOI: 10.1016/j.compbiolchem.2024.108330

Lu Zhang , Jianxin Ying , Jian Ke , Likun Ma , Yamin Zhou

{"title":"Serum levels of PSA and VEGF2 as the prognosis markers for bone metastasis of prostate cancer: A retrospective study","authors":"Lu Zhang , Jianxin Ying , Jian Ke , Likun Ma , Yamin Zhou","doi":"10.1016/j.compbiolchem.2024.108330","DOIUrl":"10.1016/j.compbiolchem.2024.108330","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Prostate cancer (PCa) is the second most commonly diagnosed cancer in males, the mechanism of PCa with bone metastasis remains unclear. In this study, we aimed to utilize a retrospective clinical study to evaluate the diagnostic value of bone metastases from PCa and provide reference values for future applications.</div></div><div><h3>Methods</h3><div>We retrospectively collected a total of 200 samples including 100 PCa patients with bone metastatic and 100 without from June 2019 to August 2021. Transrectal ultrasonography (TRUS) was applied for observing the microvascular blood flow in the lesion. The serum levels of prostate specific antigen (PSA), vascular endothelial growth factor 2 (VEGF2), interleukin-6 (IL-6) and Pro-gastrin-releasing peptide (ProGRP) was determined using Enzyme-linked immunosorbent assay Kit. Regression model was constructed to analyze the risk factors for PCa with bone metastasis, the prognosis value of which was evaluated using receiver operating characteristic (ROC) curves. Ultimately, dataset GSE32269 was employed for validation.</div></div><div><h3>Results</h3><div>The focal blood perfusion was significantly improved in patients with bone metastasis than those without (<em>P</em> < 0.01). The examination results indicated that PCa patients with bone metastasis had higher levels of PSA, VEGF2, IL-6 and ProGRP than non-bone metastasis (P < 0.01). Moreover, the regression analysis indicated that the four cytokines were the risk factors for bone metastasis, and the ROC curves further confirmed that PSA and VEGF2 had high value of prediction value for bone metastasis with AUC of 0.901 and 0.8519.</div></div><div><h3>Conclusion</h3><div>The expression of PSA and VEGF2 in serum had high prognosis value for bone metastasis in PCa patients.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108330"},"PeriodicalIF":2.6,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Selectivity mechanism of inhibition towards Phosphodiesterase 1B and phosphodiesterase 10A in silico investigation 磷酸二酯酶1B和磷酸二酯酶10A抑制的选择性机制研究。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-26 DOI: 10.1016/j.compbiolchem.2024.108322

Jianheng Li, Pengfei Song, Hanxun Wang, Wenxiong Lian, Jiabo Li, Zhijian Wang, Yaming Zhang, Qingkui Cai, Huali Yang, Maosheng Cheng

{"title":"Selectivity mechanism of inhibition towards Phosphodiesterase 1B and phosphodiesterase 10A in silico investigation","authors":"Jianheng Li, Pengfei Song, Hanxun Wang, Wenxiong Lian, Jiabo Li, Zhijian Wang, Yaming Zhang, Qingkui Cai, Huali Yang, Maosheng Cheng","doi":"10.1016/j.compbiolchem.2024.108322","DOIUrl":"10.1016/j.compbiolchem.2024.108322","url":null,"abstract":"<div><div>Due to the unclear selectivity of the protein system, designing selective small molecule inhibitors has been a significant challenge. This issue is particularly prominent in the phosphodiesterases (PDEs) system. Phosphodiesterase 1B (PDE1B) and phosphodiesterase 10 A (PDE10A) are two closely related subtypes of PDE proteins that play diverse roles in the immune system and tumorigenesis, respectively. Distinguishing the selective mechanism of these two subtypes is crucial for maximizing therapeutic efficacy and minimizing the side effects of inhibitors. We have investigated the interactions between crucial amino acid residues and selective inhibitors through several computer-aided drug design methods such as molecular docking, molecular dynamic simulation, MM/GBSA calculation, and alanine scanning mutagenesis revealing the selective inhibition mechanism between PDE1B and PDE10A. Our finding shows the selective residues of PDE1B are His373 and Gln421, while the selective residues for PDE10A are Tyr683 and Phe719. Specifically, PDE10A inhibitors form hydrogen bonds and hydrophobic interactions with Tyr683 and Phe719, whereas PDE1B inhibitors only demonstrate weak hydrophobic interactions in the corresponding region. Overall, elucidating the selectivity mechanism underlying the differential interaction between PDE1B and PDE10A is crucial for designing inhibitors with distinct selectivity towards PDE1B/10 A.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108322"},"PeriodicalIF":2.6,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142960257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drug repositioning in castration-resistant prostate cancer using systems biology and computational drug design techniques 使用系统生物学和计算药物设计技术在去势抵抗性前列腺癌中的药物重新定位。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-25 DOI: 10.1016/j.compbiolchem.2024.108329

Javad Rafiee , Khadijeh Jamialahmadi , Mohammad Javad Bazyari , Seyed Hamid Aghaee-Bakhtiari

{"title":"Drug repositioning in castration-resistant prostate cancer using systems biology and computational drug design techniques","authors":"Javad Rafiee , Khadijeh Jamialahmadi , Mohammad Javad Bazyari , Seyed Hamid Aghaee-Bakhtiari","doi":"10.1016/j.compbiolchem.2024.108329","DOIUrl":"10.1016/j.compbiolchem.2024.108329","url":null,"abstract":"<div><h3>Background and objective</h3><div>Castration-resistant prostate cancer (CRPC) is caused by resistance to androgen deprivation treatment and leads to the death of patients and there is almost no chance of survival. Therefore, finding a cure to overcome CRPC is challenging and important, but discovering a new drug is very time-consuming and expensive. To overcome these problems, we used Drug repositioning (drug repurposing) strategy in this study.</div></div><div><h3>Methods</h3><div>Gene expression data of CRPC and primary prostate samples were extracted from the GEO database to identify DEGs. Pathway enrichment was performed to find the role of DEGs in signaling pathways. To identify hub proteins, the PPI network was reconstructed and analyzed. drug candidates were identified and to select the most effective drug, molecular docking analysis, and molecular dynamics simulation were performed. Then MTT and qRT-PCR tests were performed to check the effectiveness of the selected drug.</div></div><div><h3>Results</h3><div>A total of 152 upregulated DEGs and 343 downregulated DEGs were identified, and after PPI network analysis, IKBKB, SNAP23, MYC, and NOTCH1 genes were introduced as hubs. drug candidates for IKBKB were identified and by examining the results of docking screening and molecular dynamics, sulfasalazine was selected as the most effective drug. Laboratory analyses proved the effectiveness of this drug and a decrease in the expression of all target genes was observed.</div></div><div><h3>Conclusion</h3><div>In this study, IKBKB key protein were identified in CRPC, and sulfasalazine was selected as a suitable candidate for drug repositioning and its effectiveness was confirmed through tests.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108329"},"PeriodicalIF":2.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification and dissection of prostate cancer grounded on fatty acid metabolism-correlative features for predicting prognosis and assisting immunotherapy 基于脂肪酸代谢相关特征的前列腺癌鉴别和解剖预测预后和辅助免疫治疗。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-25 DOI: 10.1016/j.compbiolchem.2024.108323

Yongbo Zheng , Yueqiang Peng , Yingying Gao , Guo Yang , Yu Jiang , Gaojie Zhang , Linfeng Wang , Jiang Yu , Yong Huang , Ziling Wei , Jiayu Liu

{"title":"Identification and dissection of prostate cancer grounded on fatty acid metabolism-correlative features for predicting prognosis and assisting immunotherapy","authors":"Yongbo Zheng , Yueqiang Peng , Yingying Gao , Guo Yang , Yu Jiang , Gaojie Zhang , Linfeng Wang , Jiang Yu , Yong Huang , Ziling Wei , Jiayu Liu","doi":"10.1016/j.compbiolchem.2024.108323","DOIUrl":"10.1016/j.compbiolchem.2024.108323","url":null,"abstract":"<div><h3>Background</h3><div>Fatty acid metabolism (FAM) plays a critical role in tumor progression and therapeutic resistance by enhancing lipid biosynthesis, storage, and catabolism. Dysregulated FAM is a hallmark of prostate cancer (PCa), enabling cancer cells to adapt to extracellular signals and metabolic changes, with the tumor microenvironment (TME) playing a key role. However, the prognostic significance of FAM in PCa remains unexplored.</div></div><div><h3>Methods</h3><div>We analyzed 309 FAM-related genes to develop a prognostic model using least absolute shrinkage and selection operator (LASSO) regression based on The Cancer Genome Atlas (TCGA) database. This model stratified PCa patients into high- and low-risk groups and was validated using the Gene Expression Omnibus (GEO) database. We constructed a nomogram incorporating risk score, clinical variables (T and N stage, Gleason score, age), and assessed its performance with calibration curves. The associations between risk score, tumor mutation burden (TMB), immune checkpoint inhibitors (ICIs), and TME features were also examined. Finally, a hub gene was identified via protein-protein interaction (PPI) networks and validated.</div></div><div><h3>Results</h3><div>The risk score was an independent prognostic factor for PCa. High-risk patients showed worse survival outcomes but were more responsive to immunotherapy, chemotherapy, and targeted therapies. A core gene with high expression correlated with poor prognosis, unfavorable clinicopathological features, and immune cell infiltration.</div></div><div><h3>Conclusion</h3><div>These findings reveal the prognostic importance of FAM in PCa, providing novel insights into prognosis and potential therapeutic targets for PCa management.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108323"},"PeriodicalIF":2.6,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

e-QSAR (Explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray e-QSAR (explable AI-QSAR)，分子对接和ADMET分析结构多样的gsk3 - β调节剂，以识别被x射线证实的隐藏调制特征。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-24 DOI: 10.1016/j.compbiolchem.2024.108324

Vijay H. Masand , Sami Al-Hussain , Gaurav S. Masand , Abdul Samad , Rakhi Gawali , Shravan Jadhav , Magdi E.A. Zaki

{"title":"e-QSAR (Explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray","authors":"Vijay H. Masand , Sami Al-Hussain , Gaurav S. Masand , Abdul Samad , Rakhi Gawali , Shravan Jadhav , Magdi E.A. Zaki","doi":"10.1016/j.compbiolchem.2024.108324","DOIUrl":"10.1016/j.compbiolchem.2024.108324","url":null,"abstract":"<div><div>Glycogen Synthase Kinase-3 beta (GSK-3β) is a crucial enzyme linked to various cellular processes, including neurodegeneration, autophagy, and diabetes. A structurally diverse set of 1293 molecules having GSK-3β modulatory activity has been used. Molecular docking and eXplainable Artificial Intelligence (XAI) have been used concomitantly. The approach involves using GA for feature selection and XGBoost for in-depth analysis, yielding strong statistical validation with R2tr = 0.9075, R2L10 %O = 0.9116, and Q2F3 = 0.7841. Molecular docking provided complementary and similar results. Machine learning model interpretation using SHapley Additive exPlanations (SHAP) revealed that specific structural features like aromatic carbon with specific partial charges, non-ring nitrogen atoms, sp<sup>3</sup>-hybrid carbon atoms, and the topological distance between carbon and nitrogen atoms, among others, significantly influence the modulatory profile. The results are also supported by reported X-ray resolved structures. In addition, <em>in-silico</em> ADMET analysis is also accomplished. This research underscores the value of advanced machine learning techniques in understanding complex biological phenomena and supporting rational drug design.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108324"},"PeriodicalIF":2.6,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis, characterization, and biological activities of novel organometallic compounds of rhenium(I) with 2-(2-benzylidenehydrazinyl) benzothiazole Schiff-base derivatives: Molecular docking, ADME, and DFT studies 铼(I)与2-（2-苄基乙肼基）苯并噻唑希夫碱衍生物的新型有机金属化合物的合成、表征和生物活性：分子对接、ADME和DFT研究。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-16 DOI: 10.1016/j.compbiolchem.2024.108313

Aelvish D. Padariya , Nirbhay K. Savaliya , Hitesh M. Parekh , Bhupesh S. Bhatt , Vaibhav D. Bhatt , Mohan N. Patel

{"title":"Synthesis, characterization, and biological activities of novel organometallic compounds of rhenium(I) with 2-(2-benzylidenehydrazinyl) benzothiazole Schiff-base derivatives: Molecular docking, ADME, and DFT studies","authors":"Aelvish D. Padariya , Nirbhay K. Savaliya , Hitesh M. Parekh , Bhupesh S. Bhatt , Vaibhav D. Bhatt , Mohan N. Patel","doi":"10.1016/j.compbiolchem.2024.108313","DOIUrl":"10.1016/j.compbiolchem.2024.108313","url":null,"abstract":"<div><div>A series of substituted 2-(2-benzylidenehydrazinyl)benzothiazole Schiff-base derivatives and complexes containing Re(I) were synthesized and analyzed using various characterization techniques, including elemental analysis, conductance measurement, <sup>1</sup>H-NMR, FT-IR, and LC-MS. The biological activities of the compounds were evaluated. Binding affinity between the complexes and calf thymus DNA (CT-DNA) was conducted using UV-visible spectroscopy, viscosity measurement, fluorescence spectroscopy, and molecular docking studies, indicating intercalation binding mode. The broth dilution method evaluated antibacterial activity against two Gram-positive and three Gram-negative bacteria. The results demonstrated the effectiveness of each complex against the tested pathogens. The MTT assay examined cytotoxic qualities on MCF-7 cell lines, demonstrating strong cytotoxic effects. The lethality of brine prawn assay was employed to assess the toxicity of the compounds. The Schiff base was optimized using the 6–31 G (d, p) basis set and B3LYP techniques. Density functional theory calculations were performed to compare the bond angles and lengths of the synthesized compounds with experimental values, showing good agreement, and to calculate the related orbital energies. The therapeutic qualities were evaluated using an <em>in silico</em> ADMET model, which verified that the synthesized compounds have qualities similar to those of drugs.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108313"},"PeriodicalIF":2.6,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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。

IF 2.6 4区生物学

Computational Biology and Chemistry Pub Date : 2024-12-15 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":"10.1016/j.compbiolchem.2024.108321","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108321"},"PeriodicalIF":2.6,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0