Computational Biology and Chemistry最新文献_第5页

Multi-omics data integration and analysis pipeline for precision medicine: Systematic review 精准医疗的多组学数据整合与分析管道：系统综述。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-16 DOI: 10.1016/j.compbiolchem.2024.108254

Esraa Hamdi Abdelaziz , Rasha Ismail , Mai S. Mabrouk , Eman Amin

Precision medicine has gained considerable popularity since the "one-size-fits-all" approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body’s inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. Most of the recent reviews were constrained to discussing one aspect of the multi-omics analysis pipeline, such as the dimensionality reduction step, the integration methods, or the interpretability aspect; however, very few provide a comprehensive review of every step of the analysis. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine.

由于 "千篇一律 "的方法似乎并不十分有效，也不能反映人体的复杂性，精准医疗因此大受欢迎。随后，由于单一组学无法反映人体内部运作的复杂性，也就无法在医学领域取得预期的进步。因此，多组学方法应运而生。多组学方法包括利用计算方法整合来自不同组学技术（如 DNA 测序、RNA 测序、质谱分析等）的数据，然后将整合结果用于不同的下游分析应用，如生存分析、癌症分类或生物标记物鉴定。近期的大多数综述都局限于讨论多组学分析管道的一个方面，如降维步骤、整合方法或可解释性方面；然而，很少有综述对分析的每一个步骤进行全面评述。本研究旨在概述多组学分析流水线，从近期文献中最常用的多组学数据库、降维技术入手，详细介绍不同类型的数据整合技术及其下游分析应用，描述最常用的评估指标，强调模型可解释性的重要性，最后讨论精准医疗中多组学数据整合面临的挑战和未来可能开展的工作。

{"title":"Multi-omics data integration and analysis pipeline for precision medicine: Systematic review","authors":"Esraa Hamdi Abdelaziz , Rasha Ismail , Mai S. Mabrouk , Eman Amin","doi":"10.1016/j.compbiolchem.2024.108254","DOIUrl":"10.1016/j.compbiolchem.2024.108254","url":null,"abstract":"<div><div>Precision medicine has gained considerable popularity since the \"one-size-fits-all\" approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body’s inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. Most of the recent reviews were constrained to discussing one aspect of the multi-omics analysis pipeline, such as the dimensionality reduction step, the integration methods, or the interpretability aspect; however, very few provide a comprehensive review of every step of the analysis. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108254"},"PeriodicalIF":2.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514762","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

An efficient interpretable stacking ensemble model for lung cancer prognosis 肺癌预后的高效可解释堆积集合模型

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-15 DOI: 10.1016/j.compbiolchem.2024.108248

Umair Arif , Chunxia Zhang , Sajid Hussain , Abdul Rauf Abbasi

Lung cancer significantly contributes to global cancer mortality, posing challenges in clinical management. Early detection and accurate prognosis are crucial for improving patient outcomes. This study develops an interpretable stacking ensemble model (SEM) for lung cancer prognosis prediction and identifies key risk factors. Using a Kaggle dataset of 1000 patients with 22 variables, the model classifies prognosis into Low, Medium, and High-risk categories. The bootstrap method was employed for evaluation metrics, while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) assessed model interpretability. Results showed SEM's superior interpretability over traditional models, such as Random Forest, Logistic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient Boosting Machine, and Light Gradient Boosting Machine. SEM achieved an accuracy of 98.90 %, precision of 98.70 %, F1 score of 98.85 %, sensitivity of 98.77 %, specificity of 95.45 %, Cohen’s kappa value of 94.56 %, and an AUC of 98.10 %. The SEM demonstrated robust performance in lung cancer prognosis, revealing chronic lung cancer and genetic risk as major factors.

肺癌在全球癌症死亡率中占很大比例，给临床治疗带来了挑战。早期发现和准确预后对改善患者预后至关重要。本研究为肺癌预后预测开发了一种可解释的堆叠集合模型（SEM），并确定了关键风险因素。该模型使用包含 22 个变量的 1000 名患者的 Kaggle 数据集，将预后分为低、中和高风险类别。评估指标采用了自举法，而 SHAP（夏普利相加解释）和 LIME（本地可解释模型-不可知论解释）评估了模型的可解释性。结果显示，SEM 的可解释性优于随机森林、逻辑回归、决策树、梯度提升机、极梯度提升机和轻梯度提升机等传统模型。SEM 的准确度为 98.90 %，精确度为 98.70 %，F1 分数为 98.85 %，灵敏度为 98.77 %，特异度为 95.45 %，Cohen's kappa 值为 94.56 %，AUC 为 98.10 %。SEM 在肺癌预后方面表现出色，显示慢性肺癌和遗传风险是主要因素。

{"title":"An efficient interpretable stacking ensemble model for lung cancer prognosis","authors":"Umair Arif , Chunxia Zhang , Sajid Hussain , Abdul Rauf Abbasi","doi":"10.1016/j.compbiolchem.2024.108248","DOIUrl":"10.1016/j.compbiolchem.2024.108248","url":null,"abstract":"<div><div>Lung cancer significantly contributes to global cancer mortality, posing challenges in clinical management. Early detection and accurate prognosis are crucial for improving patient outcomes. This study develops an interpretable stacking ensemble model (SEM) for lung cancer prognosis prediction and identifies key risk factors. Using a Kaggle dataset of 1000 patients with 22 variables, the model classifies prognosis into Low, Medium, and High-risk categories. The bootstrap method was employed for evaluation metrics, while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) assessed model interpretability. Results showed SEM's superior interpretability over traditional models, such as Random Forest, Logistic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient Boosting Machine, and Light Gradient Boosting Machine. SEM achieved an accuracy of 98.90 %, precision of 98.70 %, F1 score of 98.85 %, sensitivity of 98.77 %, specificity of 95.45 %, Cohen’s kappa value of 94.56 %, and an AUC of 98.10 %. The SEM demonstrated robust performance in lung cancer prognosis, revealing chronic lung cancer and genetic risk as major factors.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108248"},"PeriodicalIF":2.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142483221","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

Exploring a novel four-gene system as a diagnostic and prognostic biomarker for triple-negative breast cancer, using clinical variables 利用临床变量探索作为三阴性乳腺癌诊断和预后生物标志物的新型四基因系统。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-15 DOI: 10.1016/j.compbiolchem.2024.108247

Faisal Ali , Azhar Iqbal , Iqra Azhar , Adiba Qayyum , Syed Ali Hassan , Md. Sakib Al Hasan , Motasim Jawi , Hesham M. Hassan , Ahmed Al-Emam , Muhammad Sajid

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis. This research aims to find real hub genes for prognostic biomarkers of TNBC therapy. The GEO datasets GSE27447 and GSE233242 were analyzed using R package limma to explore DEGs. The PPI was generated using the STRING database. Cytoscape software plug-ins were used to screen the hub genes. Using the DAVID database, GO functional enrichment and KEGG pathway enrichment analysis were performed. Different online expression databases were employed to investigate the functions of real hub genes in tumor driving, diagnosis, and prognosis in TNBC patients with various clinicopathologic characteristics. A total of one hundred DEGs were identified between both datasets. The seven hub genes were identified after the topological parameter analysis of the PPI network. The KEGG pathway and GO analysis suggest that four genes (PSMB1, PSMC1, PSMF1, and PSMD8) are highly enriched in proteasome and were finally considered as real hub genes. Additionally, the expression analysis demonstrated that hub genes were notably up-regulated in TNBC patients compared to controls. Furthermore, correlational analyses revealed the positive and negative correlations among the expression of the real hub genes and various ancillary data, including tumor purity, promoter methylation status, overall survival (OS), genetic alterations, infiltration of CD8+ T and CD4+ immune cells, and a few more, across TNBC samples. Finally, our analysis identified a couple of significant chemotherapeutic drugs, miRNAs and transcription factors (TFS) with intriguing curative potential. In conclusion, we identified four real hub genes as novel biomarkers to overcome heterogenetic-particular challenges in diagnosis, prognosis, and therapy for TNBC patients.

三阴性乳腺癌（TNBC）是预后不良的乳腺癌亚型。本研究旨在寻找 TNBC 治疗预后生物标志物的真正枢纽基因。研究人员使用 R 软件包 limma 分析了 GEO 数据集 GSE27447 和 GSE233242，以探索 DEGs。PPI使用STRING数据库生成。使用 Cytoscape 软件插件筛选枢纽基因。利用 DAVID 数据库进行了 GO 功能富集和 KEGG 通路富集分析。利用不同的在线表达数据库，研究真实的枢纽基因在具有不同临床病理特征的 TNBC 患者的肿瘤驱动、诊断和预后中的功能。两个数据集共鉴定出 100 个 DEGs。在对 PPI 网络进行拓扑参数分析后，确定了七个枢纽基因。KEGG通路和GO分析表明，四个基因（PSMB1、PSMC1、PSMF1和PSMD8）高度富集于蛋白酶体，最终被认为是真正的枢纽基因。此外，表达分析表明，与对照组相比，TNBC 患者的枢纽基因明显上调。此外，相关性分析表明，在 TNBC 样本中，真正中心基因的表达与各种辅助数据（包括肿瘤纯度、启动子甲基化状态、总生存期（OS）、基因改变、CD8+ T 和 CD4+ 免疫细胞浸润等）之间存在正相关和负相关。最后，我们的分析确定了几种重要的化疗药物、miRNA 和转录因子 (TFS)，它们具有令人感兴趣的治疗潜力。总之，我们发现了四个真正的枢纽基因，它们是新型生物标记物，能克服 TNBC 患者在诊断、预后和治疗方面面临的异质性挑战。

{"title":"Exploring a novel four-gene system as a diagnostic and prognostic biomarker for triple-negative breast cancer, using clinical variables","authors":"Faisal Ali , Azhar Iqbal , Iqra Azhar , Adiba Qayyum , Syed Ali Hassan , Md. Sakib Al Hasan , Motasim Jawi , Hesham M. Hassan , Ahmed Al-Emam , Muhammad Sajid","doi":"10.1016/j.compbiolchem.2024.108247","DOIUrl":"10.1016/j.compbiolchem.2024.108247","url":null,"abstract":"<div><div>Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis. This research aims to find real hub genes for prognostic biomarkers of TNBC therapy. The GEO datasets GSE27447 and GSE233242 were analyzed using R package limma to explore DEGs. The PPI was generated using the STRING database. Cytoscape software plug-ins were used to screen the hub genes. Using the DAVID database, GO functional enrichment and KEGG pathway enrichment analysis were performed. Different online expression databases were employed to investigate the functions of real hub genes in tumor driving, diagnosis, and prognosis in TNBC patients with various clinicopathologic characteristics. A total of one hundred DEGs were identified between both datasets. The seven hub genes were identified after the topological parameter analysis of the PPI network. The KEGG pathway and GO analysis suggest that four genes (PSMB1, PSMC1, PSMF1, and PSMD8) are highly enriched in proteasome and were finally considered as real hub genes. Additionally, the expression analysis demonstrated that hub genes were notably up-regulated in TNBC patients compared to controls. Furthermore, correlational analyses revealed the positive and negative correlations among the expression of the real hub genes and various ancillary data, including tumor purity, promoter methylation status, overall survival (OS), genetic alterations, infiltration of CD8+ T and CD4+ immune cells, and a few more, across TNBC samples. Finally, our analysis identified a couple of significant chemotherapeutic drugs, miRNAs and transcription factors (TFS) with intriguing curative potential. In conclusion, we identified four real hub genes as novel biomarkers to overcome heterogenetic-particular challenges in diagnosis, prognosis, and therapy for TNBC patients.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108247"},"PeriodicalIF":2.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142483223","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-pharmacophore based virtual screening of potent lead molecules against Cystic Fibrosis: An in silico study 基于 E-pharmacophore 的囊性纤维化强效先导分子虚拟筛选：硅学研究

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-12 DOI: 10.1016/j.compbiolchem.2024.108249

Sabareeswari Jeyaraman , Jeyanthi Sankar , Ling Shing Wong , Karthikeyan Muthusamy

Cystic fibrosis is an autosomal recessive condition caused by mutations in the CFTR gene, which encodes the CFTR protein. Currently, CF is a life-limiting illness that has a limited cure. The present study aimed to identify top leads against CFTR protein with F508del in comparison with Lumacaftor. In this study, a homology model of the NBD domain of CFTR protein was developed using the available NBD domain crystal structure. The protein model was refined through apo dynamics. Energy-optimized pharmacophore mapping was carried out to identify essential features for CFTR, resulting in a model with a hydrogen-bond donor, two hydrogen-bond acceptors, and three aromatic ring sites. A screening of a compound from the NPASS database using these DAARRR six-point-pharmacophore features led to the identification of potential ligands that could act against CFTR protein. Further studies such as ADME/T, molecular dynamics, MM_GBSA, and DFT were performed to identify the top-hit compound from the NPASS database. The compound Anguibactin (NPC41982) has been identified as a top lead that exhibits higher binding affinity and stability than the reference compound Lumacaftor, suggesting their potential to bind to the active site of the CFTR protein. These compounds could serve as starting points for the development of drug-like molecules for treating cystic fibrosis.

囊性纤维化是一种常染色体隐性遗传病，由编码 CFTR 蛋白的 CFTR 基因突变引起。目前，CF 是一种限制生命的疾病，治愈率有限。本研究旨在与 Lumacaftor 相比，找出针对 CFTR 蛋白 F508del 的顶级药物。本研究利用现有的 NBD 结构域晶体结构，建立了 CFTR 蛋白 NBD 结构域的同源模型。通过apo动力学对该蛋白质模型进行了完善。为了确定 CFTR 的基本特征，研究人员进行了能量优化药性图谱绘制，最终建立了一个具有一个氢键供体、两个氢键受体和三个芳香环位点的模型。利用这些 DAARRR 六点药效位点特征对 NPASS 数据库中的一种化合物进行筛选，最终确定了可对 CFTR 蛋白起作用的潜在配体。通过 ADME/T、分子动力学、MM_GBSA 和 DFT 等进一步研究，从 NPASS 数据库中确定了最热门的化合物。与参考化合物 Lumacaftor 相比，化合物 Anguibactin（NPC41982）表现出更高的结合亲和力和稳定性，这表明它们具有与 CFTR 蛋白活性位点结合的潜力。这些化合物可以作为开发治疗囊性纤维化的类药物分子的起点。

{"title":"E-pharmacophore based virtual screening of potent lead molecules against Cystic Fibrosis: An in silico study","authors":"Sabareeswari Jeyaraman , Jeyanthi Sankar , Ling Shing Wong , Karthikeyan Muthusamy","doi":"10.1016/j.compbiolchem.2024.108249","DOIUrl":"10.1016/j.compbiolchem.2024.108249","url":null,"abstract":"<div><div>Cystic fibrosis is an autosomal recessive condition caused by mutations in the CFTR gene, which encodes the CFTR protein. Currently, CF is a life-limiting illness that has a limited cure. The present study aimed to identify top leads against CFTR protein with F508del in comparison with Lumacaftor. In this study, a homology model of the NBD domain of CFTR protein was developed using the available NBD domain crystal structure. The protein model was refined through apo dynamics. Energy-optimized pharmacophore mapping was carried out to identify essential features for CFTR, resulting in a model with a hydrogen-bond donor, two hydrogen-bond acceptors, and three aromatic ring sites. A screening of a compound from the NPASS database using these DAARRR six-point-pharmacophore features led to the identification of potential ligands that could act against CFTR protein. Further studies such as ADME/T, molecular dynamics, MM_GBSA, and DFT were performed to identify the top-hit compound from the NPASS database. The compound Anguibactin (NPC41982) has been identified as a top lead that exhibits higher binding affinity and stability than the reference compound Lumacaftor, suggesting their potential to bind to the active site of the CFTR protein. These compounds could serve as starting points for the development of drug-like molecules for treating cystic fibrosis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108249"},"PeriodicalIF":2.6,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142483222","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

Gene expression profiling in Venous thromboembolism: Insights from publicly available datasets 静脉血栓栓塞症的基因表达谱分析：从公开数据集中获得的启示

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-11 DOI: 10.1016/j.compbiolchem.2024.108246

Sunanda Arya, Rashi Khare, Iti Garg, Swati Srivastava

Background

Venous thromboembolism (VTE) is the third most common cardiovascular disease and is a major cause of mobility and mortality worldwide. VTE is a complex multifactorial disease and genetic mechanisms underlying its pathogenesis is yet to be completely elucidated. The aim of the present study was to identify hub genes and pathways involved in development and progression of blood clot during VTE using gene expression data from public repositories.

Methodology

Differential gene expression (DEG) data from two datasets, GSE48000 and GSE19151 were analysed using GEO2R tool. Gene expression data of VTE patients were compared to that of healthy controls using various bioinformatics tools.

Results

When the differentially expressed genes of the two datasets were compared, it was found that 19 genes were up-regulated while 134 genes were down-regulated. Gene ontology (GO) and pathway analysis revealed that pathways such as complement and coagulation cascade and B-cell receptor signalling along with DNA methylation, DNA alkylation and inflammatory genes were significantly up-regulated in VTE patients. On the other hand, differentially down-regulated genes included mitochondrial translation elongation, termination and biosysthesis along with heme biosynthesis, erythrocyte differentiation and homeostasis. The top 5 up-regulated hub genes obtained by protein-protein interaction (PPI) network analysis included MYC, FOS, SGK1, CR2 and CXCR4, whereas the top 5 down-regulated hub genes included MRPL13, MRPL3, MRPL11, RPS29 and RPL9. The up-regulated hub genes are functionally involved in maintain vascular integrity and complementation cascade while the down-regulated hub genes were mostly mitochondrial ribosomal proteins.

Conclusion

Present study highlights significantly enriched pathways and genes associated with VTE development and prognosis. The data hereby obtained could be used for designing newer diagnostic and therapeutic tools for VTE management.

背景静脉血栓栓塞症（VTE）是第三大最常见的心血管疾病，也是全球流动性和死亡率的主要原因。VTE 是一种复杂的多因素疾病，其发病的遗传机制尚未完全阐明。本研究的目的是利用公共资料库中的基因表达数据，找出 VTE 期间血栓形成和发展过程中的枢纽基因和通路。方法利用 GEO2R 工具分析 GSE48000 和 GSE19151 两个数据集中的差异基因表达（DEG）数据。结果比较两个数据集的差异表达基因后发现，19 个基因上调，134 个基因下调。基因本体（GO）和通路分析显示，补体和凝血级联、B 细胞受体信号传导等通路以及 DNA 甲基化、DNA 烷基化和炎症基因在 VTE 患者中明显上调。另一方面，不同程度下调的基因包括线粒体翻译延伸、终止和生物合成，以及血红素生物合成、红细胞分化和稳态。通过蛋白-蛋白相互作用（PPI）网络分析获得的前5个上调中枢基因包括MYC、FOS、SGK1、CR2和CXCR4，而前5个下调中枢基因包括MRPL13、MRPL3、MRPL11、RPS29和RPL9。上调的中枢基因在功能上参与维持血管完整性和互补级联，而下调的中枢基因主要是线粒体核糖体蛋白。本研究获得的数据可用于设计新的 VTE 诊断和治疗工具。

{"title":"Gene expression profiling in Venous thromboembolism: Insights from publicly available datasets","authors":"Sunanda Arya, Rashi Khare, Iti Garg, Swati Srivastava","doi":"10.1016/j.compbiolchem.2024.108246","DOIUrl":"10.1016/j.compbiolchem.2024.108246","url":null,"abstract":"<div><h3>Background</h3><div>Venous thromboembolism (VTE) is the third most common cardiovascular disease and is a major cause of mobility and mortality worldwide. VTE is a complex multifactorial disease and genetic mechanisms underlying its pathogenesis is yet to be completely elucidated. The aim of the present study was to identify hub genes and pathways involved in development and progression of blood clot during VTE using gene expression data from public repositories.</div></div><div><h3>Methodology</h3><div>Differential gene expression (DEG) data from two datasets, GSE48000 and GSE19151 were analysed using GEO2R tool. Gene expression data of VTE patients were compared to that of healthy controls using various bioinformatics tools.</div></div><div><h3>Results</h3><div>When the differentially expressed genes of the two datasets were compared, it was found that 19 genes were up-regulated while 134 genes were down-regulated. Gene ontology (GO) and pathway analysis revealed that pathways such as complement and coagulation cascade and B-cell receptor signalling along with DNA methylation, DNA alkylation and inflammatory genes were significantly up-regulated in VTE patients. On the other hand, differentially down-regulated genes included mitochondrial translation elongation, termination and biosysthesis along with heme biosynthesis, erythrocyte differentiation and homeostasis. The top 5 up-regulated hub genes obtained by protein-protein interaction (PPI) network analysis included MYC, FOS, SGK1, CR2 and CXCR4, whereas the top 5 down-regulated hub genes included MRPL13, MRPL3, MRPL11, RPS29 and RPL9. The up-regulated hub genes are functionally involved in maintain vascular integrity and complementation cascade while the down-regulated hub genes were mostly mitochondrial ribosomal proteins.</div></div><div><h3>Conclusion</h3><div>Present study highlights significantly enriched pathways and genes associated with VTE development and prognosis. The data hereby obtained could be used for designing newer diagnostic and therapeutic tools for VTE management.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108246"},"PeriodicalIF":2.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437805","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

A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss 基于自适应尺度判别器和混合损失的多级眼底疾病分类系统

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-11 DOI: 10.1016/j.compbiolchem.2024.108241

Shiyu Zhou, Jue Wang, Bo Li

Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of fundus disease structures, which leads to low detection accuracy. Another challenge is the class imbalance problem common in multi-label image classification, which increases the difficulty of algorithm training and evaluation. To address these issues, this study leverages deep learning to propose an ophthalmic disease classification system. We first employ ResNet50 as the backbone network to extract image features, and then use our designed multi-dimensional attention module and adaptive scale discriminator to enhance the network's ability to detect disease features. During training, we innovatively propose a hybrid loss function method to improve the detection capability on imbalanced data. Finally, we conducted experiments on the ODRI-5K dataset with the proposed classification system. In the test set, our method achieved an AUC of 98.53 and an F1-score of 89.73. This result fully demonstrates the excellent disease classification capability of our method. In summary, the multi-label fundus image disease classification system we proposed exhibits outstanding recognition capability, providing an effective solution for the diagnosis of multi-label fundus image diseases.

眼底图像对于观察和检测眼科疾病至关重要。然而，利用深度学习技术从眼底图像中检测多种眼科疾病是一项复杂而具有挑战性的任务。一个挑战是眼底疾病结构的复杂性导致检测准确率较低。另一个挑战是多标签图像分类中常见的类不平衡问题，这增加了算法训练和评估的难度。为了解决这些问题，本研究利用深度学习提出了眼科疾病分类系统。我们首先采用 ResNet50 作为骨干网络来提取图像特征，然后利用我们设计的多维注意力模块和自适应尺度判别器来增强网络检测疾病特征的能力。在训练过程中，我们创新性地提出了一种混合损失函数方法，以提高对不平衡数据的检测能力。最后，我们利用所提出的分类系统在 ODRI-5K 数据集上进行了实验。在测试集中，我们的方法取得了 98.53 的 AUC 和 89.73 的 F1 分数。这一结果充分证明了我们的方法具有出色的疾病分类能力。总之，我们提出的多标签眼底图像疾病分类系统具有出色的识别能力，为多标签眼底图像疾病诊断提供了有效的解决方案。

{"title":"A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss","authors":"Shiyu Zhou, Jue Wang, Bo Li","doi":"10.1016/j.compbiolchem.2024.108241","DOIUrl":"10.1016/j.compbiolchem.2024.108241","url":null,"abstract":"<div><div>Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of fundus disease structures, which leads to low detection accuracy. Another challenge is the class imbalance problem common in multi-label image classification, which increases the difficulty of algorithm training and evaluation. To address these issues, this study leverages deep learning to propose an ophthalmic disease classification system. We first employ ResNet50 as the backbone network to extract image features, and then use our designed multi-dimensional attention module and adaptive scale discriminator to enhance the network's ability to detect disease features. During training, we innovatively propose a hybrid loss function method to improve the detection capability on imbalanced data. Finally, we conducted experiments on the ODRI-5K dataset with the proposed classification system. In the test set, our method achieved an AUC of 98.53 and an F1-score of 89.73. This result fully demonstrates the excellent disease classification capability of our method. In summary, the multi-label fundus image disease classification system we proposed exhibits outstanding recognition capability, providing an effective solution for the diagnosis of multi-label fundus image diseases.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108241"},"PeriodicalIF":2.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433266","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

Unraveling the biological functions of UCEC: Insights from a prognostic signature model 揭示 UCEC 的生物功能：预后特征模型的启示。

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-11 DOI: 10.1016/j.compbiolchem.2024.108219

Qi Zhu , Wulin Shan , Xiaoyu Li , Yao Chen , Xu Huang , Bairong Xia , Liting Qian

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological tumor with a bleak prognosis. Anomalous glycosylation plays a pivotal role in tumorigenesis. Currently, there is a lack of prognostic signatures based on glycosylation-related genes for UCEC. Thus, our research aims to construct a predictive model and validate the correlation between relevant genes and biological functions.

Methods

Using the TCGA database, we developed prognostic models and explored their relationships with survival outcomes. We further selected key genes to verify their expression in tissues and assess their impact on cellular behavior.

Results

The clinical prognosis of the high-risk group was significantly worse than that of the low-risk group. The nomogram model accurately predicted UCEC patient prognosis. Additionally, we identified OLFML1 as a unique signature gene that can inhibit UCEC progression and reduce radiation resistance in vitro.

Conclusions

Our model, which is based on glycosylation-related genes in UCEC, effectively identifies high-risk patients and provides valuable prognostic information. In addition, OLFML1 acts as a tumor suppressor in UCEC and enhances radiosensitivity, suggesting a new potential target for improving therapeutic efficacy.

背景：子宫内膜癌（UCEC）是一种常见的妇科肿瘤，预后不良。异常糖基化在肿瘤发生中起着关键作用。目前，还缺乏基于糖基化相关基因的 UCEC 预后特征。因此，我们的研究旨在构建一个预测模型，并验证相关基因与生物学功能之间的相关性：方法：我们利用 TCGA 数据库建立了预后模型，并探讨了它们与生存结果的关系。我们进一步选择了关键基因，以验证它们在组织中的表达，并评估它们对细胞行为的影响：结果：高危组的临床预后明显差于低危组。提名图模型能准确预测 UCEC 患者的预后。此外，我们还发现OLFML1是一个独特的特征基因，可抑制UCEC的进展并降低体外抗辐射能力：我们的模型基于 UCEC 中的糖基化相关基因，能有效识别高危患者并提供有价值的预后信息。此外，OLFML1在UCEC中起肿瘤抑制作用，并能增强放射敏感性，这为提高疗效提供了一个新的潜在靶点。

{"title":"Unraveling the biological functions of UCEC: Insights from a prognostic signature model","authors":"Qi Zhu , Wulin Shan , Xiaoyu Li , Yao Chen , Xu Huang , Bairong Xia , Liting Qian","doi":"10.1016/j.compbiolchem.2024.108219","DOIUrl":"10.1016/j.compbiolchem.2024.108219","url":null,"abstract":"<div><h3>Background</h3><div>Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological tumor with a bleak prognosis. Anomalous glycosylation plays a pivotal role in tumorigenesis. Currently, there is a lack of prognostic signatures based on glycosylation-related genes for UCEC. Thus, our research aims to construct a predictive model and validate the correlation between relevant genes and biological functions.</div></div><div><h3>Methods</h3><div>Using the TCGA database, we developed prognostic models and explored their relationships with survival outcomes. We further selected key genes to verify their expression in tissues and assess their impact on cellular behavior.</div></div><div><h3>Results</h3><div>The clinical prognosis of the high-risk group was significantly worse than that of the low-risk group. The nomogram model accurately predicted UCEC patient prognosis. Additionally, we identified OLFML1 as a unique signature gene that can inhibit UCEC progression and reduce radiation resistance in vitro.</div></div><div><h3>Conclusions</h3><div>Our model, which is based on glycosylation-related genes in UCEC, effectively identifies high-risk patients and provides valuable prognostic information. In addition, OLFML1 acts as a tumor suppressor in UCEC and enhances radiosensitivity, suggesting a new potential target for improving therapeutic efficacy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108219"},"PeriodicalIF":2.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Potential mechanisms for predicting comorbidity between multiple myeloma and femoral head necrosis based on multiple bioinformatics 基于多重生物信息学预测多发性骨髓瘤与股骨头坏死合并症的潜在机制

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-10 DOI: 10.1016/j.compbiolchem.2024.108220

Jie Li , Jing Dong , Ming Li , Hongbo Zhu , Peicheng Xin

Objective

This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).

Method

High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.

Results

Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.

Conclusion

MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions.

本研究旨在利用多种生物信息学工具阐明多发性骨髓瘤（MM）和股骨头坏死（ONFH）并发症的潜在机制。方法从GEO数据库中检索MM和ONFH的高通量芯片数据集，然后分别进行预处理。我们应用加权基因共表达网络分析（WGCNA）在MM数据集中构建共表达网络，进一步确定与MM临床特征相关的模块和基因。利用通路和网络分析工具对潜在的合并基因进行了富集和分析，并利用Cytoscape初步筛选了MM和ONFH合并症的关键基因。利用两个疾病相关数据集对基因表达能力和性能进行了验证，我们还评估了两种疾病免疫微环境的差异和一致性。通过蛋白质-蛋白质相互作用（PPI）网络进一步确定了关键基因，并在验证队列中验证了这些基因的性能，其接收者操作特征曲线（ROC）面积超过了 0.8。免疫微环境分析强调了浆细胞浸润与疾病并发症的相关性，为未来的疗法提出了潜在的免疫靶点。已发现的关键基因，如 RPS19、RPL35、RPL24、RPL36 和 EIF3G，以及浆细胞浸润，可能是这两种疾病发病的核心机制。这项研究为进一步研究这些疾病的靶向治疗提供了启示和参考。

{"title":"Potential mechanisms for predicting comorbidity between multiple myeloma and femoral head necrosis based on multiple bioinformatics","authors":"Jie Li , Jing Dong , Ming Li , Hongbo Zhu , Peicheng Xin","doi":"10.1016/j.compbiolchem.2024.108220","DOIUrl":"10.1016/j.compbiolchem.2024.108220","url":null,"abstract":"<div><h3>Objective</h3><div>This study aims to utilize multiple bioinformatics tools to elucidate the potential mechanisms underlying the comorbidity of Multiple Myeloma (MM) and Osteonecrosis of the Femoral Head (ONFH).</div></div><div><h3>Method</h3><div>High-throughput microarray datasets for MM and ONFH were retrieved from the GEO database, followed by separate preprocessing. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct co-expression networks within the MM datasets, further identifying modules and genes associated with MM clinical characteristics. Potential comorbid genes were enriched and analyzed using pathway and network analysis tools, and key genes for MM and ONFH comorbidity were preliminarily screened using Cytoscape. The gene expression capabilities and performance were validated using two disease-related datasets, and we evaluated the differences and consistencies in the immune microenvironment between the two diseases.</div></div><div><h3>Results</h3><div>Our screening identified 418 immune-related comorbid genes, showing consistent biological processes in ribosome synthesis, particularly protein synthesis across both diseases. Key genes were further identified through Protein-Protein Interaction (PPI) networks, and their performance was validated in a validation cohort, with Receiver Operating Characteristic (ROC) curve areas exceeding 0.8. The immune microenvironment analysis highlighted consistent plasma cell infiltration correlated with disease comorbidity, suggesting potential immune targets for future therapies.</div></div><div><h3>Conclusion</h3><div>MM and ONFH share common pathogenic genes that mediate changes in signaling pathways and immune cell dynamics, potentially influencing the comorbidity and progression of these diseases. Key genes identified, such as RPS19, RPL35, RPL24, RPL36, and EIF3G, along with plasma cell infiltration, may serve as central mechanisms in the development of both diseases. This study offers insights and references for further research into targeted treatments for these conditions<strong>.</strong></div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108220"},"PeriodicalIF":2.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433268","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

Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors 整合三维-QSAR、分子对接和机器学习技术，合理设计基于烟酰胺的 SIRT2 抑制剂

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-10 DOI: 10.1016/j.compbiolchem.2024.108242

Aleksandra Ilic, Nemanja Djokovic, Teodora Djikic , Katarina Nikolic

Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selective SIRT2 inhibitors reported to date (Ai et al., 2016, Ai et al., 2023, Baroni et al., 2007). In this study, a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model was developed using a dataset of 86 nicotinamide-based SIRT2 inhibitors from the literature, along with GRIND-derived pharmacophore models for selected inhibitors. External validation parameters emphasized the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined applicability domain. The interpretation of the 3D-QSAR model facilitated the generation of GRIND-derived pharmacophore models, which in turn enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, two classification models were developed: a SIRT1/2 model using the Naive Bayes algorithm and a SIRT2/3 model using the k-nearest neighbors algorithm, to predict the selectivity of inhibitors for SIRT1/2 and SIRT2/3. External validation parameters of the selectivity models confirmed their predictive power. Ultimately, the integration of 3D-QSAR, selectivity models and prediction of ADMET properties facilitated the identification of the most promising selective SIRT2 inhibitors for further development.

人们日益认识到，sirtuin-2（SIRT2）的选择性抑制剂是治疗癌症和神经退行性疾病的潜在疗法。5-((3-脒基苄基)氧基)烟酰胺的衍生物已被确定为迄今报道的一些最有效和最具选择性的 SIRT2 抑制剂（Ai 等人，2016 年；Ai 等人，2023 年；Baroni 等人，2007 年）。在本研究中，利用文献中 86 种基于烟酰胺的 SIRT2 抑制剂数据集以及 GRIND 衍生的选定抑制剂的药效模型，建立了一个 3D-QSAR （3D-定量结构-活性关系）模型。外部验证参数强调了三维-QSAR 模型在定义的适用范围内预测 SIRT2 抑制作用的可靠性。三维-QSAR 模型的解释有助于生成 GRIND 衍生的药理模型，进而有助于设计新型 SIRT2 抑制剂。此外，根据 SIRT1-3 异构体的分子对接结果，还开发了两个分类模型：一个是使用 Naive Bayes 算法的 SIRT1/2 模型，另一个是使用 k-nearest neighbors 算法的 SIRT2/3 模型，用于预测抑制剂对 SIRT1/2 和 SIRT2/3 的选择性。选择性模型的外部验证参数证实了它们的预测能力。最终，3D-QSAR、选择性模型和 ADMET 特性预测的整合促进了最有前途的选择性 SIRT2 抑制剂的确定，以便进一步开发。

{"title":"Integration of 3D-QSAR, molecular docking, and machine learning techniques for rational design of nicotinamide-based SIRT2 inhibitors","authors":"Aleksandra Ilic, Nemanja Djokovic, Teodora Djikic , Katarina Nikolic","doi":"10.1016/j.compbiolchem.2024.108242","DOIUrl":"10.1016/j.compbiolchem.2024.108242","url":null,"abstract":"<div><div>Selective inhibitors of sirtuin-2 (SIRT2) are increasingly recognized as potential therapeutics for cancer and neurodegenerative diseases. Derivatives of 5-((3-amidobenzyl)oxy)nicotinamides have been identified as some of the most potent and selective SIRT2 inhibitors reported to date (<span><span>Ai et al., 2016</span></span>, <span><span>Ai et al., 2023</span></span>, <span><span>Baroni et al., 2007</span></span>). In this study, a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model was developed using a dataset of 86 nicotinamide-based SIRT2 inhibitors from the literature, along with GRIND-derived pharmacophore models for selected inhibitors. External validation parameters emphasized the reliability of the 3D-QSAR model in predicting SIRT2 inhibition within the defined applicability domain. The interpretation of the 3D-QSAR model facilitated the generation of GRIND-derived pharmacophore models, which in turn enabled the design of novel SIRT2 inhibitors. Furthermore, based on molecular docking results for the SIRT1–3 isoforms, two classification models were developed: a SIRT1/2 model using the Naive Bayes algorithm and a SIRT2/3 model using the k-nearest neighbors algorithm, to predict the selectivity of inhibitors for SIRT1/2 and SIRT2/3. External validation parameters of the selectivity models confirmed their predictive power. Ultimately, the integration of 3D-QSAR, selectivity models and prediction of ADMET properties facilitated the identification of the most promising selective SIRT2 inhibitors for further development.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108242"},"PeriodicalIF":2.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433267","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

Computational analysis of natural compounds as potential phosphodiesterase type 5A inhibitors 作为潜在 5A 型磷酸二酯酶抑制剂的天然化合物的计算分析

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2024-10-10 DOI: 10.1016/j.compbiolchem.2024.108239

Abdullahi Ibrahim Uba , Nicholas Joseph Paradis , Chun Wu , Gokhan Zengin

Phosphodiesterase type 5 (PDE5) is a cyclic nucleotide-hydrolyzing enzyme that plays essential roles in the regulation of second messenger cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) produced in response to various stimuli. Pharmacological inhibition of PDE5 has been shown to have several therapeutic uses, including treating cardiovascular diseases and erectile dysfunction. In search of PDE5A inhibitors with safer pharmacokinetic properties, computational analyses of the binding propensity of fifty natural compounds comprising flavonoids, polyphenols, and glycosides were conducted. Molecular dynamics simulation coupled with Molecular mechanics with generalized Born and surface area solvation (MM/GBSA) showed that verbascoside may inhibit the activity of PDE5 with a comparative binding energy (ΔG) of -87.8 ± 9.2 kcal/mol to that of the cocrystal ligand (PDB ID: 3BJC), having ΔG = -77.7±4.5 kcal/mol. However, the other top compounds studied were found to have lower binding propensities than the cocrystal ligand WAN: hesperidin (ΔG = -33.8 ± 3.4 kcal/mol), rutin (ΔG = -23.6 ± 26.3 kcal/mol), caftaric acid (ΔG = -21.2 ±3.6 kcal/mol), and chlorogenic acid (ΔG = 6.0 ± 16.5 kcal/mol). Therefore, verbascoside may serve as a potential PDE5A inhibitor while hesperidin, rutin, and caftaric acid may provide templates for further structural optimization for the designs of safer PDE5 inhibitors.

5 型磷酸二酯酶（PDE5）是一种环核苷酸水解酶，在调节第二信使环磷酸腺苷（cAMP）和环磷酸鸟苷（cGMP）对各种刺激的反应中发挥着重要作用。药理抑制 PDE5 已被证明具有多种治疗用途，包括治疗心血管疾病和勃起功能障碍。为了寻找具有更安全药代动力学特性的 PDE5A 抑制剂，研究人员对包括类黄酮、多酚和苷类在内的 50 种天然化合物的结合倾向进行了计算分析。分子动力学模拟结合广义玻恩和表面积溶解分子力学（MM/GBSA）表明，马鞭草苷可抑制 PDE5 的活性，其结合能（ΔG）为-87.8 ± 9.2 kcal/mol，而共晶体配体（PDB ID：3BJC）的结合能（ΔG = -77.7±4.5 kcal/mol）为-77.8 ± 9.2 kcal/mol。然而，研究发现其他顶级化合物的结合率低于共晶配体 WAN：橙皮甙（ΔG = -33.8 ± 3.4 kcal/mol）、芦丁（ΔG = -23.6 ± 26.3 kcal/mol）、茶醛酸（ΔG = -21.2 ± 3.6 kcal/mol）和绿原酸（ΔG = 6.0 ± 16.5 kcal/mol）。因此，马鞭草苷可作为一种潜在的 PDE5A 抑制剂，而橙皮甙、芦丁和茶黄酸则可为进一步优化结构以设计更安全的 PDE5 抑制剂提供模板。

{"title":"Computational analysis of natural compounds as potential phosphodiesterase type 5A inhibitors","authors":"Abdullahi Ibrahim Uba , Nicholas Joseph Paradis , Chun Wu , Gokhan Zengin","doi":"10.1016/j.compbiolchem.2024.108239","DOIUrl":"10.1016/j.compbiolchem.2024.108239","url":null,"abstract":"<div><div>Phosphodiesterase type 5 (PDE5) is a cyclic nucleotide-hydrolyzing enzyme that plays essential roles in the regulation of second messenger cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) produced in response to various stimuli. Pharmacological inhibition of PDE5 has been shown to have several therapeutic uses, including treating cardiovascular diseases and erectile dysfunction. In search of PDE5A inhibitors with safer pharmacokinetic properties, computational analyses of the binding propensity of fifty natural compounds comprising flavonoids, polyphenols, and glycosides were conducted. Molecular dynamics simulation coupled with Molecular mechanics with generalized Born and surface area solvation (MM/GBSA) showed that verbascoside may inhibit the activity of PDE5 with a comparative binding energy (ΔG) of -87.8 ± 9.2kcal/mol to that of the cocrystal ligand (PDB ID: 3BJC), having ΔG = -77.7±4.5kcal/mol. However, the other top compounds studied were found to have lower binding propensities than the cocrystal ligand WAN: hesperidin (ΔG = -33.8 ± 3.4kcal/mol), rutin (ΔG = -23.6 ± 26.3kcal/mol), caftaric acid (ΔG = -21.2 ±3.6kcal/mol), and chlorogenic acid (ΔG = 6.0 ± 16.5kcal/mol). Therefore, verbascoside may serve as a potential PDE5A inhibitor while hesperidin, rutin, and caftaric acid may provide templates for further structural optimization for the designs of safer PDE5 inhibitors.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108239"},"PeriodicalIF":2.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428645","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