Pub Date : 2026-04-01Epub Date: 2025-11-11DOI: 10.1016/j.compbiolchem.2025.108774
Fangping Deng , Shuxin Fu , Dandan Li , Shuo Qi , Hong Zou
Oral submucous fibrosis(OSF) is a chronically progressive lesion in which the pathologic process begins with abnormal changes in the mucosal tissue. It is widely recognized as a precancerous lesion of oral squamous cell carcinoma (OSCC). Curcumin, a polyphenol derived from turmeric, has several biological effects. Curcumin suppresses the process of OSF and improves signs of related diseases. Nevertheless, the molecular actions involved in curcumin's intervention in OSF remain to be further elucidated. Accordingly, our study used network pharmacology combined with molecular docking strategy to systematically investigate the multiple-target mechanism of action of curcumin in intervening in oral submucosal fibrosis. Relying on the network topology approach, the study initially identified 194 potential targets of action. The core action targets of curcumin are MMP9, TP53, MYC and TNF, among others, and its key signaling pathways are PI3K/AKT, tumor and other signals, and so on, so that multi-component, multi-targets, and multi-pathways exert its therapeutic effects on OSF. By elucidating the multi-target mechanism of action of curcumin, our study offers a new theoretical basis for the clinical therapy strategy of OSF.
{"title":"A network pharmacology analysis of curcumin in the treatment of oral submucous fibrosis","authors":"Fangping Deng , Shuxin Fu , Dandan Li , Shuo Qi , Hong Zou","doi":"10.1016/j.compbiolchem.2025.108774","DOIUrl":"10.1016/j.compbiolchem.2025.108774","url":null,"abstract":"<div><div>Oral submucous fibrosis(OSF) is a chronically progressive lesion in which the pathologic process begins with abnormal changes in the mucosal tissue. It is widely recognized as a precancerous lesion of oral squamous cell carcinoma (OSCC). Curcumin, a polyphenol derived from turmeric, has several biological effects. Curcumin suppresses the process of OSF and improves signs of related diseases. Nevertheless, the molecular actions involved in curcumin's intervention in OSF remain to be further elucidated. Accordingly, our study used network pharmacology combined with molecular docking strategy to systematically investigate the multiple-target mechanism of action of curcumin in intervening in oral submucosal fibrosis. Relying on the network topology approach, the study initially identified 194 potential targets of action. The core action targets of curcumin are MMP9, TP53, MYC and TNF, among others, and its key signaling pathways are PI3K/AKT, tumor and other signals, and so on, so that multi-component, multi-targets, and multi-pathways exert its therapeutic effects on OSF. By elucidating the multi-target mechanism of action of curcumin, our study offers a new theoretical basis for the clinical therapy strategy of OSF.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108774"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673093","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}
Pub Date : 2026-04-01Epub Date: 2025-12-18DOI: 10.1016/j.compbiolchem.2025.108846
Shuxin Song , Mingxian Lu , Yusen Su , Taigang Liu
Proinflammatory peptides (PIPs) are short bioactive sequences that mediate immune responses and contribute to various inflammatory diseases. Accurate identification of PIPs is essential for elucidating disease mechanisms and accelerating therapeutic development. However, sequence diversity and complexity make traditional wet-lab assays time-consuming and costly, highlighting the need for efficient computational solutions. Inspired by the success of pre-trained protein language models (PLMs) in protein recognition tasks, we present CCMPIP, a unified framework that fuses semantic embeddings from ProtT5 with physicochemical descriptors from AAindex via a cross-attention mechanism. Peptide sequences are first encoded into dual feature matrices, which are then integrated by cross-attention to capture interdependencies. The resulting representation is refined through cascading convolutional neural network (CNN) layers and a capsule network to model local patterns and hierarchical features, and finally classified by multilayer perceptron (MLP) under 5-fold cross-validation. Comparative experiments against recent ensemble predictors demonstrate CCMPIP’s superior predictive power. Moreover, interpretability analyses using attention heatmaps and STREME motif enrichment confirm that CCMPIP highlights biologically relevant residues, providing transparent insights into proinflammatory activity.
{"title":"CCMPIP: Cross-attention and capsule network-based multi-feature fusion for proinflammatory peptide prediction","authors":"Shuxin Song , Mingxian Lu , Yusen Su , Taigang Liu","doi":"10.1016/j.compbiolchem.2025.108846","DOIUrl":"10.1016/j.compbiolchem.2025.108846","url":null,"abstract":"<div><div>Proinflammatory peptides (PIPs) are short bioactive sequences that mediate immune responses and contribute to various inflammatory diseases. Accurate identification of PIPs is essential for elucidating disease mechanisms and accelerating therapeutic development. However, sequence diversity and complexity make traditional wet-lab assays time-consuming and costly, highlighting the need for efficient computational solutions. Inspired by the success of pre-trained protein language models (PLMs) in protein recognition tasks, we present CCMPIP, a unified framework that fuses semantic embeddings from ProtT5 with physicochemical descriptors from AAindex via a cross-attention mechanism. Peptide sequences are first encoded into dual feature matrices, which are then integrated by cross-attention to capture interdependencies. The resulting representation is refined through cascading convolutional neural network (CNN) layers and a capsule network to model local patterns and hierarchical features, and finally classified by multilayer perceptron (MLP) under 5-fold cross-validation. Comparative experiments against recent ensemble predictors demonstrate CCMPIP’s superior predictive power. Moreover, interpretability analyses using attention heatmaps and STREME motif enrichment confirm that CCMPIP highlights biologically relevant residues, providing transparent insights into proinflammatory activity.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108846"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145828928","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}
Pub Date : 2026-04-01Epub Date: 2025-12-17DOI: 10.1016/j.compbiolchem.2025.108851
Chetana Singh , Manish Gaur
Lung sound analysis is critical for diagnosing respiratory diseases such as asthma, bronchiectasis, bronchiolitis, COPD, LRTI, pneumonia, and URTI. Traditional diagnostic methods rely heavily on physicians’ expertise, making them time-consuming and subjective. To address these limitations, this study introduces a novel deep learning-based model, Bidirectional-Gated Recurrent Unit-Modified SqueezeNet (BGRMSNet), for automated lung sound detection and classification. The proposed approach consists of four key phases: preprocessing, feature extraction, data augmentation, and detection. In the preprocessing stage, a Threshold-based Wiener Filtering (T-WF) technique effectively removes impulse noise and outliers. The feature extraction phase captures comprehensive frequency-domain characteristics using permutation entropy, Modified Stockwell Transform (MST), Short-Time Fourier Transform (STFT), spectral centroid, and spectral rolloff. These features are further enhanced through random sampling-based data augmentation to improve model robustness.The detection phase employs the BGRMSNet architecture, which integrates Bidirectional Gated Recurrent Units (Bi-GRU) for modeling temporal dependencies and a Modified SqueezeNet (MSNet) for efficient feature extraction. MSNet incorporates enhancements including Improved Batch Normalization (IBN), multi-head attention, dropout, dense layers, and an improved exponential Softmax activation function. The combined architecture allows BGRMSNet to capture both temporal and spatial features effectively. Comprehensive evaluations, including ablation studies, statistical analysis, and k-fold cross-validation, demonstrate the model's high performance. The BGRMSNet model achieved an accuracy of 0.970, specificity of 0.987, and negative predictive value (NPV) of 0.972, outperforming conventional diagnostic approaches. These results highlight the potential of BGRMSNet as a robust and accurate tool for automated lung disease detection, supporting enhanced diagnostic decision-making in clinical environments.
{"title":"Automated lung sound detection via Bi-GRU-modified SqueezeNet architecture with new stock well feature set","authors":"Chetana Singh , Manish Gaur","doi":"10.1016/j.compbiolchem.2025.108851","DOIUrl":"10.1016/j.compbiolchem.2025.108851","url":null,"abstract":"<div><div>Lung sound analysis is critical for diagnosing respiratory diseases such as asthma, bronchiectasis, bronchiolitis, COPD, LRTI, pneumonia, and URTI. Traditional diagnostic methods rely heavily on physicians’ expertise, making them time-consuming and subjective. To address these limitations, this study introduces a novel deep learning-based model, Bidirectional-Gated Recurrent Unit-Modified SqueezeNet (BGRMSNet), for automated lung sound detection and classification. The proposed approach consists of four key phases: preprocessing, feature extraction, data augmentation, and detection. In the preprocessing stage, a Threshold-based Wiener Filtering (T-WF) technique effectively removes impulse noise and outliers. The feature extraction phase captures comprehensive frequency-domain characteristics using permutation entropy, Modified Stockwell Transform (MST), Short-Time Fourier Transform (STFT), spectral centroid, and spectral rolloff. These features are further enhanced through random sampling-based data augmentation to improve model robustness.The detection phase employs the BGRMSNet architecture, which integrates Bidirectional Gated Recurrent Units (Bi-GRU) for modeling temporal dependencies and a Modified SqueezeNet (MSNet) for efficient feature extraction. MSNet incorporates enhancements including Improved Batch Normalization (IBN), multi-head attention, dropout, dense layers, and an improved exponential Softmax activation function. The combined architecture allows BGRMSNet to capture both temporal and spatial features effectively. Comprehensive evaluations, including ablation studies, statistical analysis, and k-fold cross-validation, demonstrate the model's high performance. The BGRMSNet model achieved an accuracy of 0.970, specificity of 0.987, and negative predictive value (NPV) of 0.972, outperforming conventional diagnostic approaches. These results highlight the potential of BGRMSNet as a robust and accurate tool for automated lung disease detection, supporting enhanced diagnostic decision-making in clinical environments.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108851"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145828913","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}
Pub Date : 2026-04-01Epub Date: 2025-12-24DOI: 10.1016/j.compbiolchem.2025.108861
Debojyati Datta , Semanti Ghosh
Previous studies have concluded that miRNAs may be implicated in the pathogenesis of dengue, as upregulated miRNAs were observed in blood and serum samples from infected patients. These biomarkers for dengue infection are highly promising. Among these, microRNA-21 has emerged as a major candidate, although its role in the pathogenesis of dengue infection is not clear. In this study, we predicted the target genes of miR-21 using in silico approaches and modeled guide target duplexes docked to Argonaute protein to hypothesize potential engagement with the RISC in dengue. Potential miR-21 targets and their interacting proteins were identified from public databases. Binding affinities were estimated with the help of miRWalk and miRDB, and expression across the stages of dengue was analyzed based on UniProt. Three-dimensional models of miR-21-mRNA duplexes were derived by RNA Composer and then subjected to molecular docking experiments with AGO (PDB ID: 3F73). Among them, NUDT3, MYRF, and ZNRF1 showed the highest binding affinity and were selected for molecular characterization. The mode of AGO-mediated gene silencing was further explored computationally to assess its regulatory potential. Our findings showed good agreement with previously reported interactions of miR-21 and identified new associations that may contribute to dengue pathogenesis. These genes have strong links to the progression and prognosis of disease and, hence, may serve as a potential therapeutic target. This study supports the development of RNA interference-based strategies targeting the modulation of miR-21 activity for the treatment of dengue.
{"title":"Role of micro RNA 21 (miR-21) in dengue disease progression and cross talk with target proteins","authors":"Debojyati Datta , Semanti Ghosh","doi":"10.1016/j.compbiolchem.2025.108861","DOIUrl":"10.1016/j.compbiolchem.2025.108861","url":null,"abstract":"<div><div>Previous studies have concluded that miRNAs may be implicated in the pathogenesis of dengue, as upregulated miRNAs were observed in blood and serum samples from infected patients. These biomarkers for dengue infection are highly promising. Among these, microRNA-21 has emerged as a major candidate, although its role in the pathogenesis of dengue infection is not clear. In this study, we predicted the target genes of miR-21 using in silico approaches and modeled guide target duplexes docked to Argonaute protein to hypothesize potential engagement with the RISC in dengue. Potential miR-21 targets and their interacting proteins were identified from public databases. Binding affinities were estimated with the help of miRWalk and miRDB, and expression across the stages of dengue was analyzed based on UniProt. Three-dimensional models of miR-21-mRNA duplexes were derived by RNA Composer and then subjected to molecular docking experiments with AGO (PDB ID: 3F73). Among them, NUDT3, MYRF, and ZNRF1 showed the highest binding affinity and were selected for molecular characterization. The mode of AGO-mediated gene silencing was further explored computationally to assess its regulatory potential. Our findings showed good agreement with previously reported interactions of miR-21 and identified new associations that may contribute to dengue pathogenesis. These genes have strong links to the progression and prognosis of disease and, hence, may serve as a potential therapeutic target. This study supports the development of RNA interference-based strategies targeting the modulation of miR-21 activity for the treatment of dengue.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108861"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851681","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}
Pub Date : 2026-04-01Epub Date: 2025-11-25DOI: 10.1016/j.compbiolchem.2025.108795
Mohammadamin Madadi , Maryam Arabi , Ahmad Bereimipour
The tumor microenvironment (TME) is a complex interplay of immune, stromal, and malignant cells whose interactions shape cancer progression and therapeutic responses. In this study, we performed an integrative single-cell transcriptomic analysis to define cell-type–specific gene signatures with emphasis on immune–tumor communication, exhaustion states, and their prognostic implications. We derived 30-gene signatures for B cells, CD8⁺ T cells, fibroblasts, macrophages, NK cells, T cells, Tregs, tumor cells, and unclassified clusters. Ligand–receptor mapping revealed widespread communication, including macrophage–fibroblast and Treg–tumor axes. Pseudotime analysis further showed immune exhaustion as a dynamic process enriched with checkpoint genes such as PDCD1, CTLA4, LAG3, and TIGIT, particularly within Tregs and exhausted CD8⁺ T cells. Survival analysis of representative genes revealed contrasting effects of immune and stromal activity: MS4A1 (B-cell signature) and TPPP3 (tumor signature) correlated with improved prognosis, whereas fibroblast-specific COL1A1 predicted poor outcomes. Incorporation of less-characterized genes highlighted novel prognostic signals. Correlation networks among signatures underscored the functional interdependence of immune and stromal compartments. Together, this study provides a systems-level framework linking transcriptional with survival outcomes. By combining established immune checkpoints with novel candidates, our findings expand the biomarker landscape for prognostic stratification and therapeutic targeting in lung cancer.
{"title":"Cell-type specific gene signatures reveal novel immune checkpoints and prognostic markers in lung cancer","authors":"Mohammadamin Madadi , Maryam Arabi , Ahmad Bereimipour","doi":"10.1016/j.compbiolchem.2025.108795","DOIUrl":"10.1016/j.compbiolchem.2025.108795","url":null,"abstract":"<div><div>The tumor microenvironment (TME) is a complex interplay of immune, stromal, and malignant cells whose interactions shape cancer progression and therapeutic responses. In this study, we performed an integrative single-cell transcriptomic analysis to define cell-type–specific gene signatures with emphasis on immune–tumor communication, exhaustion states, and their prognostic implications. We derived 30-gene signatures for B cells, CD8⁺ T cells, fibroblasts, macrophages, NK cells, T cells, Tregs, tumor cells, and unclassified clusters. Ligand–receptor mapping revealed widespread communication, including macrophage–fibroblast and Treg–tumor axes. Pseudotime analysis further showed immune exhaustion as a dynamic process enriched with checkpoint genes such as PDCD1, CTLA4, LAG3, and TIGIT, particularly within Tregs and exhausted CD8⁺ T cells. Survival analysis of representative genes revealed contrasting effects of immune and stromal activity: MS4A1 (B-cell signature) and TPPP3 (tumor signature) correlated with improved prognosis, whereas fibroblast-specific COL1A1 predicted poor outcomes. Incorporation of less-characterized genes highlighted novel prognostic signals. Correlation networks among signatures underscored the functional interdependence of immune and stromal compartments. Together, this study provides a systems-level framework linking transcriptional with survival outcomes. By combining established immune checkpoints with novel candidates, our findings expand the biomarker landscape for prognostic stratification and therapeutic targeting in lung cancer.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108795"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679702","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}
Pub Date : 2026-04-01Epub Date: 2025-11-14DOI: 10.1016/j.compbiolchem.2025.108769
Nisha A , A. Anitha
Hyperspectral technology contains the most basic and accurate data in topography through taking hundreds of finely categorized spectral bands at the same time, which serves very useful places, like the surveillance of agriculture, geological exploration, and national security. For all efforts in or related to hyperspectral data analysis, the greatest efforts, indeed, go into image classification. Deep learning-based feature extraction frameworks thus exert their influence over several contemporary applications. This work presents a method for Hyperspectral Image Classification (HSIC) by merging deep learning models. Initially, band selection is performed by utilizing Double Exponential Smoothing-Artificial Flora Optimization (DES-AFO) algorithm by integration of Double Exponential smoothing (DES) in Artificial Flora Optimization (AFO). Then, feature engineering is done where, the feature extraction is done by Empirical wavelet transform (EWT), Convolutional Neural Network (CNN), together with the features extracted using ResNet50. Then, the dimension of the extracted features is reduced for computational efficiency and data compression using Canonical Correlation Analysis (CCA). Finally, classification is performed using Optimized Deep Convolutional Spectral-Spatial Attention Network (Opt Deep CSSAN), where Deep CSSAN is proposed by combining deep CNN and Spectral-Spatial Attention Network (SSAN). Moreover, proposed Deep CSSAN is trained using DES-AFO. Experimental evidence highlights that DES-AFO based Opt Deep CSSAN technique exhibited superior performance relative to standard methods with 96.9 % accuracy, 97.1 % of TPR, 95.8 % of Kappa, 96.9 % of TNR and 91.5 % of PPV.
高光谱技术通过同时采集数百个精细分类的光谱带，包含了最基本和最准确的地形数据，这在农业监测、地质勘探和国家安全等领域非常有用。在所有与高光谱数据分析相关的工作中，最大的努力确实是在图像分类方面。因此，基于深度学习的特征提取框架对几个当代应用产生了影响。本文提出了一种融合深度学习模型的高光谱图像分类（HSIC）方法。首先，利用双指数平滑-人工植物群优化（DES-AFO）算法对人工植物群优化（AFO）中的双指数平滑算法进行波段选择。然后进行特征工程，利用经验小波变换（Empirical wavelet transform， EWT）、卷积神经网络（Convolutional Neural Network， CNN）和ResNet50提取的特征进行特征提取。然后，使用典型相关分析（CCA）降低提取的特征的维数以提高计算效率和数据压缩。最后，使用优化深度卷积频谱空间注意网络（Opt Deep CSSAN）进行分类，其中Deep CSSAN是将深度CNN和频谱空间注意网络（SSAN）相结合而提出的。此外，所提出的深度CSSAN使用DES-AFO进行训练。实验证据表明，基于DES-AFO的Opt Deep CSSAN技术相对于标准方法表现出更高的性能，准确率为96.9% %，TPR为97.1 %，Kappa为95.8% %，TNR为96.9% %，PPV为91.5 %。
{"title":"Opt Deep CSSAN: Optimized Deep Convolutional Spectral-Spatial Attention Network for hyperspectral image classification","authors":"Nisha A , A. Anitha","doi":"10.1016/j.compbiolchem.2025.108769","DOIUrl":"10.1016/j.compbiolchem.2025.108769","url":null,"abstract":"<div><div>Hyperspectral technology contains the most basic and accurate data in topography through taking hundreds of finely categorized spectral bands at the same time, which serves very useful places, like the surveillance of agriculture, geological exploration, and national security. For all efforts in or related to hyperspectral data analysis, the greatest efforts, indeed, go into image classification. Deep learning-based feature extraction frameworks thus exert their influence over several contemporary applications. This work presents a method for Hyperspectral Image Classification (HSIC) by merging deep learning models. Initially, band selection is performed by utilizing Double Exponential Smoothing-Artificial Flora Optimization (DES-AFO) algorithm by integration of Double Exponential smoothing (DES) in Artificial Flora Optimization (AFO). Then, feature engineering is done where, the feature extraction is done by Empirical wavelet transform (EWT), Convolutional Neural Network (CNN), together with the features extracted using ResNet50. Then, the dimension of the extracted features is reduced for computational efficiency and data compression using Canonical Correlation Analysis (CCA). Finally, classification is performed using Optimized Deep Convolutional Spectral-Spatial Attention Network (Opt Deep CSSAN), where Deep CSSAN is proposed by combining deep CNN and Spectral-Spatial Attention Network (SSAN). Moreover, proposed Deep CSSAN is trained using DES-AFO. Experimental evidence highlights that DES-AFO based Opt Deep CSSAN technique exhibited superior performance relative to standard methods with 96.9 % accuracy, 97.1 % of TPR, 95.8 % of Kappa, 96.9 % of TNR and 91.5 % of PPV.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108769"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787162","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}
Pub Date : 2026-04-01Epub Date: 2026-01-01DOI: 10.1016/j.compbiolchem.2025.108875
Vipra Ajay Parekh , Musarat Amina , Md Lutful Islam , Pritee Chunarkar Patil , Mohammad Ajmal Ali , Saikh Mohammad Wabaidur , Md Ataul Islam
Phosphodiesterase (PDE) is a crucial enzyme that regulates intracellular signal transduction by breaking down cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) into inactive forms. Among the 11 PDE families, PDE10A has gained attention as a potential therapeutic target for neurodegenerative and psychiatric disorders. This study aimed to identify potent inhibitors targeting the active site of PDE10A. A ligand-guided virtual screening method was used to find potential modulators from the ZINCPharmer database. The ligand library was subjected to grid-based molecular docking using AutoDock Vina (ADV) and PLANTS tools. Absolute binding affinity was predicted and refined with KDEEP. The docking protocol was validated by evaluating ADMET properties of sorted compounds using ADMET-AI. Protein-ligand interactions were analyzed with ProteinPlus. The final four compounds ZINC09233950, ZINC19374064, ZINC33686121, and ZINC58090432 showed binding affinities of −9.1, −9.3, −9.7, and −9.3 kcal/mol, respectively. Molecular dynamics (MD) simulations were conducted over 100 ns to assess the stability of the protein-ligand complexes within a cubic water box. The binding free energies of selected compounds were evaluated using the MM-GBSA method, confirming their potential as PDE10A inhibitors. The study identified potential inhibitors and highlighted the value of a ligand-guided drug discovery approach in enhancing specificity and efficacy.
{"title":"Identification of phosphodiesterase 10 A modulators for neurodegenerative and psychiatric disorders: Combination of physics-based virtual screening and machine learning approaches","authors":"Vipra Ajay Parekh , Musarat Amina , Md Lutful Islam , Pritee Chunarkar Patil , Mohammad Ajmal Ali , Saikh Mohammad Wabaidur , Md Ataul Islam","doi":"10.1016/j.compbiolchem.2025.108875","DOIUrl":"10.1016/j.compbiolchem.2025.108875","url":null,"abstract":"<div><div>Phosphodiesterase (PDE) is a crucial enzyme that regulates intracellular signal transduction by breaking down cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) into inactive forms. Among the 11 PDE families, PDE10A has gained attention as a potential therapeutic target for neurodegenerative and psychiatric disorders. This study aimed to identify potent inhibitors targeting the active site of PDE10A. A ligand-guided virtual screening method was used to find potential modulators from the ZINCPharmer database. The ligand library was subjected to grid-based molecular docking using AutoDock Vina (ADV) and PLANTS tools. Absolute binding affinity was predicted and refined with KDEEP. The docking protocol was validated by evaluating ADMET properties of sorted compounds using ADMET-AI. Protein-ligand interactions were analyzed with ProteinPlus. The final four compounds ZINC09233950, ZINC19374064, ZINC33686121, and ZINC58090432 showed binding affinities of −9.1, −9.3, −9.7, and −9.3 kcal/mol, respectively. Molecular dynamics (MD) simulations were conducted over 100 ns to assess the stability of the protein-ligand complexes within a cubic water box. The binding free energies of selected compounds were evaluated using the MM-GBSA method, confirming their potential as PDE10A inhibitors. The study identified potential inhibitors and highlighted the value of a ligand-guided drug discovery approach in enhancing specificity and efficacy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108875"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880346","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}
This study explores the network pharmacology (NP) and molecular dynamics (MD) simulation analysis of pleural mesothelioma (PM) related enzymes. Through the investigation of 1253 associated genes, culminating in a protein-protein interaction (PPI) network constructed using the STRING database. Pathway analysis identified critical signaling pathways, including MAPK, PI3K/AKT, and RAS, associated with PM pathogenesis. Furthermore, we have synthesized plumbagin-indole-3-proponic acid (PLU-IPA) from plumbagin (PLU) and assessed the toxicity profiles of PLU and PLU-IPA, revealing a reduction in toxicity following IPA incorporation. MD simulations highlighted the stability of PLU-IPA complexes with various proteins (IL6, KRASG12D, SRC and TNFα), supported by analyses of root mean square deviation (RMSD), root mean square fluctuations (RMSF), clustering, and dynamic cross-correlation matrices (DCCM). Principal component analysis (PCA) assessment elucidated the conformational dynamics of the complexes. Additionally, MMGBSA and decomposition binding free energy calculations provided insights into the energetics of ligand binding. Notably, low-frequency mode analyses via Elastic Network Models (ENM) offered a comprehensive view of protein flexibility and ligand interactions. The prominent conformation modifications of each complex during MD simulation has been determined via Markov state model confirms the stability of PLU-IPA in the binding site. These findings underscore the intricate molecular mechanisms underlying PM and highlight PLU-IPA as a potential therapeutic target for future investigations.
{"title":"Targeting key proteins of pleural mesothelioma using plumbagin-indole-3-propionic acid ester: Insights from network pharmacology, molecular dynamics simulation and machine learning-based analysis","authors":"Binjawhar Dalal Nasser , Chitra Loganathan , Revathi Ramalingam , Ancy Iruthayaraj","doi":"10.1016/j.compbiolchem.2025.108871","DOIUrl":"10.1016/j.compbiolchem.2025.108871","url":null,"abstract":"<div><div>This study explores the network pharmacology (NP) and molecular dynamics (MD) simulation analysis of pleural mesothelioma (PM) related enzymes. Through the investigation of 1253 associated genes, culminating in a protein-protein interaction (PPI) network constructed using the STRING database. Pathway analysis identified critical signaling pathways, including MAPK, PI3K/AKT, and RAS, associated with PM pathogenesis. Furthermore, we have synthesized plumbagin-indole-3-proponic acid (PLU-IPA) from plumbagin (PLU) and assessed the toxicity profiles of PLU and PLU-IPA, revealing a reduction in toxicity following IPA incorporation. MD simulations highlighted the stability of PLU-IPA complexes with various proteins (IL6, KRAS<sup>G12D</sup>, SRC and TNFα), supported by analyses of root mean square deviation (RMSD), root mean square fluctuations (RMSF), clustering, and dynamic cross-correlation matrices (DCCM). Principal component analysis (PCA) assessment elucidated the conformational dynamics of the complexes. Additionally, MMGBSA and decomposition binding free energy calculations provided insights into the energetics of ligand binding. Notably, low-frequency mode analyses via Elastic Network Models (ENM) offered a comprehensive view of protein flexibility and ligand interactions. The prominent conformation modifications of each complex during MD simulation has been determined via Markov state model confirms the stability of PLU-IPA in the binding site. These findings underscore the intricate molecular mechanisms underlying PM and highlight PLU-IPA as a potential therapeutic target for future investigations.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108871"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880349","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}
Pub Date : 2026-04-01Epub Date: 2025-12-11DOI: 10.1016/j.compbiolchem.2025.108840
Ahmet Efe Köseoğlu , Gülsüm Deniz Köseoğlu , Buminhan Özgültekin , Bilge İrem Göç , Sabina Neziri , Nehir Özdemir Özgentürk
Foxp2 is a transcription factor containing poly-Q repeats, commonly found in brain proteins. It plays essential roles in speech, motor function, cognition, and emotion, and is expressed during embryonic development in the brain, lungs, heart, and intestines. Foxp2 is highly conserved among vertebrates. This study investigated the molecular evolution of Foxp2 by analyzing its sequence, structure, post-translational modifications (N-glycosylation and phosphorylation), positive selection signals, conserved motifs, domains, and interaction networks across five representative vertebrates: human, cattle, coelacanth, zebrafish, and pufferfish. Structural comparison showed closer similarity among human, cattle, and coelacanth, with a poly-Q tract absent in zebrafish and pufferfish. A unique 25-amino acid insertion was identified only in cattle. Two conserved domains were found in all species, while one domain was restricted to human, cattle, and coelacanth. Of 37 predicted motifs, motifs 30–37 associated with poly-Q repeats were exclusive to human, cattle, and coelacanth. Poly-Q tracts are notable due to their links with neurodegenerative disorders such as prion diseases and Huntington’s disease. Two distinct N-glycosylation profiles emerged: one shared by human, cattle, and coelacanth, and another by zebrafish and pufferfish. Protein interaction analysis consistently identified Ctb1, Nfatc2, and Tbr1 as partners. Phylogenetic analysis placed coelacanth closer to the human/cattle clade than to teleosts, reflecting its transitional evolutionary status. Together, these integrative bioinformatics results provide new insights into the molecular evolution of Foxp2, highlighting the evolutionary position of coelacanth and the functional relevance of poly-Q repeats in vertebrates.
{"title":"Tracing the molecular evolution of Foxp2 proteins in vertebrates from fish to tetrapods: Insights into poly-Q tract variation, structural changes, and interaction networks","authors":"Ahmet Efe Köseoğlu , Gülsüm Deniz Köseoğlu , Buminhan Özgültekin , Bilge İrem Göç , Sabina Neziri , Nehir Özdemir Özgentürk","doi":"10.1016/j.compbiolchem.2025.108840","DOIUrl":"10.1016/j.compbiolchem.2025.108840","url":null,"abstract":"<div><div>Foxp2 is a transcription factor containing poly-Q repeats, commonly found in brain proteins. It plays essential roles in speech, motor function, cognition, and emotion, and is expressed during embryonic development in the brain, lungs, heart, and intestines. Foxp2 is highly conserved among vertebrates. This study investigated the molecular evolution of Foxp2 by analyzing its sequence, structure, post-translational modifications (N-glycosylation and phosphorylation), positive selection signals, conserved motifs, domains, and interaction networks across five representative vertebrates: human, cattle, coelacanth, zebrafish, and pufferfish. Structural comparison showed closer similarity among human, cattle, and coelacanth, with a poly-Q tract absent in zebrafish and pufferfish. A unique 25-amino acid insertion was identified only in cattle. Two conserved domains were found in all species, while one domain was restricted to human, cattle, and coelacanth. Of 37 predicted motifs, motifs 30–37 associated with poly-Q repeats were exclusive to human, cattle, and coelacanth. Poly-Q tracts are notable due to their links with neurodegenerative disorders such as prion diseases and Huntington’s disease. Two distinct N-glycosylation profiles emerged: one shared by human, cattle, and coelacanth, and another by zebrafish and pufferfish. Protein interaction analysis consistently identified Ctb1, Nfatc2, and Tbr1 as partners. Phylogenetic analysis placed coelacanth closer to the human/cattle clade than to teleosts, reflecting its transitional evolutionary status. Together, these integrative bioinformatics results provide new insights into the molecular evolution of Foxp2, highlighting the evolutionary position of coelacanth and the functional relevance of poly-Q repeats in vertebrates.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108840"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734059","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}
Spinocerebellar ataxias (SCAs) are progressive neurodegenerative disorders caused by abnormal CAG repeat expansions in genes such as ATXN1, ATXN2, and ATXN3, with no effective therapeutic options currently available. To identify key molecular drivers of disease pathology, transcriptomic datasets GSE75249 and GSE151276 were analyzed. Differentially expressed genes were determined, and overlapping upregulated genes from both datasets were extracted for downstream analysis. Functional enrichment revealed significant biological processes and pathways related to protein homeostasis, cellular stress response, and neurodegeneration. Protein–protein interaction networks were constructed to investigate gene connectivity, and hub gene analysis identified Valosin-Containing Protein (VCP) as the top-ranked common hub gene. Importantly, VCP expression was experimentally validated in plasma samples from SCA1, SCA2, and SCA3 patients using ELISA, confirming its dysregulation and central role in SCA pathogenesis. To explore therapeutic potential, pharmacophore-based virtual screening of natural compounds was conducted, followed by molecular docking to evaluate interactions with VCP. Among the shortlisted candidates, sesamolin demonstrated the strongest binding affinity and favorable pharmacological properties. A 250 ns molecular dynamics simulation further confirmed the stability of the VCP–sesamolin complex, revealing sustained interactions, reduced fluctuations, and conformational stabilization of VCP. Collectively, this integrative approach combining transcriptomic profiling, enrichment analysis, hub gene identification, experimental validation, and structure-based drug discovery highlights VCP as a crucial regulator in CAG repeat–associated SCAs and proposes sesamolin as a promising neuroprotective lead compound for further preclinical development.
脊髓小脑共济失调（SCAs）是由ATXN1、ATXN2和ATXN3等基因CAG重复扩增异常引起的进行性神经退行性疾病，目前尚无有效的治疗方案。为了确定疾病病理的关键分子驱动因素，对转录组数据集GSE75249和GSE151276进行了分析。确定差异表达基因，并从两个数据集中提取重叠的上调基因进行下游分析。功能富集揭示了与蛋白质稳态、细胞应激反应和神经变性相关的重要生物学过程和途径。通过构建蛋白-蛋白互作网络来研究基因连通性，hub基因分析发现Valosin-Containing Protein （VCP）是最常见的hub基因。重要的是，通过ELISA实验验证了SCA1、SCA2和SCA3患者血浆样本中VCP的表达，证实了其失调和在SCA发病机制中的核心作用。为了探索天然化合物的治疗潜力，我们进行了基于药物团的虚拟筛选，然后进行了分子对接以评估与VCP的相互作用。在候选药物中，芝麻素表现出最强的结合亲和力和良好的药理特性。250 ns分子动力学模拟进一步证实了VCP-芝麻素复合物的稳定性，揭示了VCP的持续相互作用、波动减少和构象稳定。总的来说，这种结合转录组分析、富集分析、中心基因鉴定、实验验证和基于结构的药物发现的综合方法强调了VCP是CAG重复相关SCAs的关键调节因子，并提出芝麻素是一种有前途的神经保护先导化合物，可用于进一步的临床前开发。
{"title":"Valosin-Containing Protein as a therapeutic target in CAG repeat–driven Spinocerebellar ataxias: Integrative transcriptomic and computational insights","authors":"Surbhi Singh , Deepika Joshi , Janki Makani , Suchitra Singh , Janhavi Yadav , Shraddha Chaurasiya , Chandmayee Mohanty , Anand Kumar , Royana Singh","doi":"10.1016/j.compbiolchem.2025.108838","DOIUrl":"10.1016/j.compbiolchem.2025.108838","url":null,"abstract":"<div><div>Spinocerebellar ataxias (SCAs) are progressive neurodegenerative disorders caused by abnormal CAG repeat expansions in genes such as ATXN1, ATXN2, and ATXN3, with no effective therapeutic options currently available. To identify key molecular drivers of disease pathology, transcriptomic datasets GSE75249 and GSE151276 were analyzed. Differentially expressed genes were determined, and overlapping upregulated genes from both datasets were extracted for downstream analysis. Functional enrichment revealed significant biological processes and pathways related to protein homeostasis, cellular stress response, and neurodegeneration. Protein–protein interaction networks were constructed to investigate gene connectivity, and hub gene analysis identified Valosin-Containing Protein (VCP) as the top-ranked common hub gene. Importantly, VCP expression was experimentally validated in plasma samples from SCA1, SCA2, and SCA3 patients using ELISA, confirming its dysregulation and central role in SCA pathogenesis. To explore therapeutic potential, pharmacophore-based virtual screening of natural compounds was conducted, followed by molecular docking to evaluate interactions with VCP. Among the shortlisted candidates, sesamolin demonstrated the strongest binding affinity and favorable pharmacological properties. A 250 ns molecular dynamics simulation further confirmed the stability of the VCP–sesamolin complex, revealing sustained interactions, reduced fluctuations, and conformational stabilization of VCP. Collectively, this integrative approach combining transcriptomic profiling, enrichment analysis, hub gene identification, experimental validation, and structure-based drug discovery highlights VCP as a crucial regulator in CAG repeat–associated SCAs and proposes sesamolin as a promising neuroprotective lead compound for further preclinical development.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"121 ","pages":"Article 108838"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822416","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}
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