Computational Biology and Chemistry最新文献_第2页

DisSubFormer: A subgraph transformer model for disease subgraph representation and comorbidity prediction DisSubFormer：一个用于疾病子图表示和共病预测的子图转换器模型。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-02-03 DOI: 10.1016/j.compbiolchem.2026.108935

Ashwag Altayyar, Li Liao

Disease comorbidity—the co-occurrence of multiple diseases in the same individual—is increasingly prevalent and poses major clinical and biological challenges. Computational approaches for studying disease relationships and predicting comorbidity have evolved from overlap-based similarity measures to molecular network modeling and graph deep learning. However, existing methods often (i) learn global or subgraph-based disease embeddings without modeling the topology of fragmented disease subgraphs in a comorbidity-adaptive manner, or (ii) incorporate Gene Ontology (GO) information in ways that underutilize GO’s hierarchical ancestry and deeper functional abstractions. In this work, we propose DisSubFormer, a subgraph Transformer model for disease subgraph representation learning and comorbidity prediction. We first learn unified protein representations by integrating structural patterns from a PPI network with GO-aware functional information, explicitly incorporating GO’s hierarchical ancestry. We next sample biologically informed anchor patches in a property-aware manner to prioritize disease-relevant regions of the PPI network, replacing full-graph attention with subgraph-to-subgraph attention between disease subgraphs and these anchor patches to improve scalability and relevance. Specifically, DisSubFormer introduces a learnable multi-head attention mechanism where each head attends over a distinct anchor-patch type, with head-specific relational terms to capture complementary positional, neighborhood, and structural properties within fragmented disease subgraphs for comorbidity prediction. Experiments on a benchmark comorbidity dataset demonstrate that DisSubFormer consistently outperforms state-of-the-art methods, achieving an AUROC of 0.97.

疾病合并症——同一个体同时出现多种疾病——越来越普遍，并带来了重大的临床和生物学挑战。研究疾病关系和预测共病的计算方法已经从基于重叠的相似性度量发展到分子网络建模和图深度学习。然而，现有的方法通常(i)学习全局或基于子图的疾病嵌入，而没有以共病自适应的方式对碎片化疾病子图的拓扑进行建模，或者（ii）以未充分利用GO的层次祖先和更深的功能抽象的方式合并基因本体（GO）信息。在这项工作中，我们提出了DisSubFormer，一个用于疾病子图表示学习和共病预测的子图转换器模型。我们首先通过整合来自PPI网络的结构模式和氧化石墨烯感知功能信息来学习统一的蛋白质表示，明确地结合氧化石墨烯的等级祖先。接下来，我们以一种属性感知的方式对生物学信息锚定补丁进行采样，优先考虑PPI网络中与疾病相关的区域，用疾病子图和锚定补丁之间的子图对子图的关注取代全图关注，以提高可扩展性和相关性。具体来说，DisSubFormer引入了一种可学习的多头注意机制，其中每个头部都关注一个不同的锚点补丁类型，使用特定于头部的关系术语来捕获碎片化疾病子图中互补的位置、邻域和结构属性，以预测共病。在一个基准共病数据集上的实验表明，DisSubFormer始终优于最先进的方法，达到了0.97的AUROC。

{"title":"DisSubFormer: A subgraph transformer model for disease subgraph representation and comorbidity prediction","authors":"Ashwag Altayyar, Li Liao","doi":"10.1016/j.compbiolchem.2026.108935","DOIUrl":"10.1016/j.compbiolchem.2026.108935","url":null,"abstract":"<div><div>Disease comorbidity—the co-occurrence of multiple diseases in the same individual—is increasingly prevalent and poses major clinical and biological challenges. Computational approaches for studying disease relationships and predicting comorbidity have evolved from overlap-based similarity measures to molecular network modeling and graph deep learning. However, existing methods often (i) learn global or subgraph-based disease embeddings without modeling the topology of fragmented disease subgraphs in a comorbidity-adaptive manner, or (ii) incorporate Gene Ontology (GO) information in ways that underutilize GO’s hierarchical ancestry and deeper functional abstractions. In this work, we propose DisSubFormer, a subgraph Transformer model for disease subgraph representation learning and comorbidity prediction. We first learn unified protein representations by integrating structural patterns from a PPI network with GO-aware functional information, explicitly incorporating GO’s hierarchical ancestry. We next sample biologically informed anchor patches in a property-aware manner to prioritize disease-relevant regions of the PPI network, replacing full-graph attention with subgraph-to-subgraph attention between disease subgraphs and these anchor patches to improve scalability and relevance. Specifically, DisSubFormer introduces a learnable multi-head attention mechanism where each head attends over a distinct anchor-patch type, with head-specific relational terms to capture complementary positional, neighborhood, and structural properties within fragmented disease subgraphs for comorbidity prediction. Experiments on a benchmark comorbidity dataset demonstrate that DisSubFormer consistently outperforms state-of-the-art methods, achieving an AUROC of 0.97.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108935"},"PeriodicalIF":3.1,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144430","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

Repurposing sulfonamide drugs as anticancer ligands and understanding its properties through density functional theory 利用密度泛函理论研究磺胺类药物抗癌配体的特性。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-02-01 DOI: 10.1016/j.compbiolchem.2026.108933

Palanisamy Deepa , Balasubramanian Sundarakannan , Duraisamy Thirumeignanam

Drug repurposing represents a promising approach towards drug discovery that has the potential to improve patient outcomes and address unmet medical needs. This study attempted to repurpose existing sulfonamide drugs in search of novel anticancer drugs because of their effectiveness in treating bacterial infections. A search was made in DrugBank for Sulfonamide, and 25 drugs with functional groups like SH, OSO, CS, and -S- were chosen for our study. The drug properties, such as dipole moment, volume, polarisability, highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and electrostatic potential map, were analysed through a quantum mechanical approach at different functionals: M062X, M06HF, and B3LYP with basis sets (6–31 +G*, LANL2DZ). The electrostatic potential map was analyzed to determine the magnitude, size, and distribution of the electron cloud surrounding the sulfur atoms. Analysis of NBO (Natural Bond Orbital) and NCI (Non-Covalent Interaction) plots confirmed the presence of intramolecular hydrogen bonding in the sulfonamide drugs. Furthermore, the frontier molecular orbitals (HOMO and LUMO) and the band gap were thoroughly examined for all drugs to identify the best electron acceptors and donors. Docking analysis was performed to have a lock-and-key model of 25 sulfonamide drugs with the most promising cancer-targeted protein (1ZZ1): histone deacetylases (HDACs). The best drug orientation (optimal position) was discussed and compared with the control ligand SHH based on the analysis of binding affinity and root mean square deviation (RMSD). Binding affinity of control ligand SHH is −8.1 kcal/mol for the 2nd pose, which matches exactly with 1ZZ1 SHH ligand. The drugs Tolazamide, Fezolinetant, Ensulizole, Taurolidine, Acetohexamide, Isoxicam, Sulfamethizole, Sulfamethoxazole, Sulfapyridine, Sulfaphenazole, and Dodecyl sulphate were observed to exhibit high molecular volume, polarizability, dipole moment and significant HOMO, LUMO values, which are recommended for further quantum mechanical calculations. The findings of this study will be essential for evaluating the properties of sulfonamide drugs from a drugbank using a variety of analyses in order to repurpose them as novel anticancer drugs. Quantum mechanical calculations will be performed on the optimal docking poses in future work. Keywords: Sulfonamide drugs, Docking, Histone deacetylases, Lipinsk’s rule, Binding affinity

药物再利用是一种很有前途的药物发现方法，有可能改善患者的治疗效果并解决未满足的医疗需求。由于磺胺类药物在治疗细菌感染方面的有效性，本研究试图重新利用现有的磺胺类药物来寻找新的抗癌药物。我们在DrugBank中检索了磺胺类药物，选取了含有SH、OSO、CS、- s -等功能基团的25种药物作为研究对象。利用量子力学方法分析了M062X、M06HF和B3LYP不同官能团（6-31 +G*, LANL2DZ）上的偶极矩、体积、极化率、最高占据分子轨道（HOMO）、最低未占据分子轨道（LUMO）和静电势图等药物性质。分析静电势图以确定硫原子周围电子云的大小、大小和分布。NBO（天然键轨道）和NCI（非共价相互作用）图的分析证实了磺胺类药物分子内氢键的存在。此外，对所有药物的前沿分子轨道（HOMO和LUMO）和带隙进行了彻底的检查，以确定最佳的电子受体和给体。对接分析25种磺胺类药物与最有希望的癌症靶向蛋白（1ZZ1）：组蛋白去乙酰化酶（hdac）建立锁-钥匙模型。通过结合亲和力和均方根偏差（RMSD）分析，讨论了最佳药物取向（最佳位置），并与对照配体SHH进行了比较。控制配体SHH第二位姿的结合亲和力为-8.1 kcal/mol，与1ZZ1 SHH配体完全匹配。药物Tolazamide、Fezolinetant、ensullizole、taaurolidine、Acetohexamide、Isoxicam、sulfameethizole、Sulfamethoxazole、Sulfapyridine、Sulfaphenazole和Dodecyl sulphate表现出较高的分子体积、极化率、偶极矩和显著的HOMO、LUMO值，建议进一步进行量子力学计算。本研究的发现对于利用各种分析方法评估药库中磺胺类药物的特性，以便将其重新用作新型抗癌药物至关重要。在未来的工作中，将对最佳对接姿态进行量子力学计算。关键词：磺胺类药物，对接，组蛋白去乙酰化酶，利平斯克规则，结合亲和力

{"title":"Repurposing sulfonamide drugs as anticancer ligands and understanding its properties through density functional theory","authors":"Palanisamy Deepa , Balasubramanian Sundarakannan , Duraisamy Thirumeignanam","doi":"10.1016/j.compbiolchem.2026.108933","DOIUrl":"10.1016/j.compbiolchem.2026.108933","url":null,"abstract":"<div><div>Drug repurposing represents a promising approach towards drug discovery that has the potential to improve patient outcomes and address unmet medical needs. This study attempted to repurpose existing sulfonamide drugs in search of novel anticancer drugs because of their effectiveness in treating bacterial infections. A search was made in DrugBank for Sulfonamide, and 25 drugs with functional groups like SH, O<img>S<img>O, C<img>S, and -S- were chosen for our study. The drug properties, such as dipole moment, volume, polarisability, highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and electrostatic potential map, were analysed through a quantum mechanical approach at different functionals: M062X, M06HF, and B3LYP with basis sets (6–31 +G*, LANL2DZ). The electrostatic potential map was analyzed to determine the magnitude, size, and distribution of the electron cloud surrounding the sulfur atoms. Analysis of NBO (Natural Bond Orbital) and NCI (Non-Covalent Interaction) plots confirmed the presence of intramolecular hydrogen bonding in the sulfonamide drugs. Furthermore, the frontier molecular orbitals (HOMO and LUMO) and the band gap were thoroughly examined for all drugs to identify the best electron acceptors and donors. Docking analysis was performed to have a lock-and-key model of 25 sulfonamide drugs with the most promising cancer-targeted protein (1ZZ1): histone deacetylases (HDACs). The best drug orientation (optimal position) was discussed and compared with the control ligand SHH based on the analysis of binding affinity and root mean square deviation (RMSD). Binding affinity of control ligand SHH is −8.1 kcal/mol for the 2nd pose, which matches exactly with 1ZZ1 SHH ligand. The drugs Tolazamide, Fezolinetant, Ensulizole, Taurolidine, Acetohexamide, Isoxicam, Sulfamethizole, Sulfamethoxazole, Sulfapyridine, Sulfaphenazole, and Dodecyl sulphate were observed to exhibit high molecular volume, polarizability, dipole moment and significant HOMO, LUMO values, which are recommended for further quantum mechanical calculations. The findings of this study will be essential for evaluating the properties of sulfonamide drugs from a drugbank using a variety of analyses in order to repurpose them as novel anticancer drugs. Quantum mechanical calculations will be performed on the optimal docking poses in future work. <strong>Keywords:</strong> Sulfonamide drugs, Docking, Histone deacetylases, Lipinsk’s rule, Binding affinity</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108933"},"PeriodicalIF":3.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121383","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

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers MMRCL：用于预测hERG阻滞剂的可解释多模态深度学习框架。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-31 DOI: 10.1016/j.compbiolchem.2026.108926

Yang Su , Jinzhou Wu , Ao Yang , Yumin Yuan , Wenli Du , Yi Xiang , Weifeng Shen

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-induced hERG inhibition can prolong the QT interval, causing severe heart diseases like torsade de pointes and fatal arrhythmias. In pharmaceutical chemistry, early prediction of hERG blockers is crucial to mitigate cardiotoxicity risks, minimizing drug withdrawals and economic losses in discovery. To address this, an interpretable multi-modal molecular representation cross-learning framework (MMRCL) is developed, integrating multi-dimensional molecular fingerprints and molecular graphs to enrich structural features. MMRCL combines a dual-channel message passing neural network (MPNN) for atom- and bond-level structural features with a multi-layer perceptron for molecular fingerprint-based semantics. A multi-head cross-attention mechanism adaptively fuses features across modalities, enabling deep correlation modeling, followed by a fully connected neural network classifier. Extensive evaluation on an internal dataset (12,518 compounds with high-dimensional fingerprints and graph features) and three external test sets demonstrates MMRCL's superior performance compared to seven state-of-the-art baseline models, achieving the best AUC of 0.8895, PRC of 0.9073, and MCC of 0.6146 on the internal set. Interpretability analysis identifies key toxic substructures linked to hERG-blocking activity, aiding structure-activity relationship exploration. Ablation studies further confirm the contributions of multi-modal input and attention-based fusion. MMRCL achieves superior prediction accuracy and generalization, also enhances model interpretability, providing actionable insights for medicinal chemists.

人类以太相关基因（hERG）编码对心脏动作电位复极至关重要的电压门控钾通道。药物诱导的hERG抑制可延长QT间期，引起点扭转和致死性心律失常等严重心脏病。在药物化学中，早期预测hERG阻滞剂对于降低心脏毒性风险、最大限度地减少药物停药和发现过程中的经济损失至关重要。为了解决这个问题，开发了一个可解释的多模态分子表示交叉学习框架（MMRCL），将多维分子指纹和分子图谱结合起来，丰富了结构特征。MMRCL结合了用于原子和键级结构特征的双通道消息传递神经网络（MPNN）和用于基于分子指纹语义的多层感知器。一个多头交叉注意机制自适应融合跨模式的特征，实现深度关联建模，然后是一个完全连接的神经网络分类器。在内部数据集（12,518种具有高维指纹图谱和图形特征的化合物）和3个外部测试集上进行的广泛评估表明，与7个最先进的基线模型相比，MMRCL的性能优越，在内部数据集上实现了最佳AUC为0.8895，PRC为0.9073，MCC为0.6146。可解释性分析确定了与heg阻断活性相关的关键毒性亚结构，有助于探索结构-活性关系。消融研究进一步证实了多模态输入和基于注意的融合的贡献。MMRCL具有较高的预测精度和泛化能力，增强了模型的可解释性，为药物化学家提供了可操作的见解。

{"title":"MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers","authors":"Yang Su , Jinzhou Wu , Ao Yang , Yumin Yuan , Wenli Du , Yi Xiang , Weifeng Shen","doi":"10.1016/j.compbiolchem.2026.108926","DOIUrl":"10.1016/j.compbiolchem.2026.108926","url":null,"abstract":"<div><div>The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-induced hERG inhibition can prolong the QT interval, causing severe heart diseases like torsade de pointes and fatal arrhythmias. In pharmaceutical chemistry, early prediction of hERG blockers is crucial to mitigate cardiotoxicity risks, minimizing drug withdrawals and economic losses in discovery. To address this, an interpretable multi-modal molecular representation cross-learning framework (MMRCL) is developed, integrating multi-dimensional molecular fingerprints and molecular graphs to enrich structural features. MMRCL combines a dual-channel message passing neural network (MPNN) for atom- and bond-level structural features with a multi-layer perceptron for molecular fingerprint-based semantics. A multi-head cross-attention mechanism adaptively fuses features across modalities, enabling deep correlation modeling, followed by a fully connected neural network classifier. Extensive evaluation on an internal dataset (12,518 compounds with high-dimensional fingerprints and graph features) and three external test sets demonstrates MMRCL's superior performance compared to seven state-of-the-art baseline models, achieving the best AUC of 0.8895, PRC of 0.9073, and MCC of 0.6146 on the internal set. Interpretability analysis identifies key toxic substructures linked to hERG-blocking activity, aiding structure-activity relationship exploration. Ablation studies further confirm the contributions of multi-modal input and attention-based fusion. MMRCL achieves superior prediction accuracy and generalization, also enhances model interpretability, providing actionable insights for medicinal chemists.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108926"},"PeriodicalIF":3.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115029","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 screening of natural plant and marine compounds as potential inhibitors of Mycobacterium tuberculosis dihydrodipicolinate synthase 天然植物和海洋化合物作为结核分枝杆菌二氢二吡啶合酶潜在抑制剂的计算筛选。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-31 DOI: 10.1016/j.compbiolchem.2026.108936

Swati Meena , Firdaus Fatima , Faizan Abul Qais , Srinivasan Ramachandran

The escalating threat of antimicrobial resistance (AMR) in Mycobacterium tuberculosis highlights the urgent need for innovative therapeutic strategies through searching for novel inhibitor molecules. Plant and marine habitats are rich reservoirs of bioactive compounds. Targeting critical pathways for M. tuberculosis survival, such as cell wall biosynthesis, offers a promising approach for drug discovery. Diaminopimelate, a critical component of the bacterial cell wall, is synthesized through the lysine biosynthesis pathway. Dihydrodipicolinate synthase (Mtb-DapA) is a promising drug target in this pathway. We screened antitubercular natural and marine-derived compounds against Mtb-DapA using molecular docking, molecular dynamics (MD) simulations, Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) analysis was performed to estimate binding free energies and identify promising inhibitors. In this study, we analysed 633 phytochemicals from the BioPhytMol Database, 210 anti-TB phytochemicals from recent literature, and 406 marine habitat-derived anti-TB compounds. We report top three inhibitors glycyrrhizin, micromeline and lico-isoflavone based on MD simulations and MM-PBSA analysis. Binding free energies of glycyrrhizin, micromeline and lico-isoflavone were −50.39 kcal/mol, −17.90 kcal/mol, −17.88 kcal/mol respectively as revealed through MM-PBSA analysis. Glycyrrhizin emerged as the most potent inhibitor. These findings underscore the therapeutic potential of glycyrrhizin, micromeline and lico-isoflavone as promising candidates for further development thereby offering hope for alternative treatments against M. tuberculosis.

结核分枝杆菌抗微生物药物耐药性（AMR）的威胁日益加剧，迫切需要通过寻找新的抑制剂分子来创新治疗策略。植物和海洋栖息地是生物活性化合物的丰富储存库。靶向结核分枝杆菌生存的关键途径，如细胞壁生物合成，为药物发现提供了一种有希望的方法。二氨基苯甲酸酯是细菌细胞壁的重要组成部分，通过赖氨酸生物合成途径合成。二氢二吡啶酸合成酶（Mtb-DapA）是该途径中一个很有前景的药物靶点。我们通过分子对接、分子动力学（MD）模拟、分子力学泊松-玻尔兹曼表面积（MM-PBSA）分析来估计结合自由能并确定有希望的抑制剂，筛选抗结核天然和海洋来源的Mtb-DapA化合物。在这项研究中，我们分析了来自BioPhytMol数据库的633种植物化学物质，来自最新文献的210种抗结核植物化学物质，以及406种来自海洋栖息地的抗结核化合物。基于MD模拟和MM-PBSA分析，我们报道了前三名的抑制剂甘草酸苷、微碱和甘草异黄酮。MM-PBSA分析结果显示，甘草酸、微量线和甘草异黄酮的结合自由能分别为-50.39 kcal/mol、-17.90 kcal/mol和-17.88 kcal/mol。甘草酸是最有效的抑制剂。这些发现强调了甘草酸、微碱和甘草异黄酮作为进一步开发的有希望的候选药物的治疗潜力，从而为替代治疗结核分枝杆菌提供了希望。

{"title":"Computational screening of natural plant and marine compounds as potential inhibitors of Mycobacterium tuberculosis dihydrodipicolinate synthase","authors":"Swati Meena , Firdaus Fatima , Faizan Abul Qais , Srinivasan Ramachandran","doi":"10.1016/j.compbiolchem.2026.108936","DOIUrl":"10.1016/j.compbiolchem.2026.108936","url":null,"abstract":"<div><div>The escalating threat of antimicrobial resistance (AMR) in <em>Mycobacterium tuberculosis</em> highlights the urgent need for innovative therapeutic strategies through searching for novel inhibitor molecules. Plant and marine habitats are rich reservoirs of bioactive compounds. Targeting critical pathways for <em>M. tuberculosis</em> survival, such as cell wall biosynthesis, offers a promising approach for drug discovery. Diaminopimelate, a critical component of the bacterial cell wall, is synthesized through the lysine biosynthesis pathway. Dihydrodipicolinate synthase (Mtb-DapA) is a promising drug target in this pathway. We screened antitubercular natural and marine-derived compounds against Mtb-DapA using molecular docking, molecular dynamics (MD) simulations, Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) analysis was performed to estimate binding free energies and identify promising inhibitors. In this study, we analysed 633 phytochemicals from the BioPhytMol Database, 210 anti-TB phytochemicals from recent literature, and 406 marine habitat-derived anti-TB compounds. We report top three inhibitors glycyrrhizin, micromeline and lico-isoflavone based on MD simulations and MM-PBSA analysis. Binding free energies of glycyrrhizin, micromeline and lico-isoflavone were −50.39 kcal/mol, −17.90 kcal/mol, −17.88 kcal/mol respectively as revealed through MM-PBSA analysis. Glycyrrhizin emerged as the most potent inhibitor. These findings underscore the therapeutic potential of glycyrrhizin, micromeline and lico-isoflavone as promising candidates for further development thereby offering hope for alternative treatments against <em>M. tuberculosis</em>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108936"},"PeriodicalIF":3.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168643","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 meta-heuristic aided arrhythmia classification model using advanced deep learning technique with multiple feature extraction mechanisms 基于多特征提取机制的高级深度学习元启发式心律失常分类模型

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-30 DOI: 10.1016/j.compbiolchem.2026.108917

Jay Raval , Kamalesh V.N. , Dr. Raj Kumar Patra

Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the identification of arrhythmias; yet, most of the techniques have been utilized sources such as Electrocardiogram (ECG). The ECG-based manual evaluation by the medical analysts is inaccurate. Some experiments have been concentrated on the accuracy and the speed of the learning method by utilizing Artificial Intelligence (AI), and pattern detection in the classification model. However, there are two primary limitations in the conventional mechanisms; the models demand large training time and demand feature selection on a manual basis. Hence, an intellectual arrhythmia classification model using deep learning is introduced to identify the irregular heartbeat. In the beginning, the required signals are accumulated from standard sources. Further, three different kinds of features are extracted for an efficient automatic classification process of arrhythmia. At first, the deep features are extracted by applying the Conditional Autoencoder, and these features are considered as feature set 1. Further, wave features and spectral features are retrieved from the input signal and these features are considered as feature set 2. Subsequently, the signals are converted into spectrogram images and the Graph Convolutional Neural Network (GCNN) technique is employed to retrieve the feature set 3 from those images. Further, the ensemble feature fusion process takes place to combine all three sets of features. Ensemble features are provided as input for the Optimal Dense Recurrent neural network with Attention Mechanism (ODR-AM) for classifying the arrhythmia. The classifier’s performance is boosted by optimizing the parameters using the Augmented Random value of Giant Armadillo Optimization (ARGAO). This model is useful to know about the specific type of arrhythmia. Finally, the simulation findings of the presented model are analyzed with other conventional models.

心律失常严重威胁着人类的生命安全；因此，这是一种正确诊断的冲动。有许多机制用于识别心律失常；然而，大多数技术已被利用的来源，如心电图（ECG）。医学分析人员基于心电图的人工评估是不准确的。一些实验集中在利用人工智能（AI）学习方法的准确性和速度，以及分类模型中的模式检测。然而，传统机制有两个主要限制；这些模型需要大量的训练时间，并且需要在人工的基础上选择特征。为此，引入了一种基于深度学习的智能心律失常分类模型来识别心律失常。在开始时，所需的信号是从标准源累积的。进一步，提取三种不同类型的特征，实现心律失常的有效自动分类。首先，利用条件自编码器提取深度特征，并将这些特征作为特征集1。进一步，从输入信号中检索波特征和谱特征，并将这些特征视为特征集2。随后，将信号转换为频谱图图像，并利用图卷积神经网络（GCNN）技术从这些图像中检索特征集3。此外，集成特征融合过程发生，以组合所有三组特征。将集合特征作为最优密集递归神经网络（ODR-AM）的输入，用于心律失常的分类。通过使用Giant Armadillo Optimization （ARGAO）的增强随机值来优化参数，提高了分类器的性能。该模型有助于了解心律失常的具体类型。最后，将该模型的仿真结果与其他传统模型进行了对比分析。

{"title":"A meta-heuristic aided arrhythmia classification model using advanced deep learning technique with multiple feature extraction mechanisms","authors":"Jay Raval , Kamalesh V.N. , Dr. Raj Kumar Patra","doi":"10.1016/j.compbiolchem.2026.108917","DOIUrl":"10.1016/j.compbiolchem.2026.108917","url":null,"abstract":"<div><div>Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the identification of arrhythmias; yet, most of the techniques have been utilized sources such as Electrocardiogram (ECG). The ECG-based manual evaluation by the medical analysts is inaccurate. Some experiments have been concentrated on the accuracy and the speed of the learning method by utilizing Artificial Intelligence (AI), and pattern detection in the classification model. However, there are two primary limitations in the conventional mechanisms; the models demand large training time and demand feature selection on a manual basis. Hence, an intellectual arrhythmia classification model using deep learning is introduced to identify the irregular heartbeat. In the beginning, the required signals are accumulated from standard sources. Further, three different kinds of features are extracted for an efficient automatic classification process of arrhythmia. At first, the deep features are extracted by applying the Conditional Autoencoder, and these features are considered as feature set 1. Further, wave features and spectral features are retrieved from the input signal and these features are considered as feature set 2. Subsequently, the signals are converted into spectrogram images and the Graph Convolutional Neural Network (GCNN) technique is employed to retrieve the feature set 3 from those images. Further, the ensemble feature fusion process takes place to combine all three sets of features. Ensemble features are provided as input for the Optimal Dense Recurrent neural network with Attention Mechanism (ODR-AM) for classifying the arrhythmia. The classifier’s performance is boosted by optimizing the parameters using the Augmented Random value of Giant Armadillo Optimization (ARGAO). This model is useful to know about the specific type of arrhythmia. Finally, the simulation findings of the presented model are analyzed with other conventional models.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108917"},"PeriodicalIF":3.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185182","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

Predicting antimicrobial resistance in Staphylococcus aureus using machine learning: Insights from a five-year surveillance study 使用机器学习预测金黄色葡萄球菌的抗菌素耐药性：来自五年监测研究的见解。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-29 DOI: 10.1016/j.compbiolchem.2026.108932

Mohammed F. Aldawsari , Hisham N. Altayb , Ehssan Moglad

Staphylococcus aureus is a leading cause of both community- and hospital-acquired infections, and the growing prevalence of antimicrobial resistance complicates clinical management worldwide. This study investigated the epidemiology, resistance trends, multidrug resistance (MDR) patterns, and the role of machine learning (ML) in predicting antibiotic susceptibility in Saudi Arabia. A total of 18,003 microbiology reports (2019–2024) were analyzed, identifying 2506 S. aureus isolates. Susceptibility testing included 31 antibiotics representing 11 pharmacological classes. Predictive ML models (Random Forest, Logistic Regression, Gradient Boosting) were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Wound (24 %) and blood (23 %) were the most frequent sources of S. aureus. High resistance (>70 %) was observed for β-lactams, fluoroquinolones, and macrolides/lincosamides, while glycopeptides, oxazolidinones, and lipopeptides maintained excellent activity (<10 % resistance). MDR occurred in 30 % of isolates, XDR in 0.6 %, and no PDR isolates were detected. Among ML models, Random Forest achieved the best overall performance across most antibiotics, Logistic Regression was optimal for ampicillin, and Gradient Boosting for linezolid. Vancomycin, linezolid, penicillin, and SXT achieved precision and recall above 0.92, demonstrating strong predictive reliability. S. aureus remains a major clinical threat in Saudi Arabia, with high MDR rates but preserved efficacy of last-line antibiotics. This study highlights the value of combining multi-center surveillance with interpretable machine learning approaches to support antimicrobial stewardship, enhance early resistance prediction, and inform data-driven clinical decision-making, particularly in settings where rapid molecular diagnostics may be limited.

金黄色葡萄球菌是社区和医院获得性感染的主要原因，而且全球抗菌素耐药性的日益流行使临床管理复杂化。本研究调查了沙特阿拉伯的流行病学、耐药趋势、多药耐药（MDR）模式以及机器学习（ML）在预测抗生素敏感性方面的作用。分析2019-2024年共18003份微生物学报告，鉴定出2506株金黄色葡萄球菌。药敏试验包括31种抗生素，代表11个药理学类别。预测机器学习模型（随机森林、逻辑回归、梯度增强）被训练并使用准确性、精密度、召回率、f1分数和混淆矩阵进行评估。伤口（24% %）和血液（23% %）是金黄色葡萄球菌最常见的来源。β-内酰胺类、氟喹诺酮类和大环内酯类/lincosamides具有较高的耐药性（bbb70 %），而糖肽类、恶唑烷酮类和脂肽类保持了良好的活性(

{"title":"Predicting antimicrobial resistance in Staphylococcus aureus using machine learning: Insights from a five-year surveillance study","authors":"Mohammed F. Aldawsari , Hisham N. Altayb , Ehssan Moglad","doi":"10.1016/j.compbiolchem.2026.108932","DOIUrl":"10.1016/j.compbiolchem.2026.108932","url":null,"abstract":"<div><div><em>Staphylococcus aureus</em> is a leading cause of both community- and hospital-acquired infections, and the growing prevalence of antimicrobial resistance complicates clinical management worldwide. This study investigated the epidemiology, resistance trends, multidrug resistance (MDR) patterns, and the role of machine learning (ML) in predicting antibiotic susceptibility in Saudi Arabia. A total of 18,003 microbiology reports (2019–2024) were analyzed, identifying 2506 <em>S. aureus</em> isolates. Susceptibility testing included 31 antibiotics representing 11 pharmacological classes. Predictive ML models (Random Forest, Logistic Regression, Gradient Boosting) were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Wound (24 %) and blood (23 %) were the most frequent sources of <em>S. aureus</em>. High resistance (>70 %) was observed for β-lactams, fluoroquinolones, and macrolides/lincosamides, while glycopeptides, oxazolidinones, and lipopeptides maintained excellent activity (<10 % resistance). MDR occurred in 30 % of isolates, XDR in 0.6 %, and no PDR isolates were detected. Among ML models, Random Forest achieved the best overall performance across most antibiotics, Logistic Regression was optimal for ampicillin, and Gradient Boosting for linezolid. Vancomycin, linezolid, penicillin, and SXT achieved precision and recall above 0.92, demonstrating strong predictive reliability. <em>S. aureus</em> remains a major clinical threat in Saudi Arabia, with high MDR rates but preserved efficacy of last-line antibiotics. This study highlights the value of combining multi-center surveillance with interpretable machine learning approaches to support antimicrobial stewardship, enhance early resistance prediction, and inform data-driven clinical decision-making, particularly in settings where rapid molecular diagnostics may be limited.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108932"},"PeriodicalIF":3.1,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115047","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

From virtual screening to bench: A dual-validation framework for drug repurposing against PI3K 从虚拟筛选到实验：针对PI3K药物再利用的双重验证框架。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-29 DOI: 10.1016/j.compbiolchem.2026.108934

Kavita Tewani , Zunnun Narmawala , Deepshikha Rathore , Heena Dave

Virtual screening has emerged as one of the most impactful in silico approaches for the identification of novel drug candidates, substantially reducing the cost and time associated with high-throughput screening (HTS). Ongoing efforts focus on exploring large-scale libraries of drug-like molecules to identify candidates with favourable pharmacological properties. In this study, we propose an applicability domain-based virtual screening strategy that extends beyond conventional approaches by prioritising compounds with ADMET profiles comparable to marketed drugs. To further enhance predictive performance, we developed a QSAR model on PI3K ligands using Light Gradient Boosting Machine (LGBM), which achieved an R2 value of 0.799, thereby providing an additional layer of validation for compound selection. The phosphoinositide 3-kinase (PI3K) pathway, a critical regulator of cell growth, survival, metabolism, and proliferation, is frequently dysregulated in multiple cancers and other diseases. Repurposing existing drugs that modulate PI3K activity offers the potential to accelerate therapeutic development while mitigating the challenges of de novo drug discovery.

To demonstrate the utility of our approach, we screened two compound libraries from Enamine—a hit-like locator library (

>

400,000 molecules) and a kinase-focused library (

>

64,000 molecules)—against the PI3K-

α

isoform. In addition, a set of 1367 FDA-approved drugs was screened to identify potential candidates for repurposing. From these extensive datasets, three small molecules from the Enamine libraries were identified with favourable drug-like properties and synthetic accessibility compared with existing PI3K-

α

inhibitors. Furthermore, one FDA-approved drug demonstrated potential PI3K-

α

inhibitory activity. Pharmacophore mapping provided additional validation of their drug-likeness. Importantly, wet-lab evaluation of the FDA-approved drug confirmed its inhibitory activity, thereby supporting the computational predictions.

Overall, our integrated in silico and experimental framework highlights promising PI3K-

α

inhibitors, underscoring the potential of applicability domain–based virtual screening and QSAR modelling for both drug discovery and repurposing.

虚拟筛选已成为识别新型候选药物的最具影响力的计算机方法之一，大大降低了与高通量筛选（HTS）相关的成本和时间。目前的工作重点是探索大规模的药物样分子文库，以确定具有良好药理特性的候选药物。在这项研究中，我们提出了一种基于适用性域的虚拟筛选策略，该策略超越了传统方法，优先考虑ADMET谱与上市药物相当的化合物。为了进一步提高预测性能，我们使用光梯度增强机（LGBM）建立了PI3K配体的QSAR模型，其R2值为0.799，从而为化合物选择提供了额外的验证层。磷酸肌肽3-激酶（PI3K）通路是细胞生长、存活、代谢和增殖的关键调节因子，在多种癌症和其他疾病中经常失调。重新利用现有的调节PI3K活性的药物提供了加速治疗开发的潜力，同时减轻了新药物发现的挑战。为了证明我们的方法的实用性，我们从enamine中筛选了两个化合物文库——一个类似于hit的定位文库（>400,000个分子）和一个激酶聚焦文库（>64,000个分子）——针对PI3K-α亚型。此外，一组1367 fda批准的药物进行筛选，以确定潜在的候选药物重新利用。从这些广泛的数据集中，从Enamine文库中鉴定出三个小分子，与现有的PI3K-α抑制剂相比，它们具有良好的药物样性质和合成可及性。此外，一种fda批准的药物显示出潜在的PI3K-α抑制活性。药效团图谱进一步验证了它们的药物相似性。重要的是，fda批准的药物的湿实验室评估证实了其抑制活性，从而支持了计算预测。总体而言，我们的集成硅和实验框架突出了有前途的PI3K-α抑制剂，强调了基于域的虚拟筛选和QSAR建模在药物发现和再利用方面的适用性潜力。

{"title":"From virtual screening to bench: A dual-validation framework for drug repurposing against PI3K","authors":"Kavita Tewani , Zunnun Narmawala , Deepshikha Rathore , Heena Dave","doi":"10.1016/j.compbiolchem.2026.108934","DOIUrl":"10.1016/j.compbiolchem.2026.108934","url":null,"abstract":"<div><div>Virtual screening has emerged as one of the most impactful in silico approaches for the identification of novel drug candidates, substantially reducing the cost and time associated with high-throughput screening (HTS). Ongoing efforts focus on exploring large-scale libraries of drug-like molecules to identify candidates with favourable pharmacological properties. In this study, we propose an applicability domain-based virtual screening strategy that extends beyond conventional approaches by prioritising compounds with ADMET profiles comparable to marketed drugs. To further enhance predictive performance, we developed a QSAR model on PI3K ligands using Light Gradient Boosting Machine (LGBM), which achieved an R2 value of 0.799, thereby providing an additional layer of validation for compound selection. The phosphoinositide 3-kinase (PI3K) pathway, a critical regulator of cell growth, survival, metabolism, and proliferation, is frequently dysregulated in multiple cancers and other diseases. Repurposing existing drugs that modulate PI3K activity offers the potential to accelerate therapeutic development while mitigating the challenges of de novo drug discovery.</div><div>To demonstrate the utility of our approach, we screened two compound libraries from Enamine—a hit-like locator library (<span><math><mo>></mo></math></span>400,000 molecules) and a kinase-focused library (<span><math><mo>></mo></math></span>64,000 molecules)—against the PI3K-<span><math><mi>α</mi></math></span> isoform. In addition, a set of 1367 FDA-approved drugs was screened to identify potential candidates for repurposing. From these extensive datasets, three small molecules from the Enamine libraries were identified with favourable drug-like properties and synthetic accessibility compared with existing PI3K-<span><math><mi>α</mi></math></span> inhibitors. Furthermore, one FDA-approved drug demonstrated potential PI3K-<span><math><mi>α</mi></math></span> inhibitory activity. Pharmacophore mapping provided additional validation of their drug-likeness. Importantly, wet-lab evaluation of the FDA-approved drug confirmed its inhibitory activity, thereby supporting the computational predictions.</div><div>Overall, our integrated in silico and experimental framework highlights promising PI3K-<span><math><mi>α</mi></math></span> inhibitors, underscoring the potential of applicability domain–based virtual screening and QSAR modelling for both drug discovery and repurposing.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108934"},"PeriodicalIF":3.1,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115046","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

Ningxue shengban decoction regulates T-cell immune balance in immune thrombocytopenia via the bone marrow hematopoietic microenvironment 宁血生板汤通过骨髓造血微环境调节免疫性血小板减少患者t细胞免疫平衡。

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-29 DOI: 10.1016/j.compbiolchem.2026.108925

Wuxia Yang , Huiying Kang , Yang Liu , Zhen Wang , Yanqi Song , Baoshan Liu , Aidi Wang

Background

Immune thrombocytopenia (ITP) is characterized by increased platelet clearance and decreased platelet production. Hematopoietic stem and progenitor cell (HSPCs) abnormalities are a key mechanism of ITP, contributing to megakaryocyte defects and aberrant lymphocyte differentiation. Ningxue Shengban Decoction (NXSBD) has proven to be an effective therapeutic option for ITP.

Aim

This study aimed to explore the therapeutic mechanism of NXSBD in ITP.

Method

First, we evaluated the therapeutic effect of NXSBD on thrombocytopenia. T-lymphocyte subsets were analyzed by flow cytometry, megakaryocyte features were examined by HE staining, serum cytokines were quantified by ELISA, and hepatic/renal safety indices were measured by MS. Following the identification of core targets and pathways via network pharmacology, their expression in bone marrow cells was confirmed by single-cell RNA sequencing analysis. Pseudo-temporal analysis then tracked the dynamics of these targets during HSC lineage commitment, and molecular docking finally confirmed strong binding affinities with NXSBD's constituents.

Results

NXSBD significantly ameliorated thrombocytopenia in ITP mice by rebalancing T-cell subsets and modulating key inflammatory cytokines, while also promoting the generation of thrombocytogenic megakaryocytes in the bone marrow. The core targets of NXSBD (RELA, IKBKB, AKT1, TP53, MAPK1, JUN, and FOS) were found to be present in hematopoietic stem cells (HSCs) and were involved in HSC differentiation into megakaryocytes and T lymphocytes. Molecular docking confirmed strong binding affinities between NXSBD constituents and these core targets. In conclusion, our findings demonstrate that NXSBD alleviates ITP through a multi-target mechanism that may participate in megakaryocyte production from HSCs and contribute to the restoration of peripheral T-cell homeostasis.

背景：免疫性血小板减少症（ITP）的特征是血小板清除率增加和血小板生成减少。造血干细胞和祖细胞（HSPCs）异常是ITP的关键机制，导致巨核细胞缺陷和淋巴细胞分化异常。宁血生板汤（NXSBD）是治疗ITP的有效选择。目的：探讨NXSBD治疗ITP的作用机制。方法：首先，评价NXSBD治疗血小板减少症的疗效。流式细胞术检测t淋巴细胞亚群，HE染色检测巨核细胞特征，ELISA检测血清细胞因子，ms检测肝/肾安全指标。通过网络药理学鉴定核心靶点和通路，通过单细胞RNA测序分析确认其在骨髓细胞中的表达。伪时间分析随后跟踪了这些靶点在HSC谱系承诺过程中的动态，分子对接最终证实了与NXSBD成分的强结合亲和力。结果：NXSBD通过平衡t细胞亚群和调节关键炎症细胞因子，显著改善ITP小鼠的血小板减少症，同时促进骨髓中血小板生成巨核细胞的产生。NXSBD的核心靶点（RELA、IKBKB、AKT1、TP53、MAPK1、JUN和FOS）存在于造血干细胞（HSC）中，并参与HSC向巨核细胞和T淋巴细胞的分化。分子对接证实了NXSBD成分与这些核心靶点之间的强结合亲和力。总之，我们的研究结果表明，NXSBD通过多靶点机制缓解ITP，该机制可能参与hsc巨核细胞的产生，并有助于恢复外周t细胞的稳态。

{"title":"Ningxue shengban decoction regulates T-cell immune balance in immune thrombocytopenia via the bone marrow hematopoietic microenvironment","authors":"Wuxia Yang , Huiying Kang , Yang Liu , Zhen Wang , Yanqi Song , Baoshan Liu , Aidi Wang","doi":"10.1016/j.compbiolchem.2026.108925","DOIUrl":"10.1016/j.compbiolchem.2026.108925","url":null,"abstract":"<div><h3>Background</h3><div>Immune thrombocytopenia (ITP) is characterized by increased platelet clearance and decreased platelet production. Hematopoietic stem and progenitor cell (HSPCs) abnormalities are a key mechanism of ITP, contributing to megakaryocyte defects and aberrant lymphocyte differentiation. Ningxue Shengban Decoction (NXSBD) has proven to be an effective therapeutic option for ITP.</div></div><div><h3>Aim</h3><div>This study aimed to explore the therapeutic mechanism of NXSBD in ITP.</div></div><div><h3>Method</h3><div>First, we evaluated the therapeutic effect of NXSBD on thrombocytopenia. T-lymphocyte subsets were analyzed by flow cytometry, megakaryocyte features were examined by HE staining, serum cytokines were quantified by ELISA, and hepatic/renal safety indices were measured by MS. Following the identification of core targets and pathways via network pharmacology, their expression in bone marrow cells was confirmed by single-cell RNA sequencing analysis. Pseudo-temporal analysis then tracked the dynamics of these targets during HSC lineage commitment, and molecular docking finally confirmed strong binding affinities with NXSBD's constituents.</div></div><div><h3>Results</h3><div>NXSBD significantly ameliorated thrombocytopenia in ITP mice by rebalancing T-cell subsets and modulating key inflammatory cytokines, while also promoting the generation of thrombocytogenic megakaryocytes in the bone marrow. The core targets of NXSBD (RELA, IKBKB, AKT1, TP53, MAPK1, JUN, and FOS) were found to be present in hematopoietic stem cells (HSCs) and were involved in HSC differentiation into megakaryocytes and T lymphocytes. Molecular docking confirmed strong binding affinities between NXSBD constituents and these core targets. In conclusion, our findings demonstrate that NXSBD alleviates ITP through a multi-target mechanism that may participate in megakaryocyte production from HSCs and contribute to the restoration of peripheral T-cell homeostasis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108925"},"PeriodicalIF":3.1,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121397","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 multilayered integrated analysis of insomnia-related genes ATG7 and JAK2 in the autophagy-inflammation mechanism and clinical implications in major depressive disorder 失眠相关基因ATG7和JAK2在重度抑郁症自噬-炎症机制中的多层整合分析及其临床意义

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-29 DOI: 10.1016/j.compbiolchem.2026.108931

Jinxiang Yu , Xiaochuan Feng , Haikun Zhang , Le Cao , Lifeng Jia , Pengcheng Ma , Nianliang Zhang , Tao Zhao

Objective

Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the underlying interactions among them remain to be elucidate.

Methods

In NHANES, an insomnia-like high-risk sleep phenotype was linked to an increased risk of MDD, and Mendelian randomization (MR) provided evidence for a potential causal effect of the genetic liability to insomnia. Core genes were identified via PPI network construction and machine learning analyses, integrated with the GEO database and insomnia-related genes. Subsequently, the clinical relevance of these genes was validated, and the performance of the model was evaluated using external datasets. A functional enrichment analysis of the core genes was conducted to explore the related biological pathways. The impacts of core genes on the immune microenvironment were explored via immune infiltration and cell-cell interaction analyses. Moreover, promising candidate therapeutic compounds were identified through drug enrichment and molecular interaction analyses.

Results

An insomnia-like high-risk sleep phenotype showed a consistent association with an increased MDD risk, and MR suggested a potential causal connection. The diagnostic model, constructed using the core genes ATG7 and JAK2, demonstrated good predictive abilities and showed potential for clinical application. Enrichment analyses highlighted autophagy- and inflammation-related pathways, accompanied by altered immune-cell signatures, supporting the involvement of an autophagy–inflammation axis in MDD. Molecular docking and dynamics indicated that emodin could interact stably with JAK2, which warrants experimental validation.

Conclusion

ATG7 and JAK2 are insomnia-associated genes linked to autophagy- and inflammation-related pathways in MDD. The ATG7/JAK2-based diagnostic model and the in silico–prioritized compound emodin provide testable hypotheses for future mechanistic investigations and translational exploration, pending experimental validation.

目的：失眠被广泛认为是重度抑郁障碍（MDD）的关键危险因素。然而，潜在的分子机制和它们之间潜在的相互作用仍有待阐明。方法：在NHANES中，失眠样高风险睡眠表型与MDD风险增加有关，孟德尔随机化（MR）为失眠遗传倾向的潜在因果效应提供了证据。通过PPI网络构建和机器学习分析，结合GEO数据库和失眠相关基因，鉴定出核心基因。随后，验证这些基因的临床相关性，并使用外部数据集评估模型的性能。对核心基因进行功能富集分析，探索相关生物学途径。通过免疫浸润和细胞-细胞相互作用分析，探讨核心基因对免疫微环境的影响。此外，通过药物富集和分子相互作用分析，发现了有希望的候选治疗化合物。结果：失眠样高危睡眠表型与重度抑郁症风险增加一致，MR提示潜在的因果关系。利用核心基因ATG7和JAK2构建的诊断模型具有良好的预测能力，具有临床应用潜力。富集分析强调了自噬和炎症相关的途径，伴随着免疫细胞特征的改变，支持自噬-炎症轴参与MDD。分子对接和动力学分析表明，大黄素与JAK2具有稳定的相互作用，值得实验验证。结论：ATG7和JAK2是失眠相关基因，与MDD的自噬和炎症相关途径相关。基于ATG7/ jak2的诊断模型和硅优先化合物大黄素为未来的机制研究和转化探索提供了可测试的假设，有待实验验证。

{"title":"A multilayered integrated analysis of insomnia-related genes ATG7 and JAK2 in the autophagy-inflammation mechanism and clinical implications in major depressive disorder","authors":"Jinxiang Yu , Xiaochuan Feng , Haikun Zhang , Le Cao , Lifeng Jia , Pengcheng Ma , Nianliang Zhang , Tao Zhao","doi":"10.1016/j.compbiolchem.2026.108931","DOIUrl":"10.1016/j.compbiolchem.2026.108931","url":null,"abstract":"<div><h3>Objective</h3><div>Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the underlying interactions among them remain to be elucidate.</div></div><div><h3>Methods</h3><div>In NHANES, an insomnia-like high-risk sleep phenotype was linked to an increased risk of MDD, and Mendelian randomization (MR) provided evidence for a potential causal effect of the genetic liability to insomnia. Core genes were identified via PPI network construction and machine learning analyses, integrated with the GEO database and insomnia-related genes. Subsequently, the clinical relevance of these genes was validated, and the performance of the model was evaluated using external datasets. A functional enrichment analysis of the core genes was conducted to explore the related biological pathways. The impacts of core genes on the immune microenvironment were explored via immune infiltration and cell-cell interaction analyses. Moreover, promising candidate therapeutic compounds were identified through drug enrichment and molecular interaction analyses.</div></div><div><h3>Results</h3><div>An insomnia-like high-risk sleep phenotype showed a consistent association with an increased MDD risk, and MR suggested a potential causal connection. The diagnostic model, constructed using the core genes ATG7 and JAK2, demonstrated good predictive abilities and showed potential for clinical application. Enrichment analyses highlighted autophagy- and inflammation-related pathways, accompanied by altered immune-cell signatures, supporting the involvement of an autophagy–inflammation axis in MDD. Molecular docking and dynamics indicated that emodin could interact stably with JAK2, which warrants experimental validation.</div></div><div><h3>Conclusion</h3><div>ATG7 and JAK2 are insomnia-associated genes linked to autophagy- and inflammation-related pathways in MDD. The ATG7/JAK2-based diagnostic model and the in silico–prioritized compound emodin provide testable hypotheses for future mechanistic investigations and translational exploration, pending experimental validation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108931"},"PeriodicalIF":3.1,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115053","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

Multiscale computational modeling integrated with in vitro evaluation of green-synthesized 2,3-dihydroquinazolin-4(1 H)-ones targeting U87 glioblastoma cells 结合多尺度计算模型的体外评价绿色合成的靶向U87胶质母细胞瘤细胞的2,3-二氢喹唑啉-4(1 H)-ones

IF 3.1 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry

Pub Date : 2026-01-28 DOI: 10.1016/j.compbiolchem.2026.108929

Sathiaseelan Perumal , Perumal Muthuraja , Venkatesan Muthukumar , Paramasivam Manisankar , Viswanathan Subramanian

Glioblastoma multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options, largely owing to the poor blood–brain barrier (BBB) permeability of current drugs. Quinazolinone derivatives represent an important class of heterocycles with diverse pharmacological potential; however, their activity against U87 glioblastoma cells has not been previously reported. In this study, a series of 2,3-dihydroquinazolin-4(1 H)-one derivatives (DHQs) were synthesised through a sustainable PEG-400-mediated multicomponent protocol performed in a sealed tube, providing an efficient and environmentally benign route to access this pharmacologically important scaffold. To identify potential glioblastoma inhibitors, a multiscale computational pipeline integrating DFT descriptors, ADME screening, molecular docking, molecular dynamics (MD) simulations, MM/GBSA, principal component analysis (PCA), and free energy landscape (FEL) calculations was employed. Among the synthesised molecules, compound 1h emerged as the most promising candidate, exhibiting the highest binding affinity towards EGFR (−9.11 kcal mol⁻¹) and favourable CNS-relevant physicochemical properties. MD simulations confirmed the structural stability of the 1h–3POZ complex for over 100 ns, as supported by low RMSD values, restricted residue fluctuations, and a stable free-energy profile. Experimental validation using the MTT assay on U87 glioblastoma cells demonstrated that compound 1h exhibited potent cytotoxicity (IC₅₀ = 16.57 ± 0.90μM), significantly outperforming temozolomide. Overall, this study presents the first integrated green-synthetic and computational-experimental evaluation of DHQs against U87 cells, highlighting compound 1h as a promising lead for glioblastoma drug discovery.

多形性胶质母细胞瘤（GBM）是一种高度侵袭性的脑肿瘤，治疗选择有限，主要是由于目前药物的血脑屏障（BBB）渗透性差。喹唑啉酮衍生物是一类重要的杂环化合物，具有多种药理潜力；然而，它们对U87胶质母细胞瘤细胞的活性尚未见报道。在这项研究中，一系列2,3-二氢喹唑啉-4(1 H)- 1衍生物（DHQs）通过可持续的peg -400介导的多组分协议在密封管中进行合成，提供了一个有效和环保的途径来获得这种重要的药理学支架。为了识别潜在的胶质母细胞瘤抑制剂，采用了多尺度计算管道，包括DFT描述符、ADME筛选、分子对接、分子动力学（MD）模拟、MM/GBSA、主成分分析（PCA）和自由能景观（FEL）计算。在合成的分子中，化合物1h表现出对EGFR的最高结合亲和力（- 9.11 kcal mol⁻¹）和良好的cns相关的物理化学性质，是最有希望的候选者。MD模拟证实了1h-3POZ配合物在100 ns以上的结构稳定性，RMSD值低，残留波动受限，自由能分布稳定。在U87胶质母细胞瘤细胞上使用MTT检测的实验验证表明，化合物1h具有强效的细胞毒性（IC₅₀= 16.57 ± 0.90μM），显著优于替莫唑胺。总体而言，本研究首次提出了针对U87细胞的dhq的绿色合成和计算实验综合评估，突出了化合物1h作为胶质母细胞瘤药物发现的有希望的先导物。

{"title":"Multiscale computational modeling integrated with in vitro evaluation of green-synthesized 2,3-dihydroquinazolin-4(1 H)-ones targeting U87 glioblastoma cells","authors":"Sathiaseelan Perumal , Perumal Muthuraja , Venkatesan Muthukumar , Paramasivam Manisankar , Viswanathan Subramanian","doi":"10.1016/j.compbiolchem.2026.108929","DOIUrl":"10.1016/j.compbiolchem.2026.108929","url":null,"abstract":"<div><div>Glioblastoma multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options, largely owing to the poor blood–brain barrier (BBB) permeability of current drugs. Quinazolinone derivatives represent an important class of heterocycles with diverse pharmacological potential; however, their activity against U87 glioblastoma cells has not been previously reported. In this study, a series of 2,3-dihydroquinazolin-4(1 H)-one derivatives (DHQs) were synthesised through a sustainable PEG-400-mediated multicomponent protocol performed in a sealed tube, providing an efficient and environmentally benign route to access this pharmacologically important scaffold. To identify potential glioblastoma inhibitors, a multiscale computational pipeline integrating DFT descriptors, ADME screening, molecular docking, molecular dynamics (MD) simulations, MM/GBSA, principal component analysis (PCA), and free energy landscape (FEL) calculations was employed. Among the synthesised molecules, compound <strong>1h</strong> emerged as the most promising candidate, exhibiting the highest binding affinity towards EGFR (−9.11 kcal mol⁻¹) and favourable CNS-relevant physicochemical properties. MD simulations confirmed the structural stability of the <strong>1h–3POZ</strong> complex for over 100 ns, as supported by low RMSD values, restricted residue fluctuations, and a stable free-energy profile. Experimental validation using the MTT assay on U87 glioblastoma cells demonstrated that compound <strong>1h</strong> exhibited potent cytotoxicity (IC₅₀ = 16.57 ± 0.90μM), significantly outperforming temozolomide. Overall, this study presents the first integrated green-synthetic and computational-experimental evaluation of DHQs against U87 cells, highlighting compound <strong>1h</strong> as a promising lead for glioblastoma drug discovery.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108929"},"PeriodicalIF":3.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074081","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