Computational biology and chemistry最新文献

Design and computational characterization of arginine-functionalized ZIF-8 as a pH-responsive oral insulin carrier. 精氨酸功能化ZIF-8作为ph响应型口服胰岛素载体的设计和计算表征。

Computational biology and chemistry Pub Date : 2025-10-08 DOI: 10.1016/j.compbiolchem.2025.108711

Ali Muneer Abdulrahman, Dina Hadi Abdullah Alaqebe, Zainab Abdullah Kareem, Eman Mohammed Jasim, Mohammed Mahmood Abdullah, Alaa Hamid Faisal, Mustafa M Kadhim

{"title":"Design and computational characterization of arginine-functionalized ZIF-8 as a pH-responsive oral insulin carrier.","authors":"Ali Muneer Abdulrahman, Dina Hadi Abdullah Alaqebe, Zainab Abdullah Kareem, Eman Mohammed Jasim, Mohammed Mahmood Abdullah, Alaa Hamid Faisal, Mustafa M Kadhim","doi":"10.1016/j.compbiolchem.2025.108711","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108711","url":null,"abstract":"This study presents a computational framework to evaluate arginine-modified ZIF-8 (ZIF-8@Arg) as a potential oral insulin delivery system. In contrast to unmodified metal-organic frameworks, the inclusion of arginine residues introduces guanidinium groups that enhance insulin interaction through electrostatic and hydrogen bonding effects. A combination of multiscale modeling techniques, including density functional theory (DFT), time-dependent DFT, molecular dynamics simulations, and topological analyses Atoms in Molecules (AIM) and Non-Covalent Interaction (NCI), was employed to characterize the molecular interface and environmental responsiveness. The results indicate improved binding stability at the ZIF-8@Arg-insulin interface, as well as pH-dependent structural adaptability, with swelling was observed under basic conditions. The activation energy for insulin release was calculated to be 15.39 kcal/mol. Solvation energy and partition coefficient (logP) analyses suggest favorable permeability characteristics. In silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiling indicates low predicted toxicity and compatibility with oral administration. Overall, the findings support further investigation of ZIF-8@Arg as a functional MOF-based carrier with tunable release behavior and acceptable pharmacokinetic properties for oral peptide delivery.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108711"},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing & screening of siRNA molecules for silencing the impact of the VEGF gene in cancer cells. 抑制肿瘤细胞中VEGF基因作用的siRNA分子的设计与筛选。

Computational biology and chemistry Pub Date : 2025-10-04 DOI: 10.1016/j.compbiolchem.2025.108708

Mohd Nazam Ansari, Abdulaziz S Saeedan, Sara A Aldossary

{"title":"Designing & screening of siRNA molecules for silencing the impact of the VEGF gene in cancer cells.","authors":"Mohd Nazam Ansari, Abdulaziz S Saeedan, Sara A Aldossary","doi":"10.1016/j.compbiolchem.2025.108708","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108708","url":null,"abstract":"Cancer is a complex disease characterized by uncontrolled cell proliferation and metastasis, with breast cancer remaining a leading cause of mortality among women worldwide. Hypoxia-inducible factor (HIF) and vascular endothelial growth factor (VEGF) are key mediators of angiogenesis, sustaining tumor growth and progression. RNA interference (RNAi) has emerged as a promising gene-silencing strategy for targeted cancer therapy. In this study, we designed small interfering RNAs (siRNAs) against VEGF mRNA using computational approaches. VEGF gene sequences were retrieved from NCBI, and siRNAs were designed using siDirect v2.0 and i-Score Designer. Candidate siRNAs were screened based on GC content (30-52 %), secondary structure, and thermodynamic stability. Hybridization energy analysis revealed favourable binding to VEGF mRNA, ranging from -31.1 to -37.3 kcal/mol. Molecular docking with h-Argonaute-2 (h-Ago2) yielded docking scores between -330 and -351 kcal/mol, indicating efficient RISC loading. Molecular dynamics (MD) simulations further demonstrated stable siRNA-Ago2 complexes, with RMSD values stabilizing around 2.1-2.6 Å and RMSF fluctuations primarily localized to the PAZ and MID domains. These findings confirm strong binding affinity, structural stability, and specificity of the designed siRNAs. Overall, our results suggest that RNAi-based silencing of VEGF holds significant potential as a therapeutic strategy for inhibiting angiogenesis in breast cancer.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108708"},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In silico exploration of bioactive compounds targeting the CrtM to impede Staphylococcus aureus drug resistance: Pigment inhibitors. 针对CrtM抑制金黄色葡萄球菌耐药的生物活性化合物的硅片探索：色素抑制剂。

Computational biology and chemistry Pub Date : 2025-10-01 DOI: 10.1016/j.compbiolchem.2025.108707

Sharon Munagalasetty, Samir Khan, Vitthal Kale, Vasundhra Bhandari

{"title":"In silico exploration of bioactive compounds targeting the CrtM to impede Staphylococcus aureus drug resistance: Pigment inhibitors.","authors":"Sharon Munagalasetty, Samir Khan, Vitthal Kale, Vasundhra Bhandari","doi":"10.1016/j.compbiolchem.2025.108707","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108707","url":null,"abstract":"The World Health Organization has designated the Methicillin-resistant Staphylococcus aureus (MRSA) and its variants as high-priority threats owing to their enhanced virulence and pathogenic potential. Staphyloxanthin (STX), a prominent virulence factor of S. aureus, plays a dual role: it shields the bacterium from oxidative stress generated by the host immune response and preserves the cell membrane integrity. Dehydrosqualene synthase (CrtM), a prenyl transferase, is essential for catalyzing the first step of STX biosynthesis. In this study, we evaluated 144,000 compounds, including anticancer agents, inhibitors and approved drugs, and 3D bioactive molecules to inhibit the CrtM using computational approaches. Virtual screening was performed on the prepared compound library, followed by relative binding free energy calculations based on MM/GBSA for hit compounds and 100 ns molecular dynamics (MD) simulations for top 3 hit candidates. BPH-652, a known CrtM inhibitor, was used as the reference. Our results revealed that Cmpd1 and Cmpd2 exhibit docking scores of -13.113 kcal/mol and -13.015 kcal/mol, respectively compared to BPH-652(-10.74 kcal/mol) against the CrtM. The stability was further confirmed with relative binding free energies of -57.70 kcal/mol for BPH-652, and -104.74 and -113.20 kcal/mol for Cmpd1 and Cmpd2, respectively. MD simulations demonstrated stable behavior of Cmpd1 and Cmpd2 inside active site of CrtM with minimal fluctuations, the binding energy calculated from MD trajectories also support strong affinity of these compounds. Their favorable ADME properties suggest the potential for further validation in in vitro and in vivo levels.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108707"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of protein thermostability trends based on the self-attention mechanism driven sparse convolutional network. 基于自关注机制驱动的稀疏卷积网络的蛋白质热稳定性趋势预测。

Computational biology and chemistry Pub Date : 2025-10-01 DOI: 10.1016/j.compbiolchem.2025.108693

Pei Xu, Kai Zhong, Honghua Ge, Xiaoping Song, Weihua Wang

{"title":"Prediction of protein thermostability trends based on the self-attention mechanism driven sparse convolutional network.","authors":"Pei Xu, Kai Zhong, Honghua Ge, Xiaoping Song, Weihua Wang","doi":"10.1016/j.compbiolchem.2025.108693","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108693","url":null,"abstract":"Artificial intelligence (AI)-assisted thermostability prediction of proteins can significantly alleviate the burden of mutation screening, thereby enhancing the efficiency of protein engineering. To further improve prediction accuracy and shorten the development cycle of new proteins, we integrate protein sequences, mutation relationships, and physicochemical properties for encoding, introducing the innovative Sparse Convolutional Network driven by the self-attention mechanism, named SCSAddG. Experimental results demonstrate that SCSAddG achieves a prediction accuracy of 0.868, a precision of 0.710, a recall of 0.606, an F1 score of 0.653, and an area under the Receiver Operating Characteristic (AUROC) of 0.825 in the general dataset S2648. Compared to traditional Convolutional Neural Networks (CNN), SCSAddG exhibits slightly higher prediction accuracy and outperforms the Rosetta bioinformatics simulation software 12% in terms of accuracy. Furthermore, in the experimental transglutaminase dataset, SCSAddG exhibits significantly better prediction accuracy compared to CNN (0.744 vs. 0.667), achieving a precision of 1.000. The results of wet laboratory experiments are consistent with the model predictions. In the 5-fold cross-validation, the SCSAddG model outperformed the CNN across multiple evaluation metrics, demonstrating its superior predictive performance and robust reliability. These results indicate that SCSAddG can effectively evaluate the trends in protein thermostability and serve as a valuable tool to guide protein thermostability engineering.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108693"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning approaches to predict drug resistance in tuberculosis. 预测肺结核耐药性的机器学习方法。

Computational biology and chemistry Pub Date : 2025-09-30 DOI: 10.1016/j.compbiolchem.2025.108705

A T Subalakshmi, Arundhati Mahesh

{"title":"Machine learning approaches to predict drug resistance in tuberculosis.","authors":"A T Subalakshmi, Arundhati Mahesh","doi":"10.1016/j.compbiolchem.2025.108705","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108705","url":null,"abstract":"Tuberculosis (TB) remains a global health crisis, with 10.8 million cases and 1.25 million deaths in 2023. The rise of drug-resistant TB has complicated treatment, while traditional diagnostic methods face limitations in speed, cost, and accuracy. This study explores machine learning (ML) models to predict drug resistance from genomic variants, offering a faster and more comprehensive solution. We compiled a comprehensive dataset of variations and mutations associated with resistance phenotypes from databases such as TBDReaMDB, GMTV, WHO, and CARD. For each mutation, both sequence-based features (e.g., physicochemical property changes, Provean scores) and structure-based features (e.g., hydrophobicity, flexibility, accessible surface area) were derived. Ensemble ML models (Stacking, Bagging and Voting Classifiers) were evaluated for their ability to predict resistance to key anti-TB drugs: Fluoroquinolones, Rifampicin, Isoniazid, and Pyrazinamide. Results achieved indicated that the model behaved differently on six TB resistance genes (gyrA, gyrB, inhA, katG, rpoB, pncA), with accuracy varying from 66 % (gyrA Stacking) to 91.37% (pncA Voting) and ROC scores varying from 0.69 (gyrA Bagging) to 0.92 (pncA Stacking). The Bagging model performed best for gyrA, gyrB and rpoB with strong classification, while the Stacking classifier performed well for inhA. Voting classifier proved to be the top-performing classifier for katG and pncA gene. The top-performing model for both genes was chosen, emphasizing a gene-specific strategy to maximize resistance prediction. This study demonstrates that gene-specific ensemble models, supported by a comprehensive feature set, can provide valuable predictions of drug resistance in M. tuberculosis. While promising, the findings remain a proof-of-concept and require further validation on larger and more diverse clinical datasets before clinical application.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108705"},"PeriodicalIF":0.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal. 基于gan的混合深度学习特征选择方法用于心电信号的心跳分类。

Computational biology and chemistry Pub Date : 2025-09-30 DOI: 10.1016/j.compbiolchem.2025.108704

S Haseena Beegum, R Manju

{"title":"GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal.","authors":"S Haseena Beegum, R Manju","doi":"10.1016/j.compbiolchem.2025.108704","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108704","url":null,"abstract":"Heart arrhythmias are one of the most important categories of cardiovascular illness. A heartbeat that is abnormal like too early, too slow, too fast, or uneven is indicated as an arrhythmia. Though some cardiac arrhythmias are benign, others can be dangerous and fatal if they are thought to be abnormal or the outcome of a damaged heart. The arrhythmias can be recognized by looking at and classifying the electrocardiogram (ECG) heartbeats. The automatic explanation of ECG data has witnessed a prominent development with the emergence of machine learning techniques. This paper develops an optimal deep learning technique to classify heartbeats. At first, pre-processing is done using median filter, resolution wavelet-based technique is exploited to recognize wave components. Subsequently, the features, like Discrete Wavelet Transform (DWT), autoregressive, Fractional Fourier-Transform (FrFT), and morphological features, are extracted. As the next step, feature fusion is performed by employing Kendall Tau, wrapper, and kraskov entropy together with Generative Adversarial Network (GAN). Lastly, heartbeat classification is done by employing proposed SExpHGS based DBN-VGG, where DBN-VGG is adopted by integration of Deep Belief Network and VGG, trained by employing Serial Exponential Hunger Games Search Algorithm (SExpHGS). Experimental outcomes illustrate that the SExpHGS based DBN-VGG approach performed superior when compared to conventional models with 95.7 % accuracy, 97.2 % sensitivity, and 94.9 % specificity rate.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108704"},"PeriodicalIF":0.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends. 基于深度学习的脑肿瘤分割和MRI检测的系统综述：过去的见解，目前的技术和未来的趋势。

Computational biology and chemistry Pub Date : 2025-09-29 DOI: 10.1016/j.compbiolchem.2025.108696

Krupa Chary Pasunoori, Ch Rajendra Prasad, K Raj Kumar

{"title":"A systematic review on deep learning based brain tumor segmentation and detection using MRI: Past insights, present techniques and future trends.","authors":"Krupa Chary Pasunoori, Ch Rajendra Prasad, K Raj Kumar","doi":"10.1016/j.compbiolchem.2025.108696","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108696","url":null,"abstract":"The abnormal growth of cells leads to brain malignancy in humans, which is among the most prevalent causes of fatalities in adults worldwide. Patients' likelihood of survival increases, and therapeutic opportunities improve when brain tumors are identified early. Compared to other imaging techniques, Magnetic Resonance Imaging (MRI) scans provide more comprehensive information. A brain tumor can be diagnosed and differentiated from MRI images using a variety of brain tumor recognition and segmentation approaches. The utilization of deep learning-based models has proven effective in analyzing the vast volume of MRI data. The main purpose of this review is to provide an overview of brain tumor segmentation and detection techniques. To efficiently process the large volume of images, this review presents a detailed analysis of deep learning models. Furthermore, a chronological analysis is carried out to validate the robustness of the techniques. Following that, to better understand the performance of the models, the strengths and limitations of standard deep learning methods are discussed. In addition, the dataset details, performance evaluations, and simulation tools are discussed in this review. Finally, the challenges and research gaps in brain tumor segmentation and detection models are highlighted.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 2","pages":"108696"},"PeriodicalIF":0.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Targeting Aurora A kinase: Computational discovery of potent inhibitors through integrated pharmacophore and simulation approaches. 靶向Aurora A激酶：通过综合药效团和模拟方法计算发现有效抑制剂。

Computational biology and chemistry Pub Date : 2025-09-17 DOI: 10.1016/j.compbiolchem.2025.108690

Bhuvaneswari Sivaraman, Kathiravan Muthukumaradoss

{"title":"Targeting Aurora A kinase: Computational discovery of potent inhibitors through integrated pharmacophore and simulation approaches.","authors":"Bhuvaneswari Sivaraman, Kathiravan Muthukumaradoss","doi":"10.1016/j.compbiolchem.2025.108690","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108690","url":null,"abstract":"Cancer currently ranks as the second most common cause of mortality worldwide, primarily due to uncontrolled cell growth driven by aberrant mitotic processes. Aurora A kinase (AURKA), a key regulator of mitosis involved in centrosome maturation, bipolar spindle formation, and cytokinesis, has been identified as a promising anticancer target. This study employs a comprehensive computational approach to identify new AURKA inhibitors. Using MOE software, a ligand-based pharmacophore model was developed based on six potent AURKA inhibitors. The model, consisting of three features-Aro/HydA, Acc, and Don/Acc-at an 80 % threshold, demonstrated strong discriminative power with a sensitivity of 69.8 %, specificity of 63.6 %, and accuracy of 60.4 %. Screening of the ZINC database yielded 774 hits, from which A1 (ZINC63106872) and A2 (ZINC39272872) were identified as the top candidates, with superior docking scores (-9.24 and -8.97 kcal/mol) compared to the reference MK-5108 (-7.49 kcal/mol). These hits satisfied Lipinski's rule and exhibited favourable ADMET profiles. DFT analysis revealed higher dipole moments (A1: 6.15 D, A2:6.39 D) and narrower HOMO-LUMO gaps (A1: 0.33 eV, A2: 0.38 eV), indicating enhanced polarity and reactivity. MEP plots showed defined donor-acceptor zones for both compounds, having a balanced surface. Molecular dynamics simulations over 500 ns confirmed complex stability, with protein backbone RMSD around 2.8 Å and ligand RMSD of 4.0 Å (A1) and 6.0 Å (A2). RMSF values remained below 2.4 Å. The most favourable binding energy for A1 (-75.34 kcal/mol) in MM-GBSA analysis confirms its strong interaction and therapeutic potential.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":" ","pages":"108690"},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-step joint model based on deep learning realizes intelligent recognition of exfoliated cells in serous effusion. 一种基于深度学习的两步联合模型实现了浆液积液中脱落细胞的智能识别。

Computational biology and chemistry Pub Date : 2025-08-09 DOI: 10.1016/j.compbiolchem.2025.108616

Yige Yin, Xiaotao Li, Dongsheng Li, Yue Hu, Qiang Wu, Jiarong Zhao, Qiuyan Sun, Hong-Qiang Wang, Wulin Yang

{"title":"A two-step joint model based on deep learning realizes intelligent recognition of exfoliated cells in serous effusion.","authors":"Yige Yin, Xiaotao Li, Dongsheng Li, Yue Hu, Qiang Wu, Jiarong Zhao, Qiuyan Sun, Hong-Qiang Wang, Wulin Yang","doi":"10.1016/j.compbiolchem.2025.108616","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108616","url":null,"abstract":"Cytological examination of serous effusion is critical for diagnosing malignancies, yet it heavily relies on subjective interpretation by pathologists, leading to inconsistent accuracy and misdiagnosis, especially in regions with limited medical resources. To address this challenge, we propose a two-step deep learning framework to standardize and enhance the diagnostic process. First, we improved the YOLOv8 model by integrating the Online Convolutional Reparameterization (OREPA) module, achieving a 93.09 % sensitivity for detecting abnormal cells. Second, we employed the Dual Attention Vision Transformer (DaViT) to classify normal cells (lymphocytes, mesothelial cells, histiocytes, neutrophils) with 98.74 % accuracy. By jointly deploying these models, our approach reduces missed diagnoses and provides granular insights into cell composition, offering a robust tool for rapid and objective cytopathological diagnosis. This work bridges the gap between AI-driven automation and clinical needs, particularly in resource-constrained settings.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108616"},"PeriodicalIF":0.0,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovery of biselyngbyaside B a novel lead inhibitor of drug-resistant bacteria targeting DNA gyrase B. 靶向DNA回转酶B的新型耐药细菌先导抑制剂biselyngbyaside B的发现。

Computational biology and chemistry Pub Date : 2025-08-07 DOI: 10.1016/j.compbiolchem.2025.108628

Kiran Mahapatra, Swagat Ranjan Maharana, Showkat Ahmad Mir, Munmun Bordhan, Binata Nayak

{"title":"Discovery of biselyngbyaside B a novel lead inhibitor of drug-resistant bacteria targeting DNA gyrase B.","authors":"Kiran Mahapatra, Swagat Ranjan Maharana, Showkat Ahmad Mir, Munmun Bordhan, Binata Nayak","doi":"10.1016/j.compbiolchem.2025.108628","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2025.108628","url":null,"abstract":"Antimicrobial resistance (AMR) poses a growing global threat, with antibiotic-resistant infections becoming a leading cause of death worldwide. The present study explores natural cyanobacterial compounds as possible inhibitors of Escherichia coli DNA gyrase B (GyrB) which is a verified antibacterial target that is not present in higher eukaryotes. Because of the urgent need for novel antibacterial drugs, we identified nine drug-like candidates using lipinski's rule of five and ADMET profiling. Molecular docking revealed that Biselyngbyaside B and Smenamide A exhibited greater binding affinities in comparison to the co-crystallized inhibitor EOF, with a binding energy of -9.03 kcal/mol. Further molecular dynamics simulations revealed that the Biselyngbyaside B-DNA gyrase B complex surpassed both EOF and Smenamide A in terms of structural stability, compactness, and strong hydrogen bonding. Umbrella sampling was employed to estimate the binding free energy from thirty sampling simulations, and Biselyngbyaside B exhibited a significantly favourable ΔG bind of -91.66 kJ/mol, outperforming EOF (-68.93 kJ/mol) and Smenamide A (-36.4 kJ/mol). These findings clearly indicate a stronger and more stable interaction between Biselyngbyaside B and GyrB. Biselyngbyaside B continuously showed better pharmacokinetic characteristics, non-hepatotoxicity, and a greater binding affinity than previously documented DNA gyrase B inhibitors. This study emphasizes the integration of molecular dockings, molecular dynamics simulation, umbrella sampling, and ADMET analysis provided crucial quantitative insights into the identification of potent drug-like candidates for further validation. Overall, the Biselyngbyaside B was found to be the most promising lead compound for novel antibacterial drug development targeting DNA gyrase B.","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108628"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0