Computational Biology and Chemistry最新文献

{"title":"Prediction of newly synthesized heparin mimic’s effects as heparanase inhibitor in cancer treatments via variational quantum neural networks","authors":"Samet Kocabay ,&nbsp;Erdi Acar ,&nbsp;Samet Memiş ,&nbsp;Irmak İçen Taşkın ,&nbsp;Meryem Rüveyda Sever ,&nbsp;Ramazan Şener","doi":"10.1016/j.compbiolchem.2025.108476","DOIUrl":"10.1016/j.compbiolchem.2025.108476","url":null,"abstract":"<div><div>Cancer remains a leading global cause of death, primarily driven by the uncontrolled proliferation of abnormal cells. Malignant tumors, such as carcinomas, originate from unchecked epithelial cell growth and produce growth factors like FGF and VEGF, which promote angiogenesis and tumor progression through heparanase-mediated degradation of heparan-sulfate proteoglycans. Chitosan and its derivatives have shown promise in inhibiting tumor growth and metastasis. This study aims to investigate newly synthesized sulfated chitosan oligomers as heparin mimics to inhibit heparanase, evaluating their cytotoxic effects on SH-SY5Y, HCT116, A549, and MDA-MB-231 cancer cell lines. Moreover, it seeks to leverage a variational quantum neural network (VQNN) to predict and validate cytotoxicity outcomes, integrating quantum computing methods into evaluating novel anticancer compounds. The VQNN algorithm was applied to analyze the anticancer effects of sulfated chitosan oligomers. Cytotoxicity data from wet lab experiments validated the model’s predictive performance. The VQNN model demonstrated strong predictive capabilities in evaluating anticancer compounds. Specifically, it achieved a mean absolute error (MAE) of 6.5844, indicating a similar trend to the experimental results. Additionally, the model obtained an R² value of 0.6020, reflecting a moderate level of correlation between predicted and observed outcomes. The results underscore the potential of integrating quantum-based machine learning models into cancer research. The VQNN effectively predicted experimental outcomes, showcasing its utility in assessing novel anticancer compounds. This approach could speed up drug discovery by streamlining the identification and optimization of therapeutic candidates. Furthermore, the findings support the ongoing development of quantum computing techniques for tackling complex biological challenges, contributing to innovative cancer treatment strategies that target tumor growth and angiogenesis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108476"},"PeriodicalIF":2.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859616","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}

{"title":"im7G-DCT: A two-branch strategy model based on improved DenseNet and transformer for m7G site prediction","authors":"Rufeng Lei ,&nbsp;Jian Jia ,&nbsp;Lulu Qin","doi":"10.1016/j.compbiolchem.2025.108473","DOIUrl":"10.1016/j.compbiolchem.2025.108473","url":null,"abstract":"<div><div>N-7 methylguanosine (m7G) is an important RNA modification that plays a key role in regulating gene expression and cellular physiological functions. Medical research has shown that m7G is closely associated with the development of a variety of diseases, including cancer, neurodegenerative diseases and viral infections. Therefore, accurate identification of m7G sites in mRNA is important for clinical applications and development of therapeutic strategies. With the rapid development of computational methods, deep learning prediction models are widely used in the field of m7G site. We developed a model called im7G-DCT based on the improved Densely Connected Convolutional Network and Transformer, which employs a two-branching strategy to extract local and global features from the original feature code in parallel. We found that this innovative strategy can mine the potential feature information of m7G locus sequences in a deeper way, which enhances the richness of features and improves the accuracy of prediction. As an innovative deep learning method, the im7G-DCT model shows important research value in the field of bioinformatics and has broad application prospects. In the results of independent test experiments, the im7G-DCT model achieved 81.68 %, 90.52 %, 86.10 %, and 72.48 % in sensitivity, specificity, accuracy, and Matthews' correlation coefficient, respectively. The im7G-DCT model performs well in all evaluation metrics and it significantly outperforms other existing prediction models.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108473"},"PeriodicalIF":2.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834828","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}

{"title":"Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis","authors":"Pradeep Kumar Yadalam ,&nbsp;Swarnambiga Ayyachamy ,&nbsp;Francisco T. Barbosa ,&nbsp;Prabhu Manickam Natarajan","doi":"10.1016/j.compbiolchem.2025.108451","DOIUrl":"10.1016/j.compbiolchem.2025.108451","url":null,"abstract":"<div><div>Periodontal disease, or periodontitis, is a chronic inflammatory condition affecting the tissues supporting teeth, with epigenetic mechanisms such as DNA methylation, histone modifications, and RNA molecules playing a crucial role in its progression. Histone deacetylase (HDAC) inhibitors have shown potential in treating inflammatory diseases by modulating gene expression to suppress inflammation and promote tissue regeneration. Machine learning models, particularly Kolmogorov-Arnold Networks (KANs), provide an advanced solution for predicting drug-gene associations, offering superior accuracy, efficiency, interpretability, and scalability compared to traditional Multi-Layer Perceptrons (MLPs). This study explores the application of KANs in predicting drug-gene associations of HDAC1 inhibitors for periodontitis. A dataset comprising 533 compounds and genes was analyzed using Cytoscape for network-based topological and functional insights, followed by predictive modeling using KANs. The resulting network contained 326 nodes and 3734 edges, with an average of 23.411 neighbors, a diameter 5, and a characteristic path length of 2.383. The predictive model achieved an impressive accuracy of 96.49 %, as indicated by the F1 score, reflecting balanced performance in classification. The study highlights the potential of KANs in drug discovery for periodontal disease by efficiently predicting drug-gene associations, enabling better understanding and research for experimental validation and clinical applications.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108451"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830354","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}

{"title":"Structure-based profiling of putative therapeutics against monkeypox virus VP39 using pharmacophore modelling and molecular dynamics simulation studies","authors":"Gideon Ampoma Gyebi,&nbsp;Saheed Sabiu","doi":"10.1016/j.compbiolchem.2025.108458","DOIUrl":"10.1016/j.compbiolchem.2025.108458","url":null,"abstract":"<div><div>The growing global health threat of the monkeypox virus (MPXV) underscores the critical need for effective antiviral agents, since there are currently no therapeutics. The MPXV VP39, a methyltransferase, is essential for viral replication, hence a potential target for anti-MPXV drug candidates. Herein, a structure-based pharmacophore modelling and molecular docking approach was employed to screen natural compounds (NCs: 581,426) from the COCONUT database for potential inhibitors of MPXV VP39. After ranking of the docking scores, an ensemble-based docking of the top-ranked 20 NCs against multiple conformations obtained from ttcluster analysis of the molecular dynamics simulation trajectory of unbound MPXV VP39 further identified five leads with favourable interaction profiles, drug-likeness, ADMET properties, and synthetic features when compared to the reference standard (sinefungin). Further analysis of the thermodynamic stability of the resulting complexes of the leads over a 100-ns MD simulation period revealed varying degrees of thermodynamic stability while maintaining the structural integrity of MPXV VP39. Furthermore, the thermodynamic binding free energy calculation, while corroborating the docking analysis, identified CNP0297833 (-39.07 kcal/mol), CNP0371756 (-25.76 kcal/mol), and CNP0402319 (-19.26 kcal/mol) as the most promising candidates, with better modulatory effect against MPXV VP39 relative to sinefungin (-3.68 kcal/mol). These leads were stabilised with hydrophobic (Phe115, Val139, and Val116) and electrostatic (Glu46 and Asp138) interactions in different conformational clusters. In addition to the observed consistent interaction patterns, favourable binding energies, pharmacokinetics, ADMET, thermodynamic stability, and molecular orbital energies of these leads, the potential for optimisation for enhanced binding features for the active site of MPXV VP39 was elucidated. Further <em>in vitro</em> investigation to validate these findings is suggested to establish the putative leads as therapeutics targeting the replication phase of MPXV.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108458"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838975","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}

{"title":"Transcriptomic analysis of human castration-resistant prostate cancer: Insights into novel therapeutic strategies","authors":"Ramanjaneyulu Golla ,&nbsp;Sneha Jaiswal ,&nbsp;Anaswara Jayan ,&nbsp;Srinivasulu Cheemanapalli","doi":"10.1016/j.compbiolchem.2025.108459","DOIUrl":"10.1016/j.compbiolchem.2025.108459","url":null,"abstract":"<div><div>Prostate cancer is a major cause of cancer-related deaths in men worldwide. Androgen deprivation therapy (ADT) is the standard treatment for advanced prostate cancer; however, disease progression to castration-resistant prostate cancer (CRPC) presents a significant therapeutic challenge. In this study, we employed transcriptomic analysis to investigate key genetic drivers of CRPC and identify novel therapeutic targets. Using RNA-seq data and bioinformatics tools, we identified differentially expressed genes (DEGs) associated with tumor progression, cytoskeletal dynamics, and immune modulation, including COL3A1, MYH4, FN1, ACTN1, and CALR. Functional enrichment analysis revealed significant involvement of actin-myosin filament sliding, calcium signaling, androgen receptor signaling, immune evasion, and metabolic pathways, underscoring their roles in CRPC progression and treatment resistance. Additionally, molecular docking studies demonstrated strong binding interactions between key CRPC-related genes (ABCC4 and FOLH1) and potential therapeutic ligands, including flutamide and N-acetyl glucosamine (NAG), highlighting their therapeutic potential in overcoming drug resistance. These findings provide novel insights into the molecular landscape of CRPC and support the development of precision-targeted therapies to improve patient outcomes.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108459"},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838976","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}

{"title":"Chinese herbal extract Astragalus radix potentiates human ovarian cancer cell cytotoxicity by aggravated ROS production and apoptosis","authors":"Jianjian Zhong,&nbsp;Xiaohua Fan","doi":"10.1016/j.compbiolchem.2025.108457","DOIUrl":"10.1016/j.compbiolchem.2025.108457","url":null,"abstract":"<div><h3>Background</h3><div>Ovarian cancer remains one of the most lethal gynaecological malignancies due to its late diagnosis, and resistance to conventional therapies. Traditional Chinese Medicine (TCM) is increasingly explored for its potential in cancer treatment. This study investigates the anti-tumor effects of a Chinese herbal extract on an ovarian cancer cell line in vitro.</div></div><div><h3>Methods</h3><div>The ovarian cancer cell lines OVCAR-3 and SK-OV-3 treated with varying concentrations of the Chinese herbal extract (<em>Astragalus radix)</em> at different course of time. Cell viability using the MTT assay, and apoptosis was examined by flow cytometry after staining with Annexin V/PI staining. Molecular docking and dynamics were carried out to examine the interaction of quinacetol with a well-known target of ovarian cancer, i.e., phosphoinositide 3-kinase (PI3K).</div></div><div><h3>Results</h3><div>The Chinese herbal extract <em>Astragalus radix</em> significantly reduced the viability of ovarian cancer cells in a time- and dose- dependent way. Flow cytometry analysis revealed increased apoptotic rates in ovarian cancer cells compared to controls. Quinacetol was found to interact at active site of PI3K with binding energy of −6.9 kcal/mol. The PI3K-quinacetol complex was stable at physiological conditions as evident from molecular simulation studies.</div></div><div><h3>Conclusion</h3><div>The findings of this study demonstrate that the Chinese herbal extract (<em>Astragalus radix)</em> exhibits potent anti-tumor effects against ovarian cancer cells in vitro, highlighting its potential as an adjunct or alternative therapeutic option. Further in vivo studies in animal models and clinical trials are warranted to explore the efficacy and safety of this herbal treatment in ovarian cancer patients.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108457"},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826212","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}

{"title":"Multi-computational screening identifies homovanillic acid as a potential SAP5 inhibitor against Candida albicans biofilms","authors":"Anmol Kulshrestha ,&nbsp;Pratima Gupta","doi":"10.1016/j.compbiolchem.2025.108453","DOIUrl":"10.1016/j.compbiolchem.2025.108453","url":null,"abstract":"<div><div>This work aims to find inhibitors of SAP5, a virulence factor in <em>Candida albicans</em> polymicrobial biofilms. The methodology included docking simulations, MMGBSA calculations, and molecular dynamics simulations. Of the 107 phenolic acids retrieved from PubChem, 20 passed ADMET screening. The research finds homovanillic acid to be a possible SAP5 inhibitor, with a binding energy of −19.92 kcal/mol as shown by molecular docking and MMGBSA analysis. The compound showed favorable ADMET properties, indicating low toxicity and high drug-likeness. Molecular dynamics simulations over 100 nanoseconds confirmed stable protein-ligand interactions. These findings suggest homovanillic acid's potential in treating AMR-associated biofilms and establish a foundation for experimental validation. The study demonstrates how computational methods can accelerate the discovery of novel antifungal medicines targeting polymicrobial infections.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108453"},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823820","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}

{"title":"MGMA-DTI: Drug target interaction prediction using multi-order gated convolution and multi-attention fusion","authors":"Chang Li,&nbsp;Jia Mi,&nbsp;Han Wang,&nbsp;Zhikang Liu,&nbsp;Jingyang Gao,&nbsp;Jing Wan","doi":"10.1016/j.compbiolchem.2025.108449","DOIUrl":"10.1016/j.compbiolchem.2025.108449","url":null,"abstract":"<div><div>Accurately predicting drug-target interactions (DTI) is crucial for drug discovery and can reduce drug development costs. Recent deep learning-based DTI predictions have demonstrated promising performance, but they still face two challenges: (i) The over-reliance on the extraction of local features and insufficient learning of global features limit the model’s performance. (ii) The lack of effective fusion of drug-target interaction features leads to the lack of interpretability of the model. To address these challenges, we propose a new model for predicting drug-target interactions based on multi-order gated convolution and multi-attention fusion, MGMA-DTI. The drug feature encoder obtains a two-dimensional molecular graph based on the drug’s SMILES string and uses a graph convolutional neural network to encode the drug features. The protein encoder is based on a multi-order gated convolution, which enhances the model’s ability to capture global feature between amino acid sequences. In order to better achieve interactive learning between drugs and proteins, we designed a multi-attention fusion module that effectively captures the drug-target interaction features. Experimental results show that MGMA-DTI outperforms other baseline models on three benchmark datasets: BindingDB, BioSNAP, and Human. Case studies further demonstrate that the model provides valuable insights for drug discovery. In addition, our model provides molecular-level interpretability, which can provide more scientifically meaningful guidance.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108449"},"PeriodicalIF":2.6,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829049","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}

{"title":"Harnessing marine bioactive compounds: In silico insights into therapeutics for rheumatoid arthritis and major depressive disorder","authors":"Santhiya Panchalingam,&nbsp;Govindaraju Kasivelu","doi":"10.1016/j.compbiolchem.2025.108452","DOIUrl":"10.1016/j.compbiolchem.2025.108452","url":null,"abstract":"<div><div>The quest for the discovery of novel therapeutic agents’ increases day by day owing to the increased incidence of drug-resistant infections, chronic diseases, and a need for discovering novel treatments. Conventionally, the sources for molecules of drugs have remained from terrestrial plants and microorganisms, yet the chemical adaptability of marine organisms presents something very unique in chemical terms and remains an uncharted frontier. Marine bioactive compounds-chemicals produced by marine organisms that have positive health impacts on humans-attract particular interest due to their pharmaceutical potential. Marine organisms range from macroalgae (seaweeds), microalgae, and sponges to molluscs, echinoderms, and fish. Each of these categories generates a variety of bioactive compounds that have unique biochemical properties. Many marine-derived compounds have exhibited strong antimicrobial activity, anticancer activity and neuroprotective effects. Despite the enormous potential of marine bioactive compounds in drug discovery, several challenges like Accessibility and Sustainability, Complexity of Marine Compounds, and Regulation and Approval act as bottlenecks in taking them from the lab to the clinic. It is an imperative task to tackle these challenges for a complete development of marine pharmacopoeia. This review emphasizes on the possible application of chemicals emanating from marine sources as lead molecules for the prevention of major depressive disorder and rheumatoid arthritis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108452"},"PeriodicalIF":2.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821117","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}

{"title":"A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens","authors":"Watshara Shoombuatong ,&nbsp;Saeed Ahmed ,&nbsp;SM Hasan Mahmud ,&nbsp;Nalini Schaduangrat","doi":"10.1016/j.compbiolchem.2025.108440","DOIUrl":"10.1016/j.compbiolchem.2025.108440","url":null,"abstract":"<div><div>The precise identification of tumor T-cell antigens (TTCAs) is crucial for advancements in cancer immunotherapy and other clinical uses. In contrast to the labor-intensive and time-consuming process of experimentally identifying TTCAs, computational prediction offers a complementary approach by providing a shortlist of probable TTCA candidates for further experimental validation. Currently, several computational approaches, primarily based on machine learning (ML) methods, have garnered considerable attention for the <em>in silico</em> identification of tumor T-cell antigens (TTCAs). Therefore, this study presents a comprehensive survey on the existing state-of-the-art TTCA predictors. Based on our research, this is the first comprehensive review focused on both traditional ML and ensemble learning methods for TTCA identification. Specifically, we examine critical aspects of TTCA predictor development, including core algorithms, methodologies, benchmark datasets, feature encoding methods, feature selection approaches, and web server usability. We then analyze and compare the effectiveness and robustness of existing predictors across well-known benchmark datasets and case studies. Finally, we provide a detailed summary of the advantages and disadvantages of current TTCA predictors, along with essential insights and suggestions for developing novel computational approaches to accurately identify TTCAs. The insights gained from this review and benchmarking survey are expected to offer valuable guidance to researchers, aiding in the development of high-accuracy TTCA predictors for improved antigen identification in the future.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108440"},"PeriodicalIF":2.6,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816652","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}