Computational and structural biotechnology journal最新文献

{"title":"Multi-omics characterization of diabetic nephropathy in the db/db mouse model of type 2 diabetes.","authors":"Liping Wang, Ran Zhou, Guanghui Li, Xiaodan Zhang, Yan Li, Yinchen Shen, Junwei Fang","doi":"10.1016/j.csbj.2025.07.037","DOIUrl":"10.1016/j.csbj.2025.07.037","url":null,"abstract":"<p><strong>Background: </strong>Despite optimized blood pressure control and glycemic management reducing the incidence of diabetic nephropathy (DN), significant residual risk remains, suggesting the contribution of pathogenic factors independent of glucose metabolism and hemodynamic disturbances.</p><p><strong>Methods: </strong>Renal tissues from db/db mice underwent integrative multi-omics analysis, encompassing transcriptomics, metabolomics, and lipidomics. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) was applied to identify significant metabolic perturbations, while bidirectional O2PLS integration elucidated metabolic-transcriptomic correlations. Lipid reaction networks were reconstructed using LINEX2, followed by local topology exploration to identify highly interconnected modules. Mechanistic pathways governing gene-metabolite-lipid interactions were inferred via random walk with restart algorithms and validated by gene set enrichment analysis (GSEA).</p><p><strong>Results: </strong>Transcriptomics revealed extensive dysregulation of metabolic and lipid regulatory pathways in db/db. Metabolomic integration pinpointed perturbations within glycine-serine-threonine (Gly-Ser-Thr) metabolism as the most significantly perturbed pathway (P < 0.001), with cross-omics validation identifying GLUL as a pivotal regulatory gene through. Lipidomics uncovered pronounced abnormalities in cardiolipin species composition and plasmalogen profiles. Transcriptome-lipidome integration demonstrated impaired phosphatidylcholine (PC) biosynthesis, mechanistically linked to dysregulation of choline phosphotransferase 1 (<i>chpt1</i>), which correlated significantly with compromised tissue regeneration capacity.</p><p><strong>Conclusion: </strong>This multi-omics study systematically delineates the molecular landscape of DN pathogenesis, uncovering previously underappreciated metabolic perturbations and distinct lipid dysregulation patterns. Our findings elucidate mechanistic insights into extra-glycemic disease drivers and propose potential therapeutic targets for DN management.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3399-3409"},"PeriodicalIF":4.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin\" [Comput Struct Biotechnol J 27 (2025) 1286-1295].","authors":"Ella Barkan, Ibrahim Siddiqui, Kevin J Cheng, Alex Golts, Yoel Shoshan, Jeffrey K Weber, Yailin Campos Mota, Michal Ozery-Flato, Giuseppe A Sautto","doi":"10.1016/j.csbj.2025.07.007","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.07.007","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1016/j.csbj.2025.03.038.].</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3181"},"PeriodicalIF":4.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144741432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid framework for automated generation of mammography radiology reports.","authors":"Eduardo Godoy, Diego Mellado, Joaquin de Ferrari, Marvin Querales, Alex Saez, Steren Chabert, Denis Parra, Rodrigo Salas","doi":"10.1016/j.csbj.2025.07.018","DOIUrl":"10.1016/j.csbj.2025.07.018","url":null,"abstract":"<p><p>Breast cancer remains a significant health concern for women at various stages of life, impacting both productivity and reproductive health. Recent advancements in deep learning (DL) have enabled substantial progress in the automation of radiological reports, offering potential support to radiologists and streamlining examination processes. This study introduces a framework for automated clinical text generation aimed at assisting radiologists in mammography examinations. Rather than replacing medical expertise, the system provides pre-processed evidence and automatic diagnostic suggestions for radiologist validation. The framework leverages an encoder-decoder architecture for natural language generation (NLG) models, trained and fine-tuned on a corpus of Spanish radiological text. Additionally, we incorporate an image intensity enhancement technique to address the issue of image quality variability and assess its impact on report generation outcomes. A comparative analysis using NLG metrics is conducted to identify the optimal feature extraction method. Furthermore, named entity recognition (NER) techniques are employed to extract key clinical concepts and automate precision evaluations. Our results demonstrate that the proposed framework could be a solid starting point for systematizing and implementing automated clinical report generation based on medical images.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3229-3239"},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12309959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CaMutQC: An R package for integrative quality control and filtration of cancer somatic mutations.","authors":"Xin Wang, Tengjia Jiang, Ao Shen, Yaru Chen, Yanqing Zhou, Jie Liu, Shuhan Zhao, Shifu Chen, Jian Ren, Qi Zhao","doi":"10.1016/j.csbj.2025.07.011","DOIUrl":"10.1016/j.csbj.2025.07.011","url":null,"abstract":"<p><p>The quality control and filtration of cancer somatic mutations (CAMs), including the elimination of false positives due to technical bias and the selection of key mutation candidates, are crucial steps for downstream analysis in cancer genomics. However, due to diverse needs and the lack of standardized filtering criteria, the filtering strategies applied vary from study to study, often resulting in reduced efficiency, accuracy, and reproducibility. Here, we present CaMutQC, a heuristic quality control and soft-filtering R/Bioconductor package designed specifically for CAMs. CaMutQC enables users to remove false positive mutations, select potential mutation candidates, and estimate Tumor Mutation Burden (TMB) with a single line of code, using either default or customized parameters. A filter report and a code log can also be generated after the filtration process to facilitate reproducibility and comparison. The application of CaMutQC to a Whole-exome Sequencing (WES) benchmark dataset demonstrated its strong capability by eliminating 85.55 % of false positive Single nucleotide variants (SNVs) while retaining 90.72 % of true positive SNVs. Additionally, an additional 11.56 % of true positive SNVs were rescued through CaMutQC's built-in union strategy. Similar results were observed for Insertions and Deletions (INDELs). CaMutQC is freely available through Bioconductor at https://bioconductor.org/packages/CaMutQC/ under the GPL v3 license.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3147-3154"},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12302822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidencing the role of a conserved polar signaling channel in the activation mechanism of the μ-opioid receptor.","authors":"Arijit Sarkar, Szabolcs Dvorácskó, Zoltán Lipinszki, Argha Mitra, Mária Harmati, Krisztina Buzás, Attila Borics","doi":"10.1016/j.csbj.2025.07.014","DOIUrl":"10.1016/j.csbj.2025.07.014","url":null,"abstract":"<p><p>The activity of G protein-coupled receptors has been generally linked to dynamically interconverting structural and functional states and the process of activation was proposed to be controlled by an interconnecting network of conformational switches in the transmembrane domain. However, it is yet to be uncovered how ligands with different extent of functional effect exert their actions. According to our recent hypothesis, the transmission of the external stimulus is accompanied by the shift of macroscopic polarization in the transmembrane domain, furnished by concerted movements of conserved polar amino acids and the rearrangement of polar species. Previously, we have examined the μ-opioid, β₂-adrenergic and type 1 cannabinoid receptors by performing molecular dynamics simulations. Results revealed correlated dynamics of a polar signaling channel connecting the orthosteric binding pocket and the intracellular G protein-binding surface in all three class A receptors. In the present study, the interplay of this polar signaling channel in the activation mechanism was evidenced by systematic mutation of the channel residues of the μ-opioid receptor. Mutant receptors were analyzed utilizing molecular dynamics simulations and characterized <i>in vitro</i> by means of radioligand receptor binding and G protein stimulation assays. Apart from one exception, all mutants failed to bind the endogenous agonist endomorphin-2 and to stimulate the G_i protein complex. Furthermore, mutation results confirmed allosteric connection between the binding pocket and the intracellular surface. The strong association and optimal bioactive orientation of the bound agonist was found to be crucial for the initiation of correlated motions and consequent signaling.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3216-3228"},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12309854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images.","authors":"Nam Nhut Phan, Hanzhou Wang, Tapsya Nayak, Zhenqing Ye, Yu-Chiao Chiu, Yufang Jin, Yufei Huang, Yidong Chen","doi":"10.1016/j.csbj.2025.07.026","DOIUrl":"10.1016/j.csbj.2025.07.026","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to predict the cell types that infiltrate the tumor microenvironment using hematoxylin and eosin-stained images from colon cancer and breast cancer samples.</p><p><strong>Methods: </strong>Two datasets, one focused on colon cancer and the other on breast cancer, were used to develop deep learning models. Cell segmentation was performed using Stardist, followed by the K-Nearest Neighbor method to construct a neighborhood-enhanced cellular extraction matrix for model training. Transductive semi-supervised learning was applied to the breast cancer dataset, where the Base-4 model was trained on S1 and S2 samples and subsequently used to generate assigned labels for the S3, S4, and S5 sets, on which the Base-4+ model was trained.</p><p><strong>Results: </strong>The Base-7 model trained on colon cancer cell images achieved accuracy of 0.85 on the hold-out test set and 0.74- on the independent test set, with six neighboring cells identified as the optimal condition for prediction. In addition, the Base-4 model achieved a prediction accuracy of 0.69 with four neighboring cells as the optimal condition in the breast cancer dataset, while the Base-4+ model reached an accuracy of up to 0.93 on the validation set. The model also captured invasive and ductal carcinoma cells with overall agreement relative to spot-based cell types (0.63).</p><p><strong>Conclusions: </strong>Deep learning models accurately predicted cell types in breast and colon cancer datasets using only cell morphology and neighborhood embedding.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3182-3190"},"PeriodicalIF":4.1,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12305602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144741431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3plex Web: An interactive platform for RNA:DNA triplex prediction and analysis.","authors":"Marco Masera, Chiara Cicconetti, Francesca Ferrero, Salvatore Oliviero, Ivan Molineris","doi":"10.1016/j.csbj.2025.07.005","DOIUrl":"10.1016/j.csbj.2025.07.005","url":null,"abstract":"<p><p>Long non-coding RNAs (lncRNAs) exert their functions by cooperating with other molecules, including proteins and DNA. Triplexes, formed through the interaction between a single-stranded RNA (ssRNA) and a double-stranded DNA (dsDNA), have been consistently described as a mechanism that allows lncRNAs to target specific genomic sequences in vivo. Building on the computational tool 3plex, we developed 3plex Web, an accessible platform that enhances the prediction of RNA:DNA triplexes by integrating interactive visualization, statistical evaluation, and user-friendly downstream analysis workflows. 3plex Web implements new features such as input randomization for statistical assessments, interactive profile plotting for triplex stability, and customizable DNA Binding Domain (DBD) selection. This platform enables rapid analysis through PATO, substantially reducing processing times compared to previous methods, while offering Snakemake workflows to integrate gene expression data and explore lncRNA regulatory mechanisms.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3110-3113"},"PeriodicalIF":4.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12284569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144697831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ubigo-X: Protein ubiquitination site prediction using ensemble learning with image-based feature representation and weighted voting.","authors":"Disline Manli Tantoh, Jen-Chieh Yu, Ching-Hsuan Chien, Wei-Yi Yeh, Yen-Wei Chu","doi":"10.1016/j.csbj.2025.07.025","DOIUrl":"10.1016/j.csbj.2025.07.025","url":null,"abstract":"<p><p>Accurate ubiquitination identification is crucial in biological function analysis. We developed Ubigo-X, a novel protein ubiquitination prediction tool. Our training data, sourced from the Protein Lysine Modification Database (PLMD 3.0), comprised 53,338 ubiquitination and 71,399 non-ubiquitination sites, retained after CD-HIT and CD-HIT-2d sequence filtering. Three sub-models: Single-Type sequence-based features (Single-Type SBF), k-mer sequence-based features (Co-Type SBF), and structure-based and function-based features (S-FBF), were developed. Single-Type SBF used amino acid composition (AAC), amino acid index (AAindex), and one-hot encoding; Co-Type SBF used Single-Type SBF via k-mer encoding; and S-FBF used secondary structure, relative solvent accessibility (RSA)/absolute solvent-accessible area (ASA), and signal peptide cleavage sites. S-FBF was trained using XGBoost, while Single-Type SBF and Co-Type SBF were transformed into image-based features and trained using Resnet34. Ubigo-X was developed by combining the three models via a weighted voting strategy. Independent testing using PhosphoSitePlus data (65,421 ubiquitination and 61,222 non-ubiquitination sites) retained after filtering yielded 0.85, 0.79, and 0.58 for area under the curve (AUC), accuracy (ACC), and Matthews correlation coefficient (MCC), respectively. Further testing on imbalanced PhosphoSitePlus data (1:8 positive-to-negative sample ratio) yielded 0.94 AUC, 0.85 ACC, and 0.55 MCC. Using the GPS-Uber data, the AUC, ACC, and MCC were 0.81, 0.59, and 0.27, respectively. In conclusion, Ubigo-X outperformed existing tools in MCC (for both balanced and unbalanced data) and AUC and ACC (for balanced data), highlighting the efficacy of integrating image-based feature representation and weighted voting in ubiquitination prediction. Ubigo-X is a potential species-neutral ubiquitination site prediction tool, accessible at http://merlin.nchu.edu.tw/ubigox/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3137-3146"},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence insight on structural basis and small molecule binding niches of NMDA receptor.","authors":"Yunsheng Liu, Han Tang, Jinfang Zhang, Dan Li, Zengwei Kou","doi":"10.1016/j.csbj.2025.07.027","DOIUrl":"10.1016/j.csbj.2025.07.027","url":null,"abstract":"<p><p>NMDA receptors are critical to neuronal activity and play essential roles in synaptic transmission, learning, and memory. Despite significant advances in X-ray crystallography and cryo-electron microscopy (cryo-EM), the structural diversity of NMDA receptors across species and the variations among receptor subtypes within the same species remain insufficiently explored. Additionally, several key small molecule binding sites, such as those for agonists, antagonists, and allosteric modulators, have not been fully characterized. In this study, we utilized state-of-the-art artificial intelligence algorithms to model NMDA receptors across multiple species and found that they all adopted a bouquet-like dimer-of-dimer structure. By comparing these models with cryo-EM resolved structures, we assessed the accuracy of the predictions and complemented the structural data with detailed models of transmembrane domain regions, which are traditionally challenging for experimental methods. Furthermore, through the integration of AI-based prediction tools and molecular dynamic simulations, we highlighted potential binding sites for agonists, competitive antagonists, and pore blockers at amino acid resolution. This AI-enhanced approach builds traditional structural biology techniques, revealing that NMDA receptors from different species adopt highly similar three-dimensional architectures, while also exhibiting subtype-specific structural features. Furthermore, our identification of ligand binding pockets at the amino acid resolution provides a more detailed understanding of receptor-ligand interactions, offering potential templates for rational drug design and optimization.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3167-3180"},"PeriodicalIF":4.1,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12302824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards predicting implant-induced fibrosis: A standardized network model of macrophage-fibroblast interactions.","authors":"Matilde Marradi, Martijn van Griensven, Nick R M Beijer, Jan de Boer, Aurélie Carlier","doi":"10.1016/j.csbj.2025.07.022","DOIUrl":"10.1016/j.csbj.2025.07.022","url":null,"abstract":"<p><p>The foreign body response (FBR) is a complex and multifaceted process that remains incompletely understood, often leading to complications in medical device integration. In this study, we constructed a literature-based network of the FBR and developed it into a semi-quantitative predictive model to better understand its dynamics. The <i>in silico</i> FBR model incorporates key material-related factors, including immunogenic properties and mechanical mismatch, which influence immune cell activation and extracellular matrix (ECM) deposition. Predictions align with existing knowledge, showing that material stiffness and tissue progressive stiffening due to increased ECM deposition can exacerbate the FBR and that feedback interactions can protect the system from pathological outcome by gradually reducing the initial inflammatory input. The model also successfully replicated six out of eight experimental cases of anti-fibrotic interventions, demonstrating its potential as a predictive tool. Assessing implant safety in the early pre-clinical stages of device development is critical for ensuring long-term functionality and reducing adverse reactions. By systematically analyzing and integrating all interacting aspects of the FBR, <i>in silico</i> modeling can provide valuable insights and complement <i>in vitro</i> and <i>in vivo</i> studies for improved implant safety assessment.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3251-3263"},"PeriodicalIF":4.1,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144759395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}