Computational and structural biotechnology journal最新文献

{"title":"Estimating the effect of tissue- and blood-derived cell reference matrices on deconvolving bulk transcriptomic datasets.","authors":"Siqi Sun, Shweta Yadav, Mulini Pingili, Dan Chang, Jing Wang","doi":"10.1016/j.csbj.2025.07.058","DOIUrl":"10.1016/j.csbj.2025.07.058","url":null,"abstract":"<p><p>Cell deconvolution is a widely used method to characterize the composition of the mixed cell population in bulk transcriptomic datasets. While tissue- and blood-derived cell reference matrices (CRMs) are commonly used, their impact on deconvolution accuracy has yet to be systematically evaluated. In this study, we developed tissue- and blood-derived CRMs using single-cell RNA sequencing (scRNA-seq) data from inflammatory bowel disease (IBD). Three publicly available blood-derived CRMs (IRIS, LM22, and ImmunoStates) were incorporated for benchmarking. Deconvolution performance was evaluated using both public bulk transcriptomic datasets and simulated pseudobulk samples by goodness-of-fit and cell fractions correlation. Two infliximab-treated bulk datasets were used to identify treatment-related cell types. In addition, lung adenocarcinoma (LUAD) single-cell and bulk transcriptomic datasets were also used for deconvolution evaluation. We found tissue-derived CRMs consistently outperformed blood-derived CRMs in deconvolving bulk tissue transcriptomes, exhibiting higher goodness-of-fit and more accurate cellular proportion estimates, particularly for immune and stromal cells. They also revealed more treatment-related cell types. In contrast, all CRMs performed similarly when applied to blood bulk transcriptomics. These trends also were shown in the LUAD datasets. Our results emphasize the importance of selecting appropriate CRMs for cell deconvolution in bulk tissue transcriptomes, particularly in immunology and oncology. Such considerations can be extended to encompass other disease implications. The R package (DeconvRef) for building user-defined CRMs is available at https://github.com/alohasiqi/DeconvRef.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3579-3588"},"PeriodicalIF":4.1,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871834","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":"Exploration of structural space of Cus17 lectin with glycans using molecular docking and simulation studies.","authors":"Vikas Tiwari, Aditi Pathak, Tejaswini Poojary, Ramanathan Sowdhamini, Avadhesha Surolia","doi":"10.1016/j.csbj.2025.07.028","DOIUrl":"10.1016/j.csbj.2025.07.028","url":null,"abstract":"<p><p>The Cus17 phloem protein, in the case of <i>Cucumis sativus</i> species, plays an important role in the phloem-based defense of the plant. Cus17 can bind to various carbohydrates present on insect exoskeletons or fungal cells. The recent experimental structure of chitotriose bound Cus17 elucidates the carbohydrate interacting residues of Cus17. Higher chito-oligosaccharides are also known to interact with Cus17, but the lack of experimental structure impedes our understanding of their interaction. In this study, we have employed <i>in-silico</i> methods to explore the binding interactions of higher chito-oligosaccharides with Cus17. Chitoheptaose forms stable interactions with canonical binding site residues Trp48 and Asp50. Smaller chito-oligosaccharides were observed to be relatively unstable at the canonical binding site of Cus17. Further, the chito-oligosaccharides were inspected for interactions with predicted ligand binding sites. We also generated different tetramers of Cus17 and docked the chito-oligosaccharides to the tetrameric Cus17. All chito-oligosaccharides were found to make persistent interactions with Cus17 tetramer. Interestingly, chitotriose shows the best binding affinity and maintains stable interactions with Cus17 tetramer upon extended simulations with the canonical site.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3319-3327"},"PeriodicalIF":4.1,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12337204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144820767","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":"gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes.","authors":"Leon Fehse, Mohammad Tajabadi, Roman Martin, Hajo Holzmann, Dominik Heider","doi":"10.1016/j.csbj.2025.07.031","DOIUrl":"10.1016/j.csbj.2025.07.031","url":null,"abstract":"<p><p>Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncovering the microbiome's impact on these conditions. Differential abundance analysis (DAA) can detect significant changes between groups of patients. However, since individuals have unique microbial fingerprints that could potentially be identifiable, microbiome data must be treated as sensitive patient data, which poses problems for collaborative studies in the medical field. In this work, we introduce gLinDA, a global differential abundance analysis tool that employs a privacy-preserving swarm learning approach for the analysis of distributed datasets. gLinDA maintains predictive performance while safeguarding patient sensitive data.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3456-3463"},"PeriodicalIF":4.1,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12345345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844829","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":"Computational nanobody design through deep generative modeling and epitope landscape profiling.","authors":"Liyun Huo, Tian Tian, Yanqin Xu, Qin Qin, Xinyi Jiang, Qiang Huang","doi":"10.1016/j.csbj.2025.07.052","DOIUrl":"10.1016/j.csbj.2025.07.052","url":null,"abstract":"<p><p>Nanobodies, one-tenth the size of conventional antibodies, have gained attention as therapeutic agents for autoimmune diseases, cancer, and viral infections. However, traditional methods for nanobody discovery are often time-consuming and labor-intensive. In this study, we present a computational design framework that integrates deep generative modeling with epitope profiling. We first developed a generative adversarial network (GAN)-based model named AiCDR, which incorporates two external discriminators to enhance its ability to distinguish native CDR3 sequences from random sequences and peptides. This design enables the generator to produce CDR3 sequences with natural-like properties. Approximately 10,000 CDR3 sequences were generated and grafted onto a humanized scaffold. After structural prediction, we obtained a library of about 5200 high-confidence nanobody models. Using this structure-based library, we conducted epitope profiling across six representative protein targets. The nanobody-enriched epitopes showed strong overlap with known functional regions, suggesting potential biological activity. As a case study, we selected ten nanobodies designed to target the SARS-CoV-2 Omicron RBD. Two of these showed detectable neutralization activity in vitro. Overall, our results demonstrate that computational design and structure-based profiling offer an efficient strategy for early-stage therapeutic nanobody discovery.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3443-3455"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834403","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":"Diosgenin producing <i>Bacillus</i> sp. strain IRMC27M2 as a genome-mined weapon against multidrug-resistant <i>Candidozyma (Candida) auris</i>.","authors":"Rahaf Alquwaie, Noor B Almandil, Reem AlJindan, Nehal Mahmoud, Sarah Almofty, Dana Almohazey, Hoor Hashim Alqudihi, Sarah Hunachagi, Tharmathass Stalin Dhas, P Sowmiya, Sayed AbdulAzeez, J Francis Borgio","doi":"10.1016/j.csbj.2025.07.048","DOIUrl":"10.1016/j.csbj.2025.07.048","url":null,"abstract":"<p><p><i>Candidozyma auris</i> (<i>Candida auris</i>) is an emerging multidrug-resistant (MDR) fungal pathogen prioritised by the World Health Organisation that poses a significant global health threat due to high mortality. Discovering novel antifungal drugs is crucial for effective treatment. This study identifies and describes a native bacterial isolate, <i>Bacillus</i> sp. strain IRMC27M2, with anti-<i>C. auris</i> activity. An integrated approach was used, including <i>16S rRNA</i> gene sequencing to identify the bacterial isolate, followed by whole-genome sequencing, antifungal analysis, cytotoxicity testing, gas chromatography-mass spectrometry (GC-MS) analysis and comparative genomics. The IRMC27M2 genome was sequenced using nanopore long-read sequencing and the resulting genome (3,87,328 bp) is phylogenetically related to <i>Bacillus amyloliquefaciens</i> and <i>Bacillus velezensis</i>. Biosynthesis-related gene clusters (BGCs) were identified in the IRMC27M2 genome. Media optimisation protocols (FRC6 and REM3) were performed to enhance secondary metabolite production and the resulting ethyl acetate fractions were analysed by UV spectrophotometry and GC-MS. Antifungal analysis with metabolites from <i>Bacillus</i> sp. strain IRMC27M2 significantly reduced cell size and induced crushed phenotypes in <i>C. albicans</i> and <i>C. auris</i>. Collapse of cell membranes and lysis of cells were observed. Whole-genome sequencing revealed 10 BGCs potentially involved in antifungal compound biosynthesis. The metabolites produced using FRC6 and REM3 protocols showed no cytotoxic effects. GC-MS analysis of the ethyl acetate fraction revealed a range of metabolites, with diosgenin being the most abundant. Manual and reverse verification confirmed the presence of genes linked to the methylerythritol phosphate biosynthesis pathway and confirmed the capability of IRMC27M2 for diosgenin production. In conclusion, the findings highlight the significant potential of <i>Bacillus</i> sp. strain IRMC27M2 as a biofactory for the production of diosgenin. This signifies promising future research for developing treatments against multidrug-resistant <i>C. albicans</i> and <i>C. auris</i> which are prioritised by the WHO. Further research is necessary to confirm if <i>Bacillus</i> sp. strain IRMC27M2 represents a novel subspecies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3410-3432"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341532/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834404","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":"Role of ferroptosis-related IREB2 in the shared genetic etiology between smoking and facial aging: Insights from large-scale genome-wide cross-trait analysis.","authors":"Xueyao Cai, Weidong Li, Wenjun Shi, Yuchen Cai, Jianda Zhou","doi":"10.1016/j.csbj.2025.07.049","DOIUrl":"10.1016/j.csbj.2025.07.049","url":null,"abstract":"<p><p>While the association between smoking and accelerated facial aging is well documented, the specific pathways underlying this association remain poorly understood. To investigate the shared genetic architecture between smoking and facial aging, we performed genetic analyses based on genome-wide association studies (GWAS) data. These analyses included linkage disequilibrium score regression (LDSC), pleiotropic analysis under composite null hypothesis (PLACO), functional mapping and annotation (FUMA), and multi-marker analysis of genomic annotation (MAGMA). To further explore the shared target genes, we utilized expression quantitative trait loci (eQTLs) and mediation Mendelian randomization (MR) analysis, with subsequent validation conducted through <i>in vitro</i> experiments using NIH/3T3 cells. Additionally, we carried out pan-cancer correlation analyses to assess the broader implications of the identified genes in cancer biology. Through pleiotropy and colocalization analyses, IREB2, along with CHRNA5 and AARS1, were identified as having strong evidence linking smoking and facial aging. Functional enrichment, tissue-specific analyses, and gene co-expression network were conducted to further elucidate the functions of these genes. Following eQTLs and mediation analyses, IREB2 was identified as a potential mediator connecting smoking to facial aging. Cellular experiments demonstrated that exposure to cigarette smoke particles induces cellular senescence and downregulates IREB2 expression. The pan-cancer analysis highlighted IREB2's role in shaping the tumor microenvironment and influencing immune processes. This study identifies IREB2 as a critical factor in the molecular mechanisms by which smoking accelerates facial aging, while also contributing to tumor development and immune evasion. Further functional exploration of IREB2 could uncover new therapeutic avenues to address these interconnected conditions.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3433-3442"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834406","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":"A molecular dynamics protocol for rapid prediction of EGFR overactivation and its application to the rare mutations S768I, S768N, D761N.","authors":"Julian Behn, R N V Krishna Deepak, Jiancheng Hu, Hao Fan","doi":"10.1016/j.csbj.2025.07.046","DOIUrl":"10.1016/j.csbj.2025.07.046","url":null,"abstract":"<p><p>Hyperactivation caused by mutations in the Epidermal Growth Factor Receptor (EGFR) kinase domain is implicated in various diseases, including cancer. However, the structural mechanisms underlying overactivation in many EGFR mutations remain poorly understood, and exploring these mechanisms through conventional experiments or <i>in silico</i> simulations is often labor- and cost-intensive. Here, we establish a Molecular Dynamics (MD) protocol capable of rapidly revealing EGFR mutant modes of action using multiple short simulations. We first simulated wild-type EGFR and the well-studied oncogenic mutations L858R and T790M/L858R under different simulation conditions, to derive a protocol which could recapitulate their experimentally established behavior. We then applied this protocol to three rare EGFR mutations: S768I, S768N, and D761N. Experimental studies have suggested that S768I and D761N are oncogenic, whereas S768N is likely a neutral mutation that does not significantly alter EGFR activity. Our simulation results were consistent with these functional indications and provided the corresponding molecular bases - S768I and S768N affect the orientation and stability of the catalytically important αC-helix, while D761N introduces a new hydrogen bonding network between the αC-helix and activation loop. Collectively, the protocol presented here provides a robust and rapid framework for characterizing EGFR mutation mechanisms and is readily adaptable to novel or uncharacterized variants.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3370-3378"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834402","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":"Dynamic energy conversion in protein catalysis: From brownian motion to enzymatic function.","authors":"Sarfaraz K Niazi","doi":"10.1016/j.csbj.2025.07.050","DOIUrl":"10.1016/j.csbj.2025.07.050","url":null,"abstract":"<p><p>Recent advances in computational biology and experimental techniques reveal that enzymatic catalysis fundamentally depends on proteins' ability to harness thermal energy through conformational fluctuations. Rather than functioning as rigid molecular locks, proteins operate as dynamic machines that continuously sample different structural states, with α-helices and β-sheets acting as sophisticated energy transduction elements that capture Brownian motion and channel it toward productive chemical transformations. Molecular dynamics simulations, combined with machine learning tools such as AlphaFold, demonstrate that these conformational dynamics directly modulate substrate binding affinity and reaction pathway selection, suggesting that proteins actively convert environmental thermal noise into catalytic work rather than merely stabilizing transition states. This dynamic energy conversion paradigm fundamentally reshapes our approach to pharmaceutical design and enzyme engineering by emphasizing the targeting of conformational ensembles rather than static structures, while also raising important questions about the universal applicability of this mechanism across all enzyme classes and the experimental methodologies needed to validate dynamic catalytic models. The shift from viewing proteins as passive structural scaffolds to active energy converters represents a transformative reconceptualization of biological catalysis with far-reaching implications for our understanding of life's molecular machinery.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3337-3369"},"PeriodicalIF":4.1,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12337020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144820766","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":"<i>In silico</i> characterization of Ciwujianoside E: Structural features, solvation dynamics, and eco-toxicological assessment.","authors":"A Marta Navarro-Cuñado, María J Tapia, Sergio de-la-Huerta-Sainz, Alberto Gutiérrez, Santiago Aparicio","doi":"10.1016/j.csbj.2025.07.041","DOIUrl":"10.1016/j.csbj.2025.07.041","url":null,"abstract":"<p><p>This work presents an in-depth characterization of Ciwujianoside E through Density Functional Theory (DFT) and Quantum Theory of Atoms in Molecules (QTAIM) analyses. We investigated multiple conformers, revealing the critical electronic and geometric properties that influence molecular behavior. This study includes electron density distributions and topological characteristics defining the structural integrity, along with a detailed hydrogen bonding network analysis. High-level quantum mechanical calculations provide precise geometric optimization for various conformer configurations. Complementary, molecular docking studies have assessed interactions with human proteins and plasma membranes, elucidating binding mechanisms with potential pharmacological and/or toxicological significance. Likewise, the possibility of using Deep Eutectics Solvents (DES) for the extraction of Ciwujianoside E as an environmentally friendly extraction procedure was considered when designing suitable molecular combinations to improve affinity and target molecule solubility. Moreover, the solvation mechanism(s) of Ciwujianoside E in water and Deep Eutectic Solvents were analyzed <i>via</i> Molecular Dynamics simulations. This integrated computational approach provides a comprehensive insight into the molecular characteristics of Ciwujianoside E.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3379-3398"},"PeriodicalIF":4.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834401","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":"Tokenization and deep learning architectures in genomics: A comprehensive review.","authors":"Conrad Testagrose, Christina Boucher","doi":"10.1016/j.csbj.2025.07.038","DOIUrl":"10.1016/j.csbj.2025.07.038","url":null,"abstract":"<p><p>The development of modern DNA sequencing technologies has resulted in the rapid growth of genomic data. Alongside the collection of this data, there is an increasing need for the development of modern computational tools leveraging this data for tasks including but not limited to antimicrobial resistance and gene annotation. Current deep learning architectures and tokenization techniques have been explored for the extraction of meaningful underlying information contained within this sequencing data. We aim to survey current and foundational literature surrounding the area of deep learning architectures and tokenization techniques in the field of genomics. Our survey of the literature outlines that significant work remains in developing efficient tokenization techniques that can capture or model underlying motifs within DNA sequences. While deep learning models have become more efficient, many current tokenization methods either reduce scalability through naive sequence representation, incorrectly model motifs or are borrowed directly from NLP tasks for use with biological sequences. Current and future model architectures should seek to implement and support more advanced, and biologically relevant, tokenization techniques to more effectively model the underlying information in biological sequencing data.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"3547-3555"},"PeriodicalIF":4.1,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871846","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}