Isaac Meza-Padilla, Andrew C Doxey, Jozef I Nissimov
{"title":"Cyanobacteriochrome-like GAF folds in phages revealed via AlphaFold proteomic modelling.","authors":"Isaac Meza-Padilla, Andrew C Doxey, Jozef I Nissimov","doi":"10.1016/j.csbj.2025.09.020","DOIUrl":"10.1016/j.csbj.2025.09.020","url":null,"abstract":"<p><p>Accurate protein structure prediction followed by structural homology detection enable the functional annotation of otherwise obscure viral protein-coding genes. Here we employ AlphaFold proteomic modelling and structural homology searches on the genome of CrV-01T, a representative freshwater cyanophage, to reveal previously unknown structural homologs. One of these cryptic viral proteins is found to be a cyanobacteriochrome-like GAF fold (CGF) protein. Cyanobacteriochromes (CBCRs) are known to regulate phototaxis, cyclic nucleotide metabolism and optimization of light harvesting in cyanobacteria. Phylogenetic analyses indicate that the CGF protein of CrV-01T was probably acquired from a cyanobacterial host. We then use experimentally determined CBCR structures to query the Big Fantastic Virus Database and discover that CGFs are present among many different bacteriophages. The GAF domain sequence, which is a hallmark of CBCRs, can still be detected in some of these divergent viral proteins. Remarkably, viral CGF proteins harbor an N-terminal extension that in most cases is predicted to contain a transmembrane α-helix, indicating that they may bind the host membrane after being synthesized in the virocell. The presence of CGF protein-coding genes in cyanophage genomes suggests novel ways in which viruses may manipulate the metabolism of cyanobacteria, the most abundant oxygenic phototrophs on Earth. Overall, the findings reported here emphasize the importance of applying structural homology detection methods when annotating viral genomes and highlight the potential of AlphaFold for exploring the dark matter of the aquatic virosphere.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4089-4095"},"PeriodicalIF":4.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205623","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":"iComBat: An incremental framework for batch effect correction in DNA methylation array data.","authors":"Yui Tomo, Ryo Nakaki","doi":"10.1016/j.csbj.2025.09.014","DOIUrl":"10.1016/j.csbj.2025.09.014","url":null,"abstract":"<p><p>DNA methylation is associated with various diseases and aging; thus, longitudinal and repeated assessments of methylation patterns are crucial for revealing the mechanisms of disease onset and identifying factors associated with aging. The presence of batch effects influences the analysis of DNA methylation array data. As existing methods for correcting batch effects are designed to correct all samples simultaneously, when data are incrementally measured and included, the correction of newly added data affects previous data. Therefore, we propose an incremental framework for batch-effect correction based on ComBat, a location/scale adjustment approach using a Bayesian hierarchical model, and empirical Bayes estimation. Using numerical experiments and application to actual data, we demonstrate that the proposed method can correct newly included data without re-correcting the old data. The proposed method is expected to be useful for studies involving repeated measurements of DNA methylation, such as clinical trials of anti-aging interventions.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4121-4131"},"PeriodicalIF":4.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145231440","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":"In-depth analysis of the RNA editing landscape in intracranial aneurysms and its potential role in alternative splicing.","authors":"Yulan Wang, Qingqing Li, Peipei Wang, Tianyi Xu, Xintong Zhao, Mingquan Ye","doi":"10.1016/j.csbj.2025.09.021","DOIUrl":"10.1016/j.csbj.2025.09.021","url":null,"abstract":"<p><p>Intracranial aneurysm (IA) is a focal localized dilation of cerebral arteries and is a life-threatening cerebrovascular disease. Emerging evidences have emphasized the significance of post-transcriptional regulation in diseases, particularly through the two most critical regulatory layers of RNA editing (RE) and alternative splicing (AS). However, the interplay between these mechanisms and their impact on IA pathophysiology remains unclear. This study integrated multi-cohort datasets to establish a comprehensive landscape of RE in IAs. We observed a marked decrease in RNA editing levels during the transition from unruptured to ruptured aneurysms. Further analysis revealed a dual mechanism of AS by RE: direct modulation of AS via edits near splice sites that alter regulatory sequences, and indirect influence through changes in the binding affinity and specificity of RNA-binding proteins (RBPs). By constructing an RES-RBP-AS regulatory network, we identified key nodes potentially involved in IA progression via RE-mediated splicing regulation. These findings not only provide new insights into IA molecular mechanisms, but also lay a theoretical foundation for the developing therapies strategies targeting post-transcriptional regulation.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4163-4172"},"PeriodicalIF":4.1,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12506588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257496","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}
Umut Çakır, Noujoud Gabed, Yunus Emre Köroğlu, Selen Kaya, Senjuti Sinharoy, Vagner A Benedito, Marie Brunet, Xavier Roucou, Igor S Kryvoruchko
{"title":"Discovery of diverse chimeric peptides in a eukaryotic proteome sets the stage for experimental validation of the mosaic translation hypothesis.","authors":"Umut Çakır, Noujoud Gabed, Yunus Emre Köroğlu, Selen Kaya, Senjuti Sinharoy, Vagner A Benedito, Marie Brunet, Xavier Roucou, Igor S Kryvoruchko","doi":"10.1016/j.csbj.2025.09.019","DOIUrl":"10.1016/j.csbj.2025.09.019","url":null,"abstract":"<p><p>The high complexity of eukaryotic organisms enabled their evolutionary success, driven by the diversification of their proteomes. Various mechanisms contributed to this process. Alternative splicing had the largest known impact among these mechanisms. Earlier, we hypothesized that along with alternative splicing, a different but conceptually similar mechanism creates novel versions of existing proteins in all eukaryotes. However, this mechanism operates at the level of translation, where amino acid sequence novelty arises through multiple programmed ribosomal frameshifting events occurring within the same transcript. This mechanism, which is termed mosaic translation, is very difficult to demonstrate even with the most up-to-date molecular tools. Thus, it remained unnoticed so far. Using a subset of mass spectrometry proteomic data from various organs of the model plant <i>Medicago truncatula</i>, we took the first step toward experimental validation of this hypothesis. Our original <i>in silico</i> approach resulted in the discovery of two candidates for mosaic proteins (homologs of EF1α and RuBisCo) and 154 candidates for chimeric peptides. Chimeric peptides and polypeptides are produced in the course of one ribosomal frameshifting event and may correspond to parts of mosaic proteins. In addition, our analysis reveals the possibility of translation of chimeric peptides from five ribosomal RNA transcripts, ten long non-coding RNA transcripts, and one transfer RNA transcript. These findings are novel and will form the basis for future experimental validation. We also present multiple lines of indirect evidence supporting the validity of our <i>in silico</i> data.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4048-4064"},"PeriodicalIF":4.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205640","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}
Kirill E Medvedev, R Dustin Schaeffer, Nick V Grishin
{"title":"DrugDomain 2.0: Comprehensive database of protein domains-ligands/drugs interactions across the whole Protein Data Bank.","authors":"Kirill E Medvedev, R Dustin Schaeffer, Nick V Grishin","doi":"10.1016/j.csbj.2025.09.018","DOIUrl":"10.1016/j.csbj.2025.09.018","url":null,"abstract":"<p><p>Proteins carry out essential cellular functions - signaling, metabolism, transport - through the specific interaction of small molecules and drugs within their three-dimensional structural domains. Protein domains are conserved folding units that, when combined, drive evolutionary progress. The Evolutionary Classification Of protein Domains (ECOD) places domains into a hierarchy explicitly built around distant evolutionary relationships, enabling the detection of remote homologs across the proteomes. Yet no single resource has systematically mapped domain-ligand interactions at the structural level. To fill this gap, we introduce DrugDomain v2.0, an updated comprehensive resource, that extends earlier releases by linking evolutionary domain classifications (ECOD) to ligand binding events across the entire Protein Data Bank. We also leverage AI-driven predictions from AlphaFold to extend domain-ligand annotations to human drug targets lacking experimental structures. DrugDomain v2.0 catalogs interactions with over 37,000 PDB ligands and 7560 DrugBank molecules, integrates more than 6000 small-molecule-associated post-translational modifications, and provides context for 14,000 + PTM-modified human protein models featuring docked ligands. The database encompasses 43,023 unique UniProt accessions and 174,545 PDB structures. The DrugDomain data is available online: https://drugdomain.cs.ucf.edu/ and https://github.com/kirmedvedev/DrugDomain.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4040-4047"},"PeriodicalIF":4.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184821","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}
Xinfeng Li, Xinyu Tao, Mingyue Zhong, Yiyao Wang, Heng Xue, Binda T Andongma, Shan-Ho Chou, Hongping Wei, Jin He, Hang Yang
{"title":"Computational epitope profiling and AI-driven protein engineering enable rational design of multi-epitope vaccines against <i>Mycobacterium tuberculosis</i>.","authors":"Xinfeng Li, Xinyu Tao, Mingyue Zhong, Yiyao Wang, Heng Xue, Binda T Andongma, Shan-Ho Chou, Hongping Wei, Jin He, Hang Yang","doi":"10.1016/j.csbj.2025.09.015","DOIUrl":"10.1016/j.csbj.2025.09.015","url":null,"abstract":"<p><p>Tuberculosis (TB), caused by <i>Mycobacterium tuberculosis</i> (Mtb), remains a major global health threat, accounting for approximately 1.5 million deaths annually. The rise of antibiotic-resistant strains further complicates treatment efforts. While vaccination is a cornerstone of disease control, the only licensed TB vaccine, Bacille Calmette-Guérin (BCG), shows limited efficacy in adults. There is thus a critical need for more effective vaccines. Multi-epitope vaccines, which incorporate key epitopes from multiple antigens, offer a promising strategy by eliciting both humoral and cellular immunity. Here, we employed a comparative epitopomics approach to identify immunodominant epitopes from eight major Mtb antigens and selected 17 potent epitopes for the design of a multi-epitope antigen. Using AI-driven protein design, we systematically optimized epitope arrangement and flanking sequences to generate a stable, structurally integrated antigen-MtbEpi-17. Computational analyses suggest that MtbEpi-17 can effectively interact with TLR2 and TLR4, potentially stimulating robust innate and adaptive immune responses. Our study provides a rational design framework for multi-epitope vaccines, and proposes MtbEpi-17 as a strong candidate for further preclinical and clinical evaluation.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4065-4077"},"PeriodicalIF":4.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205712","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}
Yongheng Wang, Taihang Liu, Yijie He, Yaqin Tang, Pengcheng Tan, Lin Huang, Dongyu Huang, Tong Wen, Lizhen Shao, Jia Wang, Yingxiong Wang, Zhijie Han
{"title":"Identifying Alzheimer's disease-related pathways based on whole-genome sequencing data.","authors":"Yongheng Wang, Taihang Liu, Yijie He, Yaqin Tang, Pengcheng Tan, Lin Huang, Dongyu Huang, Tong Wen, Lizhen Shao, Jia Wang, Yingxiong Wang, Zhijie Han","doi":"10.1016/j.csbj.2025.09.013","DOIUrl":"10.1016/j.csbj.2025.09.013","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a highly inheritable neurodegenerative disorder for which pathway-specific genetic profiling provides insights into its key biological mechanisms and potential treatment targets. Traditional disease-pathway analyses for AD have certain limitations, such as environmental interference and arbitrary sample division. We present a comprehensive framework that starts with genome data, avoiding these drawbacks and offering intrinsic pathway-specific genetic profiling for AD. Whole genome sequencing data from 173 individuals were used to quantify transcriptomes in 14 brain regions, estimate individual-level pathway variant scores, and analyze AD risk for each patient. These results were combined to identify AD-related pathways and quantify their interactions. The predicted expression levels were consistent with previous findings, and the estimated AD risk showed a significant correlation with Braak/Thal scores. A total of 3798 pathways were identified as potentially associated with AD, with about 19.7 % previously reported. The pathways identified as AD risk related primarily address six core biological themes, including: Immunity and inflammation, Metabolism, Protein homeostasis, DNA/RNA and Epigenetics, Synapse and structure, Cell cycle. Specifically, key pathways, such as NF-κB signaling and GSK3β activation, were linked to AD pathogenesis. The interactions among pathways highlighted shared gene functions in AD. In summary, we provided an effective framework for disease-pathway analysis, revealing the interdependence or compensatory effects of pathways in AD.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4132-4144"},"PeriodicalIF":4.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145231670","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}
Juan Manuel Olaguez-Gonzalez, Isaac Chairez, Luz Breton-Deval, Mariel Alfaro-Ponce
{"title":"<i>In-silico</i> assessment of dynamic symbiotic microbial interactions in a reduced microbiota related to the autism spectrum disorder symptoms.","authors":"Juan Manuel Olaguez-Gonzalez, Isaac Chairez, Luz Breton-Deval, Mariel Alfaro-Ponce","doi":"10.1016/j.csbj.2025.09.006","DOIUrl":"10.1016/j.csbj.2025.09.006","url":null,"abstract":"<p><p>The gut microbiota plays a crucial role in human health, with growing evidence linking its composition to the development of Autism Spectrum Disorder. However, inconsistencies in previous studies have hindered the identification of a definitive microbial signature associated with Autism Spectrum Disorder. Machine learning models have emerged as powerful tools for analyzing microbiome data, yet their interpretability remains limited. In this study, we integrate <i>in silico</i> simulations with machine learning predictions to explore microbial interactions under different dietary conditions and provide biological context to features of the intestinal microbiota that are linked to Autism Spectrum Disorder. This study employs constraint-based modeling to simulate metabolic exchanges among key bacterial taxa in order to assess their ecological relationships. Findings reveal that high-fiber diets foster mutualistic and balanced interactions, whereas Western-style diets promote competitive and parasitic dynamics, potentially contributing to gut dysbiosis in Autism Spectrum Disorder. In addition, the presence of oxygen (a factor associated with colonocyte permeability, a pathological condition of the colon) significantly alters microbial interactions, influencing metabolic dependencies and the overall structure of the community. This integrative approach enhances the interpretability of machine learning-based Autism Spectrum Disorder classifiers, bridging computational predictions with mechanistic insights. By identifying diet-dependent microbial interactions, our study highlights potential dietary interventions to modulate the composition of the gut microbiota in Autism Spectrum Disorder. These findings underscore the value of combining <i>in silico</i> modeling and machine learning for unraveling complex microbiome-host relationships and improving Autism Spectrum Disorder biomarker identification.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4078-4088"},"PeriodicalIF":4.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205688","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}
Elliot Sicheri, Daniel Mao, Michael Tyers, Frank Sicheri
{"title":"Analysis of Fbox substrate adapter proteins using <i>ProteoSync</i>, a program for projection of evolutionary conservation onto protein atomic coordinates.","authors":"Elliot Sicheri, Daniel Mao, Michael Tyers, Frank Sicheri","doi":"10.1016/j.csbj.2025.09.012","DOIUrl":"10.1016/j.csbj.2025.09.012","url":null,"abstract":"<p><p>The projection of conservation onto the surface of a protein's 3D structure is a powerful way of inferring functionally important regions. For this reason, we created ProteoSync, a Python program that semi-automates the process. The program creates an annotated sequence alignment of orthologs from a diverse set of selectable species and enables the fast projection of amino acid conservation onto a predicted or known 3D model in PyMOL <sup>1</sup>. As a test case, we used ProteoSync to analyze a subset of 31 F-box proteins, which function as substrate recognition subunits for a large family of Cul1-based E3 ubiquitin ligases. We correctly identified known substrate interaction surfaces for 11 F-box members with previously solved structures. We also identified likely ligand binding sites for 16 other members, thus demonstrating ProteoSync's utility for discovering conserved, functionally relevant surfaces.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4026-4039"},"PeriodicalIF":4.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184817","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":"Epigenetic memory: The role of the crosstalk between histone modifications and DNA methylation.","authors":"Domitilla Del Vecchio","doi":"10.1016/j.csbj.2025.08.034","DOIUrl":"10.1016/j.csbj.2025.08.034","url":null,"abstract":"<p><p>Epigenetic memory allows different cells to maintain distinct gene expression patterns despite a common genetic code and plays a role in several biological processes. Chemical modifications to DNA and histones have appeared as critical mediators of epigenetic memory and much attention has gone into characterizing their dynamics. The network of positive feedback loops that these modifications form generates a rich set of dynamics that both recapitulate the traditional binary memory paradigm and also predict a new form of memory that we call analog memory. In this paper, we review models of chromatin modifications and describe how binary or analog memory hinge on the presence or lack of positive feedback loops between repressive histone modifications and DNA methylation. Future research using advanced genetic engineering tools will be able to validate the molecular interactions that dictate different forms of memory, and will thus deepen our understanding of how epigenetic memories form in different biological contexts.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4019-4025"},"PeriodicalIF":4.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184815","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}