Computational and structural biotechnology journal最新文献

{"title":"Learning the language of plant immunity: opportunities and challenges for AI-assisted modelling of fungal effector x host protein complexes.","authors":"C Verdonk, K K Gagalova, S Raffaele, M C Derbyshire","doi":"10.1016/j.csbj.2025.06.048","DOIUrl":"10.1016/j.csbj.2025.06.048","url":null,"abstract":"<p><p>Phytopathogenic fungi cause substantial crop losses worldwide. They secrete proteins called effectors, which enable infection through interactions with diverse host proteins. These interactions are fundamentally important to plant disease and its practical control. New artificial intelligence (AI) techniques can predict many individual protein structures to near experimental accuracy. Although these techniques also predict protein complexes, they are not as accurate as single-protein models. Use of AI to study interactions between fungal pathogen effectors and plant proteins is currently limited. However, despite some challenges, early adoption of AI has highlighted its potential. General improvements in AI-assisted protein complex modelling may create more opportunities in future. This review focuses on recent research using AI to study the interactions between fungal effectors and plant proteins, outlining challenges and emerging opportunities.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2881-2889"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674055","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":"Bacteriophage-based strategies for biocontrol and treatment of infectious diseases.","authors":"Trinh Thi Trang Nhung, Swati Verma, Saravanaraman Ponne, Gautam Kumar Meghwanshi, Thomas Schön, Rajender Kumar","doi":"10.1016/j.csbj.2025.06.046","DOIUrl":"10.1016/j.csbj.2025.06.046","url":null,"abstract":"<p><p>Bacteriophages are viruses that infect bacteria, which are essential for controlling bacterial diversity. Among the novel aspects, phage display-based strategies are used for epitope mapping and the development of immunotherapy. A recent classification system has been developed based on the recent sequencing methods and bioinformatic tools. The unique specificity of phages is of increasing use in biocontrol, where bacteriophages are applied to target and reduce harmful bacterial populations in agriculture, food preservation and safety, offering a sustainable alternative to chemical exposure and a plausible solution to excessive misuse of antibiotics. Phage therapy has emerged as a complement to antibiotics for difficult-to-treat infectious diseases such as multi-drug resistant bacteria where other alternatives are lacking. The ability of bacteriophages to specifically target pathogenic bacteria while sparing the normal flora makes them attractive treatment options. Among the challenges are the slow uptake of phage therapy in the clinical setting, a lack of standardisation and regulatory issues. Nevertheless, phage-based strategies are likely to become a future cornerstone for biocontrol and treatment of infectious diseases.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2924-2932"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658627","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>dandelionR</i>: Single-cell immune repertoire trajectory analysis in R.","authors":"Jiawei Yu, Xiaohan Xu, Nicholas Borcherding, Zewen Kelvin Tuong","doi":"10.1016/j.csbj.2025.06.047","DOIUrl":"10.1016/j.csbj.2025.06.047","url":null,"abstract":"<p><p>Integration of single-cell RNA-sequencing (scRNA-seq) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) is extremely powerful in studying lymphocyte development. A python-based package, <i>Dandelion</i>, introduced the VDJ-feature space method, which addresses the challenge of integrating single-cell AIR data with gene expression data and enhances trajectory analysis results. However, no R-based equivalent or similar methods currently exist. To fill this gap, we present <i>dandelionR</i>, an R implementation of <i>Dandelion</i>'s trajectory analysis workflow, bringing the VDJ feature space construction and trajectory analysis using diffusion maps and absorbing Markov chains to R, offering a new option for scRNA-seq and scVDJ-seq analysis to R users.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2890-2897"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674047","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 labeled dataset for AI-based cryo-EM map enhancement.","authors":"Nabin Giri, Xiao Chen, Liguo Wang, Jianlin Cheng","doi":"10.1016/j.csbj.2025.06.041","DOIUrl":"10.1016/j.csbj.2025.06.041","url":null,"abstract":"<p><p>Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate model building. While various methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps: regression maps capturing idealized density distributions, binary classification maps distinguishing structural elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label maps compared to experimental maps. This resource bridges the gap between structural biology and artificial intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing cryo-EM density maps.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2843-2850"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674048","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":"ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets.","authors":"Peizheng Mu, Xiangyan Feng, Lanxin Tong, Jie Huang, Chaoqun Zhu, Fei Wang, Wei Quan, Yuanjun Ma, Yucui Dong, Xiao Zhu","doi":"10.1016/j.csbj.2025.06.045","DOIUrl":"10.1016/j.csbj.2025.06.045","url":null,"abstract":"<p><p>Accurate benchmarking of structural variant (SV) detection is essential for advancing the development and application of human whole-genome sequencing (WGS). A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the variation-awareness and strategic implementation of aligners inherently constrain SV detection algorithms that rely on alignment-based approaches. Traditional benchmarking, which primarily focuses on comparing and matching individual variants, makes it difficult to capture the relationships between multiple adjacent variants. We introduced ASVBM, an improved benchmarking framework that introduces the notion of latent positives and leverages a joint analysis and validation strategy based on local variants. This performance improvement arose from the discovery that multiple smaller variants are nearly equivalent to a larger variant. We comprehensively evaluated the performance of six state-of-the-art variant calling pipelines using real WGS datasets. According to multiple matching criteria, ASVBM employs a joint analysis strategy to uncover potential equivalences between the callset and the benchmark set, thereby reducing false mismatches caused by differences in variant representation. ASVBM is available at https://github.com/zhuxiao/asvbm.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2851-2862"},"PeriodicalIF":4.4,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674050","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":"AI-based antibody design targeting recent H5N1 avian influenza strains.","authors":"Nicholas Santolla, Colby T Ford","doi":"10.1016/j.csbj.2025.06.026","DOIUrl":"10.1016/j.csbj.2025.06.026","url":null,"abstract":"<p><p>In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the <i>Frankies</i> framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv antibody fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2915-2923"},"PeriodicalIF":4.4,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658626","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> determination of novel SARS-CoV-2 envelope protein ion channel inhibitors.","authors":"Nina Kobe, Lennart Dreisewerd, Matic Pavlin, Polona Kogovšek, Črtomir Podlipnik, Uroš Grošelj, Miha Lukšič","doi":"10.1016/j.csbj.2025.06.036","DOIUrl":"10.1016/j.csbj.2025.06.036","url":null,"abstract":"<p><p>The SARS-CoV-2 envelope protein (2-E^PRO), a viroporin crucial for viral pathogenesis, is a promising target for antiviral drug development as it is highly conserved and functionally important. Although it is a promising therapeutic target for the treatment of COVID-19, it has often been overlooked in previous studies. In this study, a high-throughput virtual screening of nearly one billion compounds was performed, followed by rigorous filtering and re-docking. Eight best-scoring and chemically versatile lead candidates were identified. In molecular dynamics simulations, three of these ligands showed stable protein-ligand complexes occupying the 2-E^PRO channel pore. Among these, ZINC001799167680 (L3) and ZINC001081252239 (L2) exhibited the strongest binding affinity, with key interactions at residues ASN15, THR11 and GLU8 identified by Molecular Mechanics Poisson-Boltzmann Surface Area analysis. All ligands were compared with the known inhibitor rimantadine and showed stronger binding to the protein. These <i>in silico</i> results highlight the potential of focusing on the 2-E^PRO ion channel in the development of novel COVID-19 therapeutics and pave the way for further <i>in vitro</i> and <i>in vivo</i> studies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2823-2831"},"PeriodicalIF":4.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658625","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":"EpiMolBio: A novel user-friendly bioinformatic program for genetic variability analysis.","authors":"Roberto Reinosa, Paloma Troyano-Hernáez, Ana Valadés-Alcaraz, África Holguín","doi":"10.1016/j.csbj.2025.06.034","DOIUrl":"10.1016/j.csbj.2025.06.034","url":null,"abstract":"<p><strong>Purpose: </strong>Genetic sequence analysis has become essential in many fields of medicine, biology, and epidemiology. However, the currently available tools can pose a challenge for users without advanced computational skills.</p><p><strong>Results: </strong>We present EpiMolBio (https://www.epimolbio.com), a free-to-use software designed with an intuitive, user-friendly interface that enables a broad spectrum of users to explore genetic variability. Its diverse toolkit encompasses sequence processing, conservation and variability analysis, consensus sequence generation, and identification of genome mutation or amino acid changes, including specialized tools for HIV and SARS-CoV-2 analysis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2968-2975"},"PeriodicalIF":4.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674052","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":"Maternal <i>Clostridium butyricum</i> supplementation during late gestation and lactation enhances gut bacterial communities, milk quality, and reduces piglet diarrhea.","authors":"Morakot Nuntapaitoon, Piraya Chatthanathon, Matanee Palasuk, Alisa Wilantho, Jakavat Ruampatana, Sissades Tongsima, Sarn Settachaimongkon, Naraporn Somboonna","doi":"10.1016/j.csbj.2025.06.040","DOIUrl":"10.1016/j.csbj.2025.06.040","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Experimental objective: &lt;/strong&gt;Diarrhea is a major cause of piglet mortality, often reported associated with maternal gut bacterial communities (microbiota). Maternal supplementation with probiotic &lt;i&gt;Clostridium butyricum&lt;/i&gt; during late gestation showed to reduce piglet diarrhea during the suckling period. This study thereby investigated the effects of probiotic supplementation on sow gut (feces) microbiota and their potential microbial metabolisms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Sow and litter performances, including milk compositions and incidences of piglet diarrhea, were recorded from farrowing to weaning of control- supplemented vs. probiotic-supplemented sows. Fecal samples from sows classified as before (Cb=17) and after (Ca=17) probiotic supplementation were analyzed using 16S rRNA gene sequencing and 16S rRNA qPCR, following bioinformatic analyses for alpha-beta diversity, quantitative microbiota, LEfSe (Linear discriminant analysis Effect Size) taxon biomarker analysis, potential microbial metabolism profiles, and statistical correlations with microbial species and clinical data performances.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Probiotic-supplemented sows demonstrated the greater average piglets born alive and lower mummified fetuses (&lt;i&gt;P&lt;/i&gt; &gt; 0.05), and the statistical higher protein and casein contents in their colostrum (&lt;i&gt;P&lt;/i&gt; &lt; 0.05). Following microbiota analyses, no significant difference was observed in operational taxonomic units (OTUs), Chao1, and Shannon alpha-diversity indices between Cb and Ca samples. Nevertheless, Ca sows exhibited higher relative abundances of &lt;i&gt;Clostridium&lt;/i&gt;, SMB53, g_&lt;i&gt;Turicibacter&lt;/i&gt;, &lt;i&gt;Treponema&lt;/i&gt;, &lt;i&gt;Bacillus&lt;/i&gt;, &lt;i&gt;Enterococcus&lt;/i&gt; and &lt;i&gt;Lactobacillus&lt;/i&gt;, while the lower abundances of &lt;i&gt;Oscillospira&lt;/i&gt;, &lt;i&gt;Prevotella&lt;/i&gt;, &lt;i&gt;Phascolarctobacterium&lt;/i&gt; and &lt;i&gt;Ruminococcus&lt;/i&gt;, compared with Cb sows. This highlighted that after the probiotic supplementation showed the sow gut microbiota more abundances of potentially beneficial bacteria, including the supplemented probiotic &lt;i&gt;C. butyricum&lt;/i&gt;, g_&lt;i&gt;Bacillus&lt;/i&gt;, g_&lt;i&gt;Enterococcus&lt;/i&gt; and g_&lt;i&gt;Lactobacillus&lt;/i&gt;, for instances. The finding was consistent with the LEfSe (Linear discriminant analysis Effect Size) taxon biomarker analysis for the Ca group. Several microbial related metabolic pathways in sow feces were altered after probiotic supplement, particularly relevant to amino acid and short-chain fatty acid metabolisms (i.e&lt;i&gt;.,&lt;/i&gt; propanoate and butanoate), xenobiotics biodegradation and lipid metabolism. Supportively, the gut microbiota changes of Ca sows might associate with improved sow performance and milk metabolomic profile.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The maternal probiotic &lt;i&gt;C. butyricum&lt;/i&gt; supplementation during late gestation and lactation showed the improved sows' intestine, milk components, and the reduced piglet diarrhea cases. This helps to understand and support the probiotic supplementation ","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2933-2945"},"PeriodicalIF":4.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674057","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":"Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models.","authors":"Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo","doi":"10.1016/j.csbj.2025.06.037","DOIUrl":"10.1016/j.csbj.2025.06.037","url":null,"abstract":"<p><p>Kawasaki disease (KD) is a leading cause of acquired coronary vasculitis in children and remains a critical diagnostic challenge among febrile pediatric patients. To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. It also takes the lead in using age-calibrated eosinophil, platelet, and hemoglobin results. Trained using the light gradient boosting machine algorithm on clinical data from 1,927 KD cases and 45,274 febrile controls, KDpredictor achieved strong performance metrics (auROC: 95.7%, auPRC: 72.4%, recall: 0.89) on a reserved test set, outperforming previous models by at least 3% in auROC and 39.3% in auPRC. Additional explainable AI analyses revealed that several top predictive features in KDpredictor are consistent with prior clinical findings. We also evaluated KDpredictor on three independent cohorts collected in East Asia (Taiwan and China) during the COVID-19 period. KDpredictor achieves recall values of 90.9%, 83.7%, and 91.7% on KD samples identified in three independent medical centers, respectively, indicating its applicability across independent clinical settings. In summary, KDpredictor demonstrates robust generalizability in KD risk identification across populations by using only standard blood samples independent of clinical symptoms. KDpredictor is freely available at https://cosbi.ee.ncku.edu.tw/KD_under7/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2832-2842"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658629","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}