Computational and structural biotechnology journal最新文献

{"title":"Identification and biophysical characterization of epitope atlas of Porcine Reproductive and Respiratory Syndrome Virus","authors":"Supantha Dey, Jennifer Bruner, Maria Brown, Mike Roof, Ratul Chowdhury","doi":"10.1016/j.csbj.2024.08.029","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.029","url":null,"abstract":"Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) have been a critical threat to swine health since 1987 due to its high mutation rate and substantial economic loss over half a billion dollar in USA. The rapid mutation rate of PRRSV presents a significant challenge in developing an effective vaccine. Even though surveillance and intervention studies have recently (2019) unveiled utilization of PRRSV glycoprotein 5 (GP5; encoded by gene) to induce immunogenic reaction and production of neutralizing antibodies in porcine populations, the future viral generations can accrue escape mutations. In this study we identify 63 porcine-PRRSV protein-protein interactions which play primary or ancillary roles in viral entry and infection. Using genome–proteome annotation, protein structure prediction, multiple docking experiments, and binding energy calculations, we identified a list of 75 epitope locations on PRRSV proteins crucial for infection. Additionally, using machine learning-based diffusion model, we designed 56 stable immunogen peptides that contain one or more of these epitopes with their native tertiary structures stabilized through optimized N– and C–terminus flank sequences and interspersed with appropriate linker regions. Our workflow successfully identified numerous known interactions and predicted several novel PRRSV-porcine interactions. By leveraging the structural and sequence insights, this study paves the way for more effective, high-avidity, multi-valent PRRSV vaccines, and leveraging neural networks for immunogen design.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG","authors":"Bingjie Zhu, Zhenhao Li, Zehua Jin, Yi Zhong, Tianhang Lv, Zhiwei Ge, Haoran Li, Tianhao Wang, Yugang Lin, Huihui Liu, Tianyi Ma, Shufang Wang, Jie Liao, Xiaohui Fan","doi":"10.1016/j.csbj.2024.09.001","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.09.001","url":null,"abstract":"Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is a potent analytical technique utilized for identifying natural products from complex sources. However, due to the structural diversity, annotating LC-MS/MS data of natural products efficiently remains challenging, hindering the discovery process of novel active structures. Here, we introduce MassKG, an algorithm that combines a knowledge-based fragmentation strategy and a deep learning-based molecule generation model to aid in rapid dereplication and the discovery of novel NP structures. Specifically, MassKG has compiled 407,720 known NP structures and, based on this, generated 266,353 new structures using chemical language models for the discovery of potential novel compounds. Furthermore, MassKG demonstrates exceptional performance in spectra annotation compared to state-of-the-art algorithms. To enhance usability, MassKG has been implemented as a web server for annotating tandem mass spectral data (MS/MS, MS2) with a user-friendly interface, automatic reporting, and fragment tree visualization. Lastly, the interpretive capability of MassKG is comprehensively validated through composition analysis and MS annotation of , , and . MassKG is now accessible at .","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics","authors":"Duo Xi, Dingnan Cui, Mingjianan Zhang, Jin Zhang, Muheng Shang, Lei Guo, Junwei Han, Lei Du","doi":"10.1016/j.csbj.2024.08.027","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.027","url":null,"abstract":"Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VICTOR: Validation and inspection of cell type annotation through optimal regression","authors":"Chia-Jung Chang, Chih-Yuan Hsu, Qi Liu, Yu Shyr","doi":"10.1016/j.csbj.2024.08.028","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.028","url":null,"abstract":"Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the cellular and molecular landscapes of Wnt/β-catenin signaling in mouse embryonic kidney development","authors":"Hui Zhao, Hui Gong, Peide Zhu, Chang Sun, Wuping Sun, Yujin Zhou, Xiaoxiao Wu, Ailin Qiu, Xiaosha Wen, Jinde Zhang, Dixian Luo, Quan Liu, Yifan Li","doi":"10.1016/j.csbj.2024.08.025","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.025","url":null,"abstract":"The Wnt/β-catenin signaling pathway is critical in kidney development, yet its specific effects on gene expression in different embryonic kidney cell types are not fully understood. Wnt/β-catenin signaling was activated in mouse E12.5 kidneys using CHIR99021, with RNA sequencing performed afterward, and the results were compared to DMSO controls (dataset GSE131240). Differential gene expression in ureteric buds and cap mesenchyme following pathway activation (datasets GSE20325 and GSE39583) was analyzed. Single-cell RNA-seq data from the Mouse Cell Atlas was used to link differentially expressed genes (DEGs) with kidney cell types. β-catenin ChIP-seq data (GSE39837) identified direct transcriptional targets. Activation of Wnt/β-catenin signaling led to 917 significant DEGs, including the upregulation of and and the downregulation of and . These DEGs were involved in kidney development and immune response. Single-cell analysis identified 787 DEGs across nineteen cell subtypes, with Macrophage_Apoe high cells showing the most pronounced enrichment of Wnt/β-catenin-activated genes. Gene expression profiles in ureteric buds and cap mesenchyme differed significantly upon β-catenin manipulation, with cap mesenchyme showing a unique set of DEGs. Analysis of β-catenin ChIP-seq data revealed 221 potential direct targets, including and . This study maps the complex gene expression driven by Wnt/β-catenin signaling in embryonic kidney cell types. The identified DEGs and β-catenin targets elucidate the molecular details of kidney development and the pathway's role in immune processes, providing a foundation for further research into Wnt/β-catenin signaling in kidney development and disease.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-preserving decentralized learning methods for biomedical applications","authors":"Mohammad Tajabadi, Roman Martin, Dominik Heider","doi":"10.1016/j.csbj.2024.08.024","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.024","url":null,"abstract":"In recent years, decentralized machine learning has emerged as a significant advancement in biomedical applications, offering robust solutions for data privacy, security, and collaboration across diverse healthcare environments. In this review, we examine various decentralized learning methodologies, including federated learning, split learning, swarm learning, gossip learning, edge learning, and some of their applications in the biomedical field. We delve into the underlying principles, network topologies, and communication strategies of each approach, highlighting their advantages and limitations. Ultimately, the selection of a suitable method should be based on specific needs, infrastructures, and computational capabilities.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic control of soft microrobots near step-out frequency: Characterization and analysis","authors":"","doi":"10.1016/j.csbj.2024.08.022","DOIUrl":"10.1016/j.csbj.2024.08.022","url":null,"abstract":"<div><p>Magnetically actuated soft microrobots hold promise for biomedical applications that necessitate precise control and adaptability in complex environments. These microrobots can be accurately steered below their step-out frequencies where they exhibit synchronized motion with external magnetic fields. However, the step-out frequencies of soft microrobots have not been investigated yet, as opposed to their rigid counterparts. In this work, we develop an analytic model from the magneto-elastohydrodynamics to establish the relationship between the step-out frequency of soft sperm-like microrobots and their magnetic properties, geometry, wave patterns, and the viscosity of the surrounding medium. We fabricate soft sperm-like microrobots using electrospinning and assess their swimming abilities in mediums with varying viscosities under an oscillating magnetic field. We observe slight variations in wave patterns of the sperm-like microrobots as the actuation frequency changes. Our theoretical model, which analyzes these wave patterns observed without exceeding the step-out threshold, quantitatively agrees with the experimentally measured step-out frequencies. By accurately predicting the step-out frequency, the proposed model lays a foundation for achieving precise control over individual soft microrobots and enabling selective control within a swarm when executing biomedical tasks.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002812/pdfft?md5=c76ce6c9daa7e1e10e5d1c11db2a3893&pid=1-s2.0-S2001037024002812-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128821","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":"UQCRB and LBH are correlated with Gleason score progression in prostate cancer: Spatial transcriptomics and experimental validation","authors":"Yongjun Quan, Hong Zhang, Mingdong Wang, Hao Ping","doi":"10.1016/j.csbj.2024.08.026","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.026","url":null,"abstract":"Prostate cancer (PCa) is a multifocal disease characterized by genomic and phenotypic heterogeneity within a single gland. In this study, Visium spatial transcriptomics (ST) analysis was applied to PCa tissues with different histological structures to infer the molecular events involved in Gleason score (GS) progression. The spots in tissue sections were classified into various groups using Principal Component Analysis (PCA) and Louvain clustering analysis based on transcriptome data. Anotation of the spots according to GS revealed notable similarities between transcriptomic profiles and histologically identifiable structures. The accuracy of macroscopic GS determination was bioinformatically verified through malignancy-related feature analysis, specifically inferred copy number variation (inferCNV), as well as developmental trajectory analyses, such as diffusion pseudotime (DPT) and partition-based graph abstraction (PAGA). Genes related to GS progression were identified from the differentially expressed genes (DEGs) through pairwise comparisons of groups along a GS gradient. The proteins encoded by the representative oncogenes UQCRB and LBH were found to be highly expressed in advanced-stage PCa tissues. Knockdown of their mRNAs significantly suppressed PCa cell proliferation and invasion. These findings were validated using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset, as well as through histological and cytological experiments. The results presented here establish a foundation for ST-based evaluation of GS progression and provide valuable insights into the GS progression-related genes UQCRB and LBH.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mass spectrometry-complemented molecular modeling predicts the interaction interface for a camelid single-domain antibody targeting the Plasmodium falciparum circumsporozoite protein’s C-terminal domain","authors":"Kwabena F.M. Opuni, Manuela Ruß, Rob Geens, Line De Vocht, Pieter Van Wielendaele, Christophe Debuy, Yann G.-J. Sterckx, Michael O. Glocker","doi":"10.1016/j.csbj.2024.08.023","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.023","url":null,"abstract":"Bioanalytical methods that enable rapid and high-detail characterization of binding specificities and strengths of protein complexes with low sample consumption are highly desired. The interaction between a camelid single domain antibody (sdAbCSP1) and its target antigen (PfCSP-Cext) was selected as a model system to provide proof-of-principle for the here described methodology. The structure of the sdAbCSP1 – PfCSP-Cext complex was modeled using AlphaFold2. The recombinantly expressed proteins, sdAbCSP1, PfCSP-Cext, and the sdAbCSP1 – PfCSP-Cext complex, were subjected to limited proteolysis and mass spectrometric peptide analysis. ITEM MS (Intact Transition Epitope Mapping Mass Spectrometry) and ITC (Isothermal Titration Calorimetry) were applied to determine stoichiometry and binding strength. The paratope of sdAbCSP1 mainly consists of its CDR3 (aa100–118). PfCSP-Cext’s epitope is assembled from its α-helix (aa40–52) and opposing loop (aa83–90). PfCSP-Cext’s GluC cleavage sites E46 and E58 were shielded by complex formation, confirming the predicted epitope. Likewise, sdAbCSP1’s tryptic cleavage sites R105 and R108 were shielded by complex formation, confirming the predicted paratope. ITEM MS determined the 1:1 stoichiometry and the high complex binding strength, exemplified by the gas phase dissociation reaction enthalpy of 50.2 kJ/mol. The complex dissociation constant is 5 × 10 M. Combining AlphaFold2 modeling with mass spectrometry/limited proteolysis generated a trustworthy model for the sdAbCSP1 – PfCSP-Cext complex interaction interface.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive machine-learning model for clinical decision-making in washed microbiota transplantation on ulcerative colitis","authors":"","doi":"10.1016/j.csbj.2024.08.021","DOIUrl":"10.1016/j.csbj.2024.08.021","url":null,"abstract":"<div><h3>Background and Aim</h3><p>Machine learning based on clinical data and treatment protocols for better clinical decision-making is a current research hotspot. This study aimed to build a machine learning model on washed microbiota transplantation (WMT) for ulcerative colitis (UC), providing patients and clinicians with a new evaluation system to optimize clinical decision-making.</p><p><strong>Methods</strong></p><p>Patients with UC who underwent WMT via mid-gut or colonic delivery route at an affiliated hospital of Nanjing Medical University from April 2013 to June 2022 were recruited. Model ensembles based on the clinical indicators were constructed by machine-learning to predict the clinical response of WMT after one month.</p><p><strong>Results</strong></p><p>A total of 366 patients were enrolled in this study, with 210 patients allocated for training and internal validation, and 156 patients for external validation. The low level of indirect bilirubin, activated antithrombin III, defecation frequency and cholinesterase and the elderly and high level of creatine kinase, HCO₃^- and thrombin time were related to the clinical response of WMT at one month. Besides, the voting ensembles exhibited an area under curve (AUC) of 0.769 ± 0.019 [accuracy, 0.754; F1-score, 0.845] in the internal validation; the AUC of the external validation was 0.614 ± 0.017 [accuracy, 0.801; F1-score, 0.887]. Additionally, the model was available at <span><span>https://wmtpredict.streamlit.app</span><svg><path></path></svg></span>.</p><p><strong>Conclusions</strong></p><p>This study pioneered the development of a machine learning model to predict the one-month clinical response of WMT on UC. The findings demonstrate the potential value of machine learning applications in the field of WMT, opening new avenues for personalized treatment strategies in gastrointestinal disorders.</p><p><strong>Trial registration</strong></p><p>clinical trials, NCT01790061. Registered 09 February 2013 - Retrospectively registered, <span><span>https://clinicaltrials.gov/study/NCT01790061</span><svg><path></path></svg></span></p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002800/pdfft?md5=507581078570f5931730f762bd1204f8&pid=1-s2.0-S2001037024002800-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084453","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}