Computational and structural biotechnology journal最新文献

{"title":"Tailoring hydrophobicity and strength in spider silk-inspired coatings via thermal treatments","authors":"","doi":"10.1016/j.csbj.2024.09.009","DOIUrl":"10.1016/j.csbj.2024.09.009","url":null,"abstract":"<div><p>The advent of advanced coatings has transformed material functionalities, extending their roles from basic coverage and visual appeal to include unique properties such as self-healing, superior hydrophobicity, and antimicrobial action. However, the traditional dependency on petrochemical-derived materials for these coatings raises environmental concerns. This study proposes the use of renewable and alternative materials for coating development. We present the use of bioengineered spider silk-inspired protein (SSIP), produced through recombinant technology, as a viable, eco-friendly alternative due to their ease of processing under ambient pressure and the utilization of water as a solvent, alongside their exceptional physicochemical properties. Our research investigates the effects of different thermal treatments and protein concentrations on the mechanical strength and surface water repellency of coatings on silica bases. Our findings reveal a direct correlation between the temperature of heat treatment and the enhancements in surface hydrophobicity and mechanical strength, where elevated temperatures facilitate increased resistance to water and improved mechanical integrity. Consequently, we advocate SSIPs present a promising, sustainable choice for advanced coatings, providing a pathway to fine-tune coating recipes for better mechanical and hydrophobic properties with a reduced ecological footprint, finding potential uses in various fields such as electronics.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002976/pdfft?md5=e578139e728a4a4cd27b5e029c98b8b7&pid=1-s2.0-S2001037024002976-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241535","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":"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":"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":"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}

{"title":"Generation and characterization of two acid-resistant macrocin O-methyltransferase variants with a higher enzyme activity at 30 °C from Streptomyces fradiae","authors":"Chaoyue Yan, Yujun Tao, Jingyan Fan, Jun Dai, Shuo Li, Qi Huang, Rui Zhou","doi":"10.1016/j.csbj.2024.08.020","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.020","url":null,"abstract":"Tylosin is an important macrolide antibiotic produced by . In the biosynthesis of tylosin, macrocin -methyltransferase TylF catalyzes the conversion of the side-product tylosin C (macrocin) to the primary component tylosin A (C/A conversion). This conversion is the rate-limiting step in the biosynthesis of tylosin, and affects the quality of the end product. To find a high activity and environment-adapted TylF enzyme, a TylF variant pool has been constructed protein evolution approach in our previous study (Fan et al., 2023 [41]). In this study, the TylF variants with higher C/A conversion rates were expressed in and purified. The variants TylF, TylF and TylF were shown to have a higher C/A conversion rate at 30 °C than that of TylF at 38 °C. Moreover, they had a greater acid resistance and showed more adaptable to the pH change during fermentation. Further protein structural and substrate-binding affinity analyses revealed that the T36S, V54A, Q138H, Y139F, and F232Y mutations enlarged the volume of the substrate-binding pocket, thereby increasing the affinity of enzyme variants for their substrates of SAM and macrocin, and decreasing the inhibition of SAH. Three of the TylF variants were overexpressed in the industrial tylosin-producing strain, and the recombinant strains showed the highest C/A conversion at 30 °C without heating up to 38 °C during the last 24 h of fermentation. This is of great energy-saving significance for tylosin industrial production.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185501","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":"RCDdb: A manually curated database and analysis platform for regulated cell death","authors":"Xiaopeng Wang, Qing Wang, Jun Zhao, Jiaxin Chen, Ruo Wu, Juanjuan Pan, Jiaxin Li, Zechang Wang, Yongchang Chen, Wenting Guo, Yuanyuan Li","doi":"10.1016/j.csbj.2024.08.012","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.012","url":null,"abstract":"Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185503","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":"NJGCG: A node-based joint Gaussian copula graphical model for gene networks inference across multiple states","authors":"Yun Huang, Sen Huang, Xiao-Fei Zhang, Le Ou-Yang, Chen Liu","doi":"10.1016/j.csbj.2024.08.010","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.010","url":null,"abstract":"Inferring the interactions between genes is essential for understanding the mechanisms underlying biological processes. Gene networks will change along with the change of environment and state. The accumulation of gene expression data from multiple states makes it possible to estimate the gene networks in various states based on computational methods. However, most existing gene network inference methods focus on estimating a gene network from a single state, ignoring the similarities between networks in different but related states. Moreover, in addition to individual edges, similarities and differences between different networks may also be driven by hub genes. But existing network inference methods rarely consider hub genes, which affects the accuracy of network estimation. In this paper, we propose a novel node-based joint Gaussian copula graphical (NJGCG) model to infer multiple gene networks from gene expression data containing heterogeneous samples jointly. Our model can handle various gene expression data with missing values. Furthermore, a tree-structured group lasso penalty is designed to identify the common and specific hub genes in different gene networks. Simulation studies show that our proposed method outperforms other compared methods in all cases. We also apply NJGCG to infer the gene networks for different stages of differentiation in mouse embryonic stem cells and different subtypes of breast cancer, and explore changes in gene networks across different stages of differentiation or different subtypes of breast cancer. The common and specific hub genes in the estimated gene networks are closely related to stem cell differentiation processes and heterogeneity within breast cancers.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185504","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}