Computational and structural biotechnology journal最新文献

{"title":"Advancements in one-dimensional protein structure prediction using machine learning and deep learning.","authors":"Wafa Alanazi, Di Meng, Gianluca Pollastri","doi":"10.1016/j.csbj.2025.04.005","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.04.005","url":null,"abstract":"<p><p>The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionized this field, offering innovative approaches to tackle one- dimensional (1D) protein structure annotations, including secondary structure, solvent accessibility, and intrinsic disorder. This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. Key advancements, such as AlphaFold's transformative impact on structure prediction and the rise of protein language models (PLMs), have enabled unprecedented accuracy in capturing sequence-structure relationships. Furthermore, we explore the role of specialized datasets, benchmarking competitions, and multimodal integration in shaping state-of-the-art prediction models. By addressing challenges in data quality, scalability, interpretability, and task-specific optimization, this review underscores the transformative impact of ML, DL, and PLMs on 1D protein prediction while providing insights into emerging trends and future directions in this rapidly evolving field.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1416-1430"},"PeriodicalIF":4.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12002955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987973","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":"Next-Generation Sequencing: a powerful multi-purpose tool in cell line development for biologics production","authors":"Luigi Grassi,&nbsp;Claire Harris,&nbsp;Jie Zhu,&nbsp;Diane Hatton,&nbsp;Sarah Dunn","doi":"10.1016/j.csbj.2025.04.006","DOIUrl":"10.1016/j.csbj.2025.04.006","url":null,"abstract":"<div><div>Within the biopharmaceutical industry, the cell line development (CLD) process generates recombinant mammalian cell lines for the expression of therapeutic proteins. Analytical methods for the extensive characterisation of the protein product are well established; however, over recent years, next-generation sequencing (NGS) technologies have rapidly become an integral part of the CLD workflow. NGS can be used for different applications to characterise the genome, epigenome and transcriptome of cell lines. The resulting extensive datasets, especially when integrated with systems biology models, can give comprehensive insights that can be applied to optimize cell lines, media, and fermentation processes. NGS also provides comprehensive methods to monitor genetic variability during CLD. High coverage NGS experiments can indeed be used to ensure the integrity of plasmids, identify integration sites, and verify monoclonality of the cell lines. This review summarises the role of NGS in advancing biopharmaceutical production to ensure safety and efficacy of therapeutic proteins.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807784","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":"Fantastic genes and where to find them expressed in CHO","authors":"Markus Riedl,&nbsp;Caterina Ruggeri,&nbsp;Nicolas Marx,&nbsp;Nicole Borth","doi":"10.1016/j.csbj.2025.03.050","DOIUrl":"10.1016/j.csbj.2025.03.050","url":null,"abstract":"<div><div>The transcriptome of Chinese hamster ovary (CHO) cells plays a crucial role in determining cellular characteristics that are essential for biopharmaceutical applications. RNA-sequencing has been extensively used to profile gene expression patterns, aiming to gain a better understanding of intracellular behavior and mechanisms. Individual datasets, however, do not provide a comprehensive overview and characterisation of the CHO cell's transcriptome, such that the fundamental structure of the transcriptome remains unknown. Using 15 RNA-sequencing datasets, encompassing almost 300 samples of various experimental setups, conditions and cell lines, we explore and classify the protein-coding transcriptome of CHO cells. Differences in cell line lineages are found to be the primary source of variation in transcribed genes. By employing a novel approach, we identified the core transcriptome that is ubiquitously expressed in all cell lines and culture conditions, as well as genes that remain entirely non-expressed. Additionally, we identified a set of genes that may be active or inactive depending on different conditions, which are linked to biological processes including translation as well as immune and stress response. Lastly, by integrating chromatin states derived from histone modifications, we provided additional context on the epigenetic level that supports our protein-coding gene classification. Our study offers a comprehensive insight into the CHO cell transcriptome and lays the foundation for future research into cellular adaptation to changing conditions and the development of phenotypes.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776246","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":"ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.","authors":"Lin Zhu, Yi Fang, Shuting Liu, Hong-Bin Shen, Wesley De Neve, Xiaoyong Pan","doi":"10.1016/j.csbj.2025.04.002","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.04.002","url":null,"abstract":"<p><strong>Motivation: </strong>Assessing the potential toxicity of proteins is crucial for both therapeutic and agricultural applications. Traditional experimental methods for protein toxicity evaluation are time-consuming, expensive, and labor-intensive, highlighting the requirement for efficient computational approaches. Recent advancements in language models and deep learning have significantly improved protein toxicity prediction, yet current models often lack the ability to integrate evolutionary and structural information, which is crucial for accurate toxicity assessment of proteins.</p><p><strong>Results: </strong>In this study, we present ToxDL 2.0, a novel multimodal deep learning model for protein toxicity prediction that integrates both evolutionary and structural information derived from a pretrained language model and AlphaFold2. ToxDL 2.0 consists of three key modules: (1) a Graph Convolutional Network (GCN) module for generating protein graph embeddings based on AlphaFold2-predicted structures, (2) a domain embedding module for capturing protein domain representations, and (3) a dense module that combines these embeddings to predict the toxicity. After constructing a comprehensive toxicity benchmark dataset, we obtained experimental results on both an original non-redundant test set (comprising pre-2022 protein sequences) and an independent non-redundant test set (a holdout set of post-2022 protein sequences), demonstrating that ToxDL 2.0 outperforms existing state-of-the-art methods. Additionally, we utilized Integrated Gradients to discover known toxic motifs associated with protein toxicity. A web server for ToxDL 2.0 is publicly available at www.csbio.sjtu.edu.cn/bioinf/ToxDL2/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1538-1549"},"PeriodicalIF":4.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986917","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":"NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil.","authors":"Gaia Meoni, Leonardo Tenori, Francesca Di Cesare, Stefano Brizzolara, Pietro Tonutti, Chiara Cherubini, Laura Mazzanti, Claudio Luchinat","doi":"10.1016/j.csbj.2025.03.045","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.045","url":null,"abstract":"<p><p>This study investigates the potential of ¹H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with ¹H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging ¹H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1359-1369"},"PeriodicalIF":4.4,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988862","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":"Targeting the inter-monomeric space of TNFR1 pre-ligand dimers: A novel binding pocket for allosteric modulators.","authors":"Chih Hung Lo","doi":"10.1016/j.csbj.2025.03.046","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.046","url":null,"abstract":"<p><p>Tumor necrosis factor (TNF) receptor 1 (TNFR1) plays a central role in signal transduction mediating inflammation and cell death associated with autoimmune and neurodegenerative disorders. Inhibition of TNFR1 signaling is a highly sought-after strategy to target these diseases. TNFR1 forms pre-ligand dimers held together by the pre-ligand assembly domain (PLAD), which is essential for receptor signaling. TNFR1 dimers form the crucial points of interaction for the entire receptor signaling complex by connecting TNF ligand bound trimeric receptors. While previous studies have shown the feasibility of disrupting TNFR1 dimeric interactions through competitive mechanism that targets the PLAD, our recent studies have demonstrated that small molecules could also bind PLAD to modulate TNFR1 signaling through an allosteric mechanism. Importantly, these allosteric modulators alter receptor dynamics and propagate long-range conformational perturbation that involves reshuffling of the receptors in the cytosolic domains without disrupting receptor-receptor or receptor-ligand interactions. In this study, we perform molecular docking of previously reported allosteric modulators on the extracellular domain of TNFR1 to understand their binding sites and interacting residues. We identify the inter-monomeric space between TNFR1 pre-ligand dimers as a novel binding pocket for allosteric modulators. We further conduct pharmacological analyses to understand the bioactivity of these compounds and their interacting residues and pharmacological properties. We then provide insights into the structure-activity relationship of these allosteric modulators and the feasibility of targeting TNFR1 conformational dynamics. This paves the way for developing new therapeutic strategies and designing chemical scaffolds to target TNFR1 signaling.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1335-1341"},"PeriodicalIF":4.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983308","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":"Fine-tuning of conditional Transformers improves <i>in silico</i> enzyme prediction and generation.","authors":"Marco Nicolini, Emanuele Saitto, Ruben Emilio Jimenez Franco, Emanuele Cavalleri, Aldo Javier Galeano Alfonso, Dario Malchiodi, Alberto Paccanaro, Peter N Robinson, Elena Casiraghi, Giorgio Valentini","doi":"10.1016/j.csbj.2025.03.037","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.037","url":null,"abstract":"<p><p>We introduce <i>Finenzyme</i>, a Protein Language Model (PLM) that employs a multifaceted learning strategy based on transfer learning from a decoder-based Transformer, conditional learning using specific functional keywords, and fine-tuning for the <i>in silico</i> modeling of enzymes. Our experiments show that <i>Finenzyme</i> significantly enhances generalist PLMs like ProGen for the <i>in silico</i> prediction and generation of enzymes belonging to specific Enzyme Commission (EC) categories. Our <i>in silico</i> experiments demonstrate that <i>Finenzyme</i> generated sequences can diverge from natural ones, while retaining similar predicted tertiary structure, predicted functions and the active sites of their natural counterparts. We show that embedded representations of the generated sequences obtained from the embeddings computed by both <i>Finenzyme</i> and ESMFold closely resemble those of natural ones, thus making them suitable for downstream tasks, including e.g. EC classification. Clustering analysis based on the primary and predicted tertiary structure of sequences reveals that the generated enzymes form clusters that largely overlap with those of natural enzymes. These overall <i>in silico</i> validation experiments indicate that <i>Finenzyme</i> effectively captures the structural and functional properties of target enzymes, and can in perspective support targeted enzyme engineering tasks.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1318-1334"},"PeriodicalIF":4.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984475","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":"Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.","authors":"Ella Barkan, Ibrahim Siddiqui, Kevin J Cheng, Alex Golts, Yoel Shoshan, Jeffrey K Weber, Yailin Campos Mota, Michal Ozery-Flato, Giuseppe A Sautto","doi":"10.1016/j.csbj.2025.03.038","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.038","url":null,"abstract":"<p><p>Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved treatments for autoimmune, infectious, and cancer diseases. However, their discovery and development remains a time-consuming and costly process. Recent advancements in machine learning (ML) and artificial intelligence (AI) have shown significant promise in revolutionizing antibody discovery field. Models that predict antibody biological activity enable in silico evaluation of binding and functional properties; such models can prioritize antibodies with the highest likelihood of success in laboratory testing procedures. We explore an AI model for predicting the binding and receptor blocking activity of antibodies against influenza A hemagglutinin (HA) antigens. Our model is developed with the Molecular Aligned Multi-Modal Architecture and Language (MAMMAL) framework for biologics discovery to predict antibody-antigen interactions using only sequence information. To evaluate the model's performance, we tested it under various data split conditions to mimic real-world scenarios. Our model achieved an area under the receiver operating characteristic (AUROC) score of ≥ 0.91 for predicting the activity of existing antibodies against seen HAs and an AUROC score of 0.9 for unseen HAs. For novel antibody activity prediction, the AUROC was 0.73, which further declined to 0.63-0.66 under stringent constraints on similarity to existing antibodies. These results demonstrate the potential of AI foundation models to transform antibody design by reducing dependence on extensive laboratory testing and enabling more efficient prioritization of antibody candidates. Moreover, our findings emphasize the critical importance of diverse and comprehensive antibody datasets to improve the generalization of prediction models, particularly for novel antibody development.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1286-1295"},"PeriodicalIF":4.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976603","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":"Bile-Liver phenotype: Exploring the microbiota landscape in bile and intratumor of cholangiocarcinoma.","authors":"Lei Wang, Hui Zhao, Fan Wu, Jiale Chen, Hanjie Xu, Wanwan Gong, Sijia Wen, Mengmeng Yang, Jiazeng Xia, Yu Chen, Daozhen Chen","doi":"10.1016/j.csbj.2025.03.030","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.030","url":null,"abstract":"<p><p>Cholangiocarcinoma (CCA) arises within the peritumoral bile microenvironment, yet microbial translocation from bile to intracholangiocarcinoma (IntraCCA) tissues remains poorly understood. Previous studies on bile microbiota alterations from biliary benign disease (BBD) to CCA have yielded inconsistent results, highlighting the need for cross-study analysis. We presented a comprehensive analysis of five cohorts (N = 266), including our newly established 16S rRNA gene profiling (n = 42), to elucidate these microbiota transitions. The concordance of bacteria between CCA bile and intraCCA tissue, represented by Enterococcus and Staphylococcus, suggested microbiota migration from bile to intratumoral tissues. A computational random forest machine learning model effectively distinguished intraCCA tissue from CCA bile, identifying Rhodococcus and Ralstonia as diagnostically significant. The model also excelled in differentiating CCA bile from BBD bile, achieving an AUC value of 0.931 in external validation. Using unsupervised hierarchical clustering, we established Biletypes based on microbial signatures in our cohort. A combination of 17 genera effectively stratified patients into Biletype A and Biletype B. Biletype B robustly discerned CCA from BBD, with Sub-Biletype B1 correlating with advanced TNM stage and poorer prognosis. Among the 17 genera, bacterial Cluster 1, composed of Sphingomonas, Staphylococcus, Massilia, Paenibacillus, Porphyrobacter, Lawsonella, and Aerococcus, was enriched in Biletype B1 and predicted CCA with an AUC of 0.96. Staphylococcus emerged as a promising single-genus predictor for CCA diagnosis and staging. In conclusion, this study delineates a potential microbiota transition pathway from the gut through CCA bile to intra-CCA tissue, proposing Biletypes and Staphylococcus as biomarkers for CCA prognosis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1173-1186"},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11981758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972548","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 cellulose-degrading <i>Bacillus altitudinis</i> from Tibetan pigs improved the <i>in vitro</i> fermentation characteristics of wheat bran.","authors":"Junhong Wang, Teng Ma, Yining Xie, Kai Li, Chengzeng Luo, Chunran Teng, Bao Yi, Liang Chen, Hongfu Zhang","doi":"10.1016/j.csbj.2025.03.025","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.025","url":null,"abstract":"<p><p>Wheat bran, a significant cereal by-product, is widely used in animal husbandry and the food industry. Our previous study identified a correlation between fiber utilization in Tibetan pigs and their efficient hindgut fermentation capacity. In this study, the <i>Bacillus altitudinis</i> strain Z-99, capable of cellulose degradation, was identified and isolated from Tibetan pigs. The results of solid-state fermentation demonstrated a significant reduction in neutral detergent fiber (NDF), acid detergent fiber (ADF), and hemicellulose content by 12.26 % (<i>P</i> < 0.01), 1.58 % (<i>P</i> < 0.05), and 10.68 % (<i>P</i> < 0.01), respectively, while crude protein content increased by 1.90 % (<i>P</i> < 0.01). Compared to the control group, supplementation with <i>Bacillus altitudinis</i> strain Z-99 enhanced the <i>in vitro</i> fermentation characteristics of wheat bran. In particular, it significantly increased the theoretical maximum gas production (<i>P</i> < 0.05) and elevated the content of acetic acid (<i>P</i> < 0.05), butyric acid (<i>P</i> < 0.05), isobutyric acid (<i>P</i> < 0.01), valeric acid (<i>P</i> < 0.05), and total short-chain fatty acids (<i>P</i> < 0.01). Furthermore, the addition of <i>Bacillus altitudinis</i> strain Z-99 significantly increased the abundance of <i>Bifidobacterium</i> (<i>P</i> < 0.05) and <i>Blautia</i> (<i>P</i> < 0.05), while decreasing the abundance of disease-associated <i>Enterococcus</i> (<i>P</i> < 0.01) and <i>Actinobacillus</i> (<i>P</i> < 0.05). Overall, a <i>Bacillus altitudinis</i> strain Z-99 with cellulose-degrading capacity was isolated from Tibetan pigs, and its functionality was validated through solid-state fermentation and <i>in vitro</i> fermentation methods. This study provided valuable insights into the utilization of wheat bran and the exploration of cellulose-degrading bacteria.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1233-1243"},"PeriodicalIF":4.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11984536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969205","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}