Computational and structural biotechnology journal最新文献

{"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}

{"title":"Going Digital to Boost Safe and Sustainable Materials Innovation Markets. The Digital Safe-and-Sustainability-by-Design Innovation Approach of the PINK Project","authors":"Thomas E. Exner ,&nbsp;Joh Dokler ,&nbsp;Steffi Friedrichs ,&nbsp;Christian Seitz ,&nbsp;Francesca L. Bleken ,&nbsp;Jesper Friis ,&nbsp;Thomas F. Hagelien ,&nbsp;Francesco Mercuri ,&nbsp;Anna L. Costa ,&nbsp;Irini Furxhi ,&nbsp;Haralambos Sarimveis ,&nbsp;Antreas Afantitis ,&nbsp;Antonino Marvuglia ,&nbsp;Gustavo M. Larrea-Gallegos ,&nbsp;Tommaso Serchi ,&nbsp;Angela Serra ,&nbsp;Dario Greco ,&nbsp;Penny Nymark ,&nbsp;Martin Himly ,&nbsp;Karin Wiench ,&nbsp;Roland Hischier","doi":"10.1016/j.csbj.2025.03.019","DOIUrl":"10.1016/j.csbj.2025.03.019","url":null,"abstract":"<div><div>In this innovation report, we present the vision of the PINK project to foster Safe-and-Sustainable-by-Design (SSbD) advanced materials and chemicals (AdMas&amp;Chems) development by integrating state-of-the-art computational modelling, simulation tools and data resources. PINK proposes a novel approach for the use of the SSbD framework, whose innovative approach is based on the application of a multi-objective optimisation procedure for the criteria of functionality, safety, sustainability and cost efficiency.</div><div>At the core is the PINK open innovation platform, a distributed system that integrates all relevant modelling resources enriched with advanced data visualisation and an AI-driven decision support system. Data and modelling tools from the, in large parts, independently developed areas of functional design, safety assessment, life cycle assessment &amp; costing are brought together based on a newly created Interoperability Framework. The PINK <em>In Silico</em> Hub, as the user Interface to the platform, finally guides the user through the complete AdMas&amp;Chems development process from idea creation to market introduction.</div><div>Guided by two Developmental Case Studies, the process of building of the PINK Platform is iterative, ensuring industry readiness to implement and apply it. Additionally, the Industrial Demonstrator programme will be introduced as part of the final project phase, which allows industry partners and especially small and medium enterprises (SMEs) to become part of the PINK consortium. Feedback from the Demonstrators as well as other stakeholder-engagement activities and collaborations will shape the platform’s final look and feel and, even more important, activities to assure long-term technical sustainability.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"29 ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767680","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":"Characterising functional redundancy in microbiome communities via relative entropy.","authors":"Daniel Fässler, Almut Heinken, Johannes Hertel","doi":"10.1016/j.csbj.2025.03.012","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.012","url":null,"abstract":"<p><p>Functional redundancy has been hypothesised to be at the core of the well-evidenced relation between high ecological microbiome diversity and human health. Here, we conceptualise and operationalise functional redundancy on a single-trait level for functionally annotated microbial communities, utilising an information-theoretic approach based on relative entropy that also allows for the quantification of functional interdependency across species. Via constraint-based microbiome community modelling of a public faecal metagenomic dataset, we demonstrate that the strength of the relation between species diversity and functional redundancy is dependent on specific attributes of the function under consideration such as the rarity and the occurring functional interdependencies. Moreover, by integrating faecal metabolome data, we highlight that measures of functional redundancy have correlates in the host's metabolome. We further demonstrate that microbiomes sampled from colorectal cancer patients display higher levels of species-species functional interdependencies than those of healthy controls. By analysing microbiome community models from an inflammatory bowel disease (IBD) study, we show that although species diversity decreased in IBD subjects, functional redundancy increased for certain metabolites, notably hydrogen sulphide. This finding highlights their potential to provide valuable insights beyond species diversity. Here, we formalise the concept of functional redundancy in microbial communities and demonstrate its usefulness in real microbiome data, providing a foundation for a deeper understanding of how microbiome diversity shapes the functional capacities of a microbiome.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1482-1497"},"PeriodicalIF":4.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12013412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062541","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":"Computational analysis of the gut microbiota-mediated drug metabolism.","authors":"Sammie Chum, Alberto Naveira Montalvo, Soha Hassoun","doi":"10.1016/j.csbj.2025.03.016","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.03.016","url":null,"abstract":"<p><p>The gut microbiota, an extensive ecosystem harboring trillions of bacteria, plays a pivotal role in human health and disease, influencing diverse conditions from obesity to cancer. Among the microbiota's myriad functions, the capacity to metabolize drugs remains relatively unexplored despite its potential implications for drug efficacy and toxicity. Experimental methods are resource-intensive, prompting the need for innovative computational approaches. We present a computational analysis, termed MDM, aimed at predicting gut microbiota-mediated drug metabolism. This computational analysis incorporates data from diverse sources, e.g., UHGG, MagMD, MASI, KEGG, and RetroRules. An existing tool, PROXIMAL2, is used iteratively over all drug candidates from experimental databases queried against biotransformation rules from RetroRules to predict potential drug metabolites along with the enzyme commission number responsible for that biotransformation. These potential metabolites are then categorized into gut MDM metabolites by cross referencing UHGG. The analysis' efficacy is validated by its coverage on each of the experimental databases in the gut microbial context, being able to recall up to 74 % of experimental data and producing a list of potential metabolites, of which an average of about 65 % are relevant to the gut microbial context. Moreover, explorations into ranking metabolites, iterative applications to account for multi-step metabolic pathways, and potential applications in experimental studies showcase its versatility and potential impact beyond raw predictions. Overall, this study presents a promising computational framework for further research and applications gut MDM, drug development and human health.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1472-1481"},"PeriodicalIF":4.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969215","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":"Computational modelling of bone growth and mineralization surrounding biodegradable Mg-based and permanent Ti implants","authors":"Nik Pohl ,&nbsp;Domenik Priebe ,&nbsp;Tamadur AlBaraghtheh ,&nbsp;Sven Schimek ,&nbsp;Florian Wieland ,&nbsp;Diana Krüger ,&nbsp;Sascha Trostorff ,&nbsp;Regine Willumeit-Römer ,&nbsp;Ralf Köhl ,&nbsp;Berit Zeller-Plumhoff","doi":"10.1016/j.csbj.2025.02.027","DOIUrl":"10.1016/j.csbj.2025.02.027","url":null,"abstract":"<div><div><em>In silico</em> testing of implant materials is a research area of high interest, as cost- and labour-intensive experiments may be omitted. However, assessing the tissue-material interaction mathematically and computationally can be very complex, in particular when functional, such as biodegradable, implant materials are investigated. In this work, we expand and refine suitable existing mathematical models of bone growth and magnesium-based implant degradation based on ordinary differential equations. We show that we can simulate the implant degradation, as well as the osseointegration in terms of relative bone volume fraction and changes in bone ultrastructure when applying the model to experimental data from titanium and magnesium-gadolinium implants for healing times up to 32 weeks. An additional sensitivity analysis highlights important parameters and their interactions. Moreover, we show that the model is predictive in terms of relative bone volume fraction with mean absolute errors below 6%.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776245","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":"An ecological and stochastic perspective on persisters resuscitation","authors":"Tania Alonso-Vásquez ,&nbsp;Michele Giovannini ,&nbsp;Gian Luigi Garbini ,&nbsp;Mikolaj Dziurzynski ,&nbsp;Giovanni Bacci ,&nbsp;Ester Coppini ,&nbsp;Donatella Fibbi ,&nbsp;Marco Fondi","doi":"10.1016/j.csbj.2024.12.002","DOIUrl":"10.1016/j.csbj.2024.12.002","url":null,"abstract":"<div><div>Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 1-9"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930755","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}