Immunoinformatics (Amsterdam, Netherlands)最新文献

{"title":"In silico single-cell metabolism analysis unravels a new transition stage of CD8 T cells 4 days post-infection","authors":"Christophe Arpin ,&nbsp;Franck Picard ,&nbsp;Olivier Gandrillon","doi":"10.1016/j.immuno.2024.100038","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100038","url":null,"abstract":"<div><p>CD8 T cell proper differentiation during antiviral responses relies on metabolic adaptations. Herein, we investigated global metabolic activity in single CD8 T cells along an <em>in vivo</em> response by estimating metabolic fluxes from single-cell RNA-sequencing data. The approach was validated by the observation of metabolic variations known from experimental studies on global cell populations, while adding temporally detailed information and unravelling yet undescribed sections of CD8 T cell metabolism that are affected by cellular differentiation. Furthermore, inter-cellular variability in gene expression level, highlighted by single cell data, and heterogeneity of metabolic activity 4 days post-infection, revealed a new transition stage accompanied by a metabolic switch in activated cells differentiating into full-blown effectors.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100038"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000089/pdfft?md5=97df354a875ea3c5bae8550349b1ee7d&pid=1-s2.0-S2667119024000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do domain-specific protein language models outperform general models on immunology-related tasks?","authors":"Nicolas Deutschmann ,&nbsp;Aurelien Pelissier ,&nbsp;Anna Weber ,&nbsp;Shuaijun Gao ,&nbsp;Jasmina Bogojeska ,&nbsp;María Rodríguez Martínez","doi":"10.1016/j.immuno.2024.100036","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100036","url":null,"abstract":"<div><p>Deciphering the antigen recognition capabilities by T-cell and B-cell receptors (antibodies) is essential for advancing our understanding of adaptive immune system responses. In recent years, the development of protein language models (PLMs) has facilitated the development of bioinformatic pipelines where complex amino acid sequences are transformed into vectorized embeddings, which are then applied to a range of downstream analytical tasks. With their success, we have witnessed the emergence of domain-specific PLMs tailored to specific proteins, such as immune receptors. Domain-specific models are often assumed to possess enhanced representation capabilities for targeted applications, however, this assumption has not been thoroughly evaluated. In this manuscript, we assess the efficacy of both generalist and domain-specific transformer-based embeddings in characterizing B and T-cell receptors. Specifically, we assess the accuracy of models that leverage these embeddings to predict antigen specificity and elucidate the evolutionary changes that B cells undergo during an immune response. We demonstrate that the prevailing notion of domain-specific models outperforming general models requires a more nuanced examination. We also observe remarkable differences between generalist and domain-specific PLMs, not only in terms of performance but also in the manner they encode information. Finally, we observe that the choice of the size and the embedding layer in PLMs are essential model hyperparameters in different tasks. Overall, our analyzes reveal the promising potential of PLMs in modeling protein function while providing insights into their information-handling capabilities. We also discuss the crucial factors that should be taken into account when selecting a PLM tailored to a particular task.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100036"},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000065/pdfft?md5=b75c9d971ec449ef41c1c0a25e659b0d&pid=1-s2.0-S2667119024000065-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141090222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using in silico models to predict lymphocyte activation and development in a data rich era","authors":"Salim I Khakoo ,&nbsp;Jayajit Das","doi":"10.1016/j.immuno.2024.100037","DOIUrl":"10.1016/j.immuno.2024.100037","url":null,"abstract":"<div><p>It has become a routine to get insights into the multi-scale nature of immune response in health and disease through ‘omics datasets. This presents us with a unique opportunity to leverage our access to such data to develop computational models that can generate usable predictions and mechanistic insights capable of seeding new ideas. However, this is a particularly challenging task due to the difficulty in integrating data and processes across multiple scales. In this review we discuss some of the challenges associated with this task and also the recent advances and opportunities that will help to makes these tractable, using the innate lymphocyte, the natural killer cell as an exemplar.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100037"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000077/pdfft?md5=61585258de9f43f4ed203fc2826b5a30&pid=1-s2.0-S2667119024000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guiding a language-model based protein design method towards MHC Class-I immune-visibility targets in vaccines and therapeutics","authors":"Hans-Christof Gasser ,&nbsp;Diego A. Oyarzún ,&nbsp;Ajitha Rajan ,&nbsp;Javier Antonio Alfaro","doi":"10.1016/j.immuno.2024.100035","DOIUrl":"10.1016/j.immuno.2024.100035","url":null,"abstract":"<div><p>Proteins have an arsenal of medical applications that include disrupting protein interactions, acting as potent vaccines, and replacing genetically deficient proteins. While therapeutics must avoid triggering unwanted immune-responses, vaccines should support a robust immune-reaction targeting a broad range of pathogen variants. Therefore, computational methods modifying proteins’ immunogenicity without disrupting function are needed. While many components of the immune-system can be involved in a reaction, we focus on Cytotoxic T-lymphocytes (CTLs). These target short peptides presented via the MHC Class I (MHC-I) pathway. To explore the limits of modifying the visibility of those peptides to CTLs within the distribution of naturally occurring sequences, we developed a novel machine learning technique, <span>CAPE-XVAE</span>. It combines a language model with reinforcement learning to modify a protein’s immune-visibility. Our results show that <span>CAPE-XVAE</span> effectively modifies the visibility of the HIV Nef protein to CTLs. We contrast <span>CAPE-XVAE</span> to <span>CAPE-Packer</span>, a physics-based method we also developed. Compared to <span>CAPE-Packer</span>, the machine learning approach suggests sequences that draw upon local sequence similarities in the training set. This is beneficial for vaccine development, where the sequence should be representative of the real viral population. Additionally, the language model approach holds promise for preserving both known and unknown functional constraints, which is essential for the immune-modulation of therapeutic proteins. In contrast, <span>CAPE-Packer</span>, emphasizes preserving the protein’s overall fold and can reach greater extremes of immune-visibility, but falls short of capturing the sequence diversity of viral variants available to learn from. Source code: <span>https://github.com/hcgasser/CAPE</span><svg><path></path></svg> (Tag: <span>v1.1</span>)</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100035"},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000053/pdfft?md5=add2e81105c2c0a169282f80ff064817&pid=1-s2.0-S2667119024000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141049339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SARS-CoV-2-identical protein regions found in mammalian coronaviruses have immunogenic potential and can imply cross-protection","authors":"Luciano Rodrigo Lopes","doi":"10.1016/j.immuno.2024.100034","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100034","url":null,"abstract":"<div><p>Coronaviruses are known to infect a wide range of mammals. In humans, coronaviruses have been responsible for causing the common cold. The immune response against common cold coronaviruses appears to elicit a cross-protective response to SARS-CoV-2. This study identified protein regions in the mammalian coronaviruses' proteome that are identical to those of SARS-CoV-2. Using bioinformatics analysis, the study predicted the involvement of SARS-CoV-2-identical protein regions, identified in mammalian coronaviruses, in antigen-presenting processes and their ability to elicit immune responses. The SARS-CoV-2-identical protein regions were predominantly found in the proteomes of betacoronaviruses, with less prevalence in alphacoronaviruses. Alphacoronaviruses, such as FCoV in domestic felines and MCoV in minks, are known to infect species highly susceptible to SARS-CoV-2. In contrast, betacoronaviruses infect mammals with lower susceptibility to SARS-CoV-2, including dogs, mice, and farmed animals. Furthermore, betacoronaviruses exhibited a higher number of peptides with an increased potential for efficient presentation during the antigen-presenting process, indicating their greater immunogenicity. Conversely, the SW1 gammacoronavirus showed a lower count of SARS-CoV-2 protein regions and a reduced potential for efficient antigen presentation. The results suggested that the elevated number of SARS-CoV-2 identical stretches found in betacoronaviruses may provide potential cross-protection between SARS-CoV-2 and mammalian betacoronaviruses. This cross-protection could be similar to that observed between human coronaviruses causing the common cold and SARS-CoV-2. The limited numbers observed in the proteomes of FCoV, MCoV, and SW1-CoV may offer an explanation for the susceptibility of cats and minks to SARS-CoV-2, as well as a potential vulnerability in cetaceans.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"14 ","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000041/pdfft?md5=910020ff52d72814379c047e7f9baa60&pid=1-s2.0-S2667119024000041-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140533472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of clustering models for inference of T cell receptor antigen specificity","authors":"Dan Hudson ,&nbsp;Alex Lubbock ,&nbsp;Mark Basham ,&nbsp;Hashem Koohy","doi":"10.1016/j.immuno.2024.100033","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100033","url":null,"abstract":"<div><p>The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"13 ","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266711902400003X/pdfft?md5=99e7206f5457951bcd4047d5992bc528&pid=1-s2.0-S266711902400003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139682406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The journey towards complete and accurate prediction of HLA antigen presentation","authors":"Jonas Birkelund Nilsson,&nbsp;Morten Nielsen","doi":"10.1016/j.immuno.2024.100032","DOIUrl":"10.1016/j.immuno.2024.100032","url":null,"abstract":"","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"13 ","pages":"Article 100032"},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000028/pdfft?md5=2f6cf5dc881387d0b5903030768d291a&pid=1-s2.0-S2667119024000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139639896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational and experimental approach to studying NFkB signaling in response to single, dual, and triple TLR signaling","authors":"Thalia Newman ,&nbsp;Annarose Taylor ,&nbsp;Sakhi Naik ,&nbsp;Swati Pandey ,&nbsp;Kimberly Manalang ,&nbsp;Robert A. Kurt ,&nbsp;Chun Wai Liew","doi":"10.1016/j.immuno.2024.100031","DOIUrl":"https://doi.org/10.1016/j.immuno.2024.100031","url":null,"abstract":"<div><p>Modeling and experimental data were used to evaluate how monocytes would respond to dual TLR4/TLR5 and dual TLR4/TLR7 signaling analogous to how the cells would respond to simultaneously encountering different types of pathogens. Both TLR4/TLR5 and TLR4/TLR7 signaling resulted in a decreased NFkB response relative to signaling through a single TLR. The NFkB response also decreased when all three signaling cascades were triggered. The model suggested that competition between the signaling pathways led to the impaired response when the cells were exposed to multiple TLR agonists, however adjusting the level of IRAKs and TABs in the model was insufficient to overcome competition between the signaling pathways. To experimentally examine how modifying TLR signaling proteins would impact the NFkB response to multiple TLR agonists, cells were pre-conditioned with lipopolysaccharide and the response to single, dual, and triple TLR signaling was followed. Pre-conditioning led to a reduction in the NFkB response to all three agonists, likely a consequence of decreased <em>tlr4, tlr5, tlr7, nfkb, tab1, tab2,</em> and <em>tab3</em> expression. Collectively, the model supported exploration of the effects of multiple agonists on the signaling pathways and the effectiveness of adjusting the level of TLR signaling proteins in improving the NFkB response. These experiments and data show the importance of having a model capable of integrating multiple TLR signaling cascades since data generated by the model of a single TLR signaling cascade could not predict how the cells would respond when multiple TLR signaling cascades were activated.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"13 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119024000016/pdfft?md5=e656e8f0367c7afdca5cc7ef50946ade&pid=1-s2.0-S2667119024000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139550047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning improves pMHC kinetic stability and immunogenicity predictions","authors":"Romanos Fasoulis ,&nbsp;Mauricio Menegatti Rigo ,&nbsp;Dinler Amaral Antunes ,&nbsp;Georgios Paliouras ,&nbsp;Lydia E. Kavraki","doi":"10.1016/j.immuno.2023.100030","DOIUrl":"https://doi.org/10.1016/j.immuno.2023.100030","url":null,"abstract":"<div><p>The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at <span>https://github.com/KavrakiLab/TL-MHC</span><svg><path></path></svg>.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"13 ","pages":"Article 100030"},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119023000101/pdfft?md5=8b373c4d3341fd69e7933198d284cc77&pid=1-s2.0-S2667119023000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139100653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning","authors":"María Rodríguez Martínez ,&nbsp;Matteo Barberis ,&nbsp;Anna Niarakis","doi":"10.1016/j.immuno.2023.100029","DOIUrl":"10.1016/j.immuno.2023.100029","url":null,"abstract":"<div><p>The immune system is highly complex, and its malfunctioning can result in many complex disorders. Understanding its inner workings is crucial to designing optimal immunotherapies, developing new vaccines, or understanding autoimmune diseases, just to name a few. Immune-related diseases present unique challenges due to our limited understanding of the complex molecular and cellular interactions involved, as well as the scarcity of available therapeutic options. Recent years have witnessed the progressive development of high-throughput experimental technologies to probe the immune system. This large amount of data has facilitated the emergence of statistical and machine-learning models focused on unravelling the intricate complexities of the immune system. With this vision in mind, a workshop titled \"Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning\" was organized on Sunday, September 18th, 2022 at the 21st European Conference on Computational Biology (ECCB) in Sitges, Spain. The workshop, led by María Rodríguez Martínez, Anna Niarakis, and Matteo Barberis, explored recent statistical models, high-throughput data analyses, and machine learning models to understand immunological mechanisms. More than 60 participants attended the workshop, comprising students, early-career and senior researchers, as well as professionals from diverse domains including Immunology, Systems Biology, Computational Biology, Computer Science, and Bioinformatics. To conclude the workshop, a round table was organized to foster discussions on the existing challenges and chart a roadmap for the development of the next generation of computational models dedicated to investigating the cellular and molecular functions that underlie the immune system.</p></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"12 ","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667119023000095/pdfft?md5=1bd294dd942e97f1e67d4a8e7ca5da39&pid=1-s2.0-S2667119023000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47600835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}