Upcoming Seminars
Upcoming 2025 Seminar Dates (details TBD):
- June 26th
- September 25th
- October 23rd
- November 20th
Past Seminars
March 27, 2025
Inviting Darwin into antibody foundation models
Established Speaker: Erick Matsen, Fred Hutch, USA
Talk abstract: Antibodies are coded by nucleotide sequences that are generated by V(D)J recombination and evolve according to nucleotide mutation and selection processes. Existing antibody language models, however, focus exclusively on antibodies as strings of amino acids and are fit using the masked language modeling objective. In this talk, I will first show that fitting using this objective implicitly incorporates nucleotide-level processes as part of the protein language model, which degrades performance when predicting functional properties of antibodies. To address this limitation, we propose a new framework: a deep amino acid selection model (DASM) that predicts the selective effect of replacing every amino acid with every alternate amino acid. By fitting selection as a separate term from the mutation process, the DASM exclusively quantifies functional effects. This separation of concerns leads to substantially improved performance on standard functional benchmarks. Moreover, our model is an order of magnitude smaller and orders of magnitude faster to evaluate than existing approaches, as well as being readily interpretable. I will then describe some surprising conclusions about how natural selection works for antibodies: there is more to the story than framework vs CDRs!
Speaker bio: Erick trained as a mathematician then as an evolutionary biologist, and now enjoys learning about the immune system by developing probabilistic models of immune repertoires. He is a professor at the Fred Hutch Cancer Center and a Howard Hughes Investigator.
Antibody affinity engineering using antibody repertoire data and machine learning
Early Career Speaker: Lena Erlach, ETH Zurich, Switzerland
Talk abstract: Advanced antibody discovery and engineering workflows take advantage of the combination of high-throughput screening, deep sequencing and machine learning (ML). Most high-throughput methods, however, lack the resolution to provide absolute affinity values of antibody-antigen interactions, limiting their utility for precise engineering of binding kinetics. In this study, we utilize antibody repertoire data, affinity characterization and ML for antibody affinity engineering. Leveraging natural antibody sequence information from repertoires of immunized mice, we identified and experimentally measured affinities for 35 antigen-specific variants. Supervised ML models trained on these sequences achieved remarkable accuracy in predicting affinity, despite the limited dataset size. We utilized the trained ML model to in silico-design eight synthetic antibody variants, of which seven exhibited the desired affinities. Our study illustrates the potential of this streamlined and efficient approach for precise engineering of the affinity of antibodies while reducing extensive experimental screening.
Speaker bio: Lena completed her master’s degree in Biotechnology before joining Sai Reddy’s group at ETH Zurich for a PhD in Computational and Systems Immunology. As she developed a particular interest in statistical and computational modeling, her research explores the use of machine learning to model antibody specificity and affinity using immune repertoire data. By integrating experimental and computational approaches, she aims to deepen our understanding of B cell biology and antibody repertoire dynamics, as well as advance antibody engineering for therapeutic applications.
February 27, 2025
A Broad Survey and Functional Analysis of Immunoglobulin Loci Variation in Rhesus Macaques
Established Speaker: Steven E. Bosinger, Emory University, USA
Talk abstract: Rhesus macaques (RMs) are a vital model for studying human disease and invaluable to pre-clinical vaccine research, particularly for the study of broadly neutralizing antibody responses. Such studies require robust genetic resources for antibody-encoding genes within the immunoglobulin (IG) loci. The complexity of the IG loci has historically made them challenging to characterize accurately. To address this we developed novel experimental and computational methodologies to generate the largest collection to date of integrated antibody repertoire and long-read genomic sequencing data in 106 Indian origin RMs. We created a comprehensive resource of IG heavy and light chain variable (V), diversity (D), and joining (J) alleles, as well as leader, intronic, and recombination signal sequences (RSSs), including the curation of 1474 novel alleles, unveiling tremendous diversity, and expanding existing IG allele sets by 60\%. This publicly available, continually updated resource provides the foundation for advancing RM immunogenomics, vaccine discovery, and translational research.
Speaker bio: Steven Bosinger, PhD, is a researcher within the Emory National Primate Research Center’s Division of Microbiology & Immunology and an Associate Professor in the Emory School of Medicine Department of Pathology & Lab Medicine. He is also Director of the Emory Primate Center’s Nonhuman Primate Genomics Core, which since 2012 has been a resource to researchers who are interested in applying genomic technology to the study of primates and the immune system.
Dr. Bosinger received his PhD in Microbiology & Immunology from the University of Western Ontario. While there, he focused his research on studying the pathogenic events in early HIV and SIV infection. Dr. Bosinger completed his postdoctoral work at the University of Pennsylvania with Dr. Guido Silvestri. It was there he began his work studying how African monkey species, such as the Sooty Mangabey, avoid AIDS despite lifelong SIV infection. He was awarded a Canadian Institutes of Health Research Fellowship and received one of five Young Investigator Awards from the Global HIV Vaccine Enterprise. Today his lab’s research focuses on viral pathogenesis of SIV and SARS-CoV-2 in the primate model, and how B cells are programmed after vaccination.
Computational detection of antigen specific BCRs for antibody discovery
Early Career Speaker: Maria F. Abbate, École Normale Supérieure, France
Talk abstract: B cell receptors (BCRs) play a crucial role in immune recognition, yet only a small fraction respond to a given antigen. Identifying these responsive sequences from immune repertoire data remains a key challenge. In this talk, I will present a computational method leveraging sequence similarity and statistical tools to detect antigen-specific BCRs directly from bulk sequencing. The analysis reveals shared CDR3 signatures across individuals, demonstrating selection at the sequence level. This pipeline was integrated into a complete antibody discovery workflow at Sanofi, where it enabled the identification of therapeutic antibodies targeting GFRAL, a receptor involved in appetite regulation. By integrating bulk and single-cell sequencing, functional antibodies with binding capabilities were identified.
Speaker bio: Maria Francesca Abbate is a computational immunologist with a statistical physics background, specializing in B cell receptor repertoire analysis and antibody discovery. During her PhD at École Normale Supérieure (Paris) in collaboration with Sanofi, she developed computational methods to identify antigen-specific BCRs, integrating statistical modeling and experimental validation. Her work contributed to a complete antibody discovery workflow, leading to the identification of potential therapeutic antibody candidates targeting GFRAL.
January 30, 2025
TIRTL-seq: Deep, quantitative, and affordable paired TCR repertoire sequencing
Established Speaker: Mikhail Pogorelyy, St. Jude Children’s Research Hospital, USA
Talk abstract: The specificity of T cells is determined by T cell receptor (TCR) ɑ and β chain sequences. While bulk TCR sequencing enables cost-effective repertoire profiling without chain pairing information, single-cell approaches provide paired data but are limited in throughput and costly. Here, we present TIRTL-seq (Throughput-Intensive Rapid TCR Library sequencing), a novel experimental and computational methodology for paired TCR repertoire sequencing.
TIRTL-seq is based on the parallel generation of hundreds of TCR libraries in 384-well plates at less than $200 per plate allowing cohort-scale paired TCR-seq studies. We benchmarked TIRTL-seq against state-of-the-art 5’RACE bulk TCR-seq and 10x Genomics Chromium technologies on longitudinal samples and identified SARS-CoV-2- and EBV-specific CD8+ T cell expansions post-infection with distinct dynamics. TIRTL-seq offers a universal protocol scalable from a single cell to millions of T cells per sample, simultaneously delivering both precise clonal frequency estimation and accurate TCR chain pairing, combining the strengths of bulk and single-cell TCR-seq.
Speaker bio: Dr. Mikhail Pogorelyy is the Director of Computational Sciences at St. Jude Children’s Research Hospital, specializing in adaptive immunity and T cell receptor (TCR) repertoire analysis. His research focuses on creating innovative experimental and computational methods for deep paired TCR and BCR repertoire sequencing to unravel immune system dynamics in cancer immunotherapy, gene therapy, and infectious diseases. During his postdoctoral training in the Thomas Lab at St. Jude, he developed a reverse epitope discovery approach to identify TCRs recognizing novel epitopes from repertoire data. He completed his PhD in the Department of Genomics of Adaptive Immunity at IBCH RAS in Moscow, collaborating with Dr. Aleksandra Walczak and Dr. Thierry Mora at ENS in Paris to develop algorithms for identifying TCRs involved in autoimmune responses and statistical models for longitudinal TCR repertoire analysis.
SeQuoIA: a pipeline for inference of phylogeny and selection pressure in BCR sequences from integrative single-cell sequencing data
Early Career Speaker: Eglantine Hector, Marseille Immunology Center, France
Talk abstract: Durable immunity is sustained by the generation of long-lived antibody producing B cells in germinal centers (GCs). Within these 
structures, B cells undergo cycling steps of random mutations in their receptor coding sequences (BCR), followed by antigen-driven selection, before differentiating into effector cells. B cell maturation can therefore be addressed in terms of cellular or molecular evolution, in the darwinian sense. Recent developments in single-cell sequencing technologies allow to get access to both transcriptional profiles and immune repertoires, at high throughput, and provide an insightful approach to dive into GC dynamics. We developed a start-to-finish bioinformatic pipeline for comprehensive BCR repertoire analysis at the single cell level: Selection Quantification in Integrative AIRR data (SeQuoIA). SeQuoIA achieved improved clonotype assignment and phylogenetic reconstruction accuracy over existing tools. Most importantly, our pipeline uniquely leverages BCR clonal tree knowledge to quantify selection based on BCR mutation patterns. Finally, SeQuoIA links BCR features to transcriptional states. With this pipeline, we explored public datasets and proposed new selection mechanisms during GC affinity maturation.
Speaker bio: After completing a master in systems biology at ENS (Paris), I joined the “Integrative B cell Biology” lab in Marseille Immunology Center (CIML), where I am currently doing my PhD. My PhD project led me to explore GC selection orchestration by BCR signaling and Tfh-derived cues, as well as selection mechanisms during PC differentiation. I hope you will enjoy my work on BCR sequencing analyses and be happy to share some thoughts on the topic.
November 21st, 2024
Established Speaker: Camilla Engblom, Karolinska University, Sweden
Speaker bio
Photo credit to Stefan Zimmerman.
Dr. Camilla Engblom is a SciLifeLab Fellow and an Assistant Professor in the Department of Medicine, Solna at the Karolinska Institutet (KI). Dr. Engblom received her PhD in Immunology from Harvard University in 2017 focusing on long-range cancer-host interactions involving myeloid cells (Dr. Mikael Pittet’s lab then at Massachusetts General Hospital/Harvard Medical School). As a MSCA postdoctoral fellow in Dr. Jonas Frisén’s lab (KI), Dr. Engblom developed a spatial transcriptomics-based tool (Spatial VDJ) to map B cell and T cell receptors within human tissues. Located at SciLifeLab and the Center for Molecular Medicine, the Engblom lab’s main research focus is to spatially and functionally resolve B cell clonal dynamics during cancer.
Early Career Speaker: Roy Ehling, Engimmune, Switzerland
Speaker bio
Roy Ehling is a scientist at Engimmune, a pre-clinical biotech company specialising in the development of safe and efficacious soluble TCR therapeutics for oncology indications. He received his PhD from ETH Zurich in Sai Reddy’s Systems and Synthetic immunology group, where he worked on antibody engineering using mammalian display coupled with deep sequencing. During his PhD and the Covid-19 pandemic, he supported the characterisation of neutralising antibodies from plasmablasts and plasma cells and applied these and other antibodies to a library-on-library screening methodology to try and identify coevolutionary trends between neutralising antibody and SARS-CoV-2.
October 24th, 2024
Analysis of human B cell responses in vaccination, infection and allergy with large multiplexed antigen panels
Established Speaker: Scott Boyd, Stanford University, USA
Speaker bio
Dr. Boyd is a physician scientist, Stanford Professor in Food Allergy and Immunology, Co-Director of the Sean N. Parker Center for Allergy and Asthma Research and Professor of Pathology in the Department of Pathology at Stanford University. The Boyd laboratory uses serological analysis, DNA sequencing, single-cell experiments and other tools to study the B cells that make antibodies in human immune responses to infection and vaccination, as well as in immunological disorders such as food allergy and immunodeficiency. Many of the laboratory’s projects study the complex genes that encode antibodies, and the way that these genes form the basis of immunological memory in health and disease.
Dr. Boyd received bachelor’s degrees in Biochemistry at the University of Manitoba, and English Literature at Oxford University, where he was a Rhodes Scholar. He obtained his M.D. from Harvard Medical School and Ph.D. from MIT, followed by pathology residency, hematopathology fellowship and postdoctoral research at Stanford University. He is a 2019 recipient of the Presidential Early Career Award for Scientists and Engineers, and was elected to the American Society for Clinical Investigation in 2020.
Tissue determinants of the human T cell receptor repertoire
Early Career Speaker: Suhas Sureshchandra, University of California Irvine, USA
Speaker bio
Suhas grew up in India before moving to the US to complete his master’s thesis in bioinformatics at Indiana University, Bloomington. He then received his Ph.D. in Biological Sciences at the University of California, Irvine (UCI), where he investigated fetal immune consequences of early life stress exposures such as high-fat diet and maternal obesity. Since 2021, he has been a postdoctoral fellow in Dr. Lisa Wagar’s lab at UCI studying the diversity of T follicular helper cells in pediatric, adult, and geriatric populations. Suhas is broadly interested in applying systems biology approaches to measure T and B cell responses to vaccines and viruses using the immune organoid system developed in the lab. His long-term goal is to be an independent investigator at an academic institute. Beyond his professional pursuits, he has a keen interest in traveling and exploring new cuisines.
September 26th, 2024
The T cell receptor core qualities, as seen by a language model
Established Speaker: Sol Efroni, Bar Ilan University, Israel
Speaker bio
Sol Efroni did his PhD at the Weizmann Institute of Science (Computer Science and Immunology) and his postdoctoral training at the NCI(NIH) working on personalized cancer genomics. He started his own group at Bar-Ilan University in 2009 and has more recently been focusing on the T-cell Repertoire.
Generative Modeling of AIRRs with SoNNia
Early Career Speaker: Giulio Isacchini, Imprint, USA
Speaker bio
I am currently a Senior Machine Learning Research Scientist at IMPRINT. Before, I was a Humboldt Research Fellow at UniLeipzig, a Postdoctoral Scientist at UC Berkeley, and a PhD student jointly between ENS Paris and MPI for Dynamics and Self-Organisaiton, Goettingen.
April 25th, 2024
Antibody Discovery Using LIBRA-seq
Established Speaker: Ivelin Georgiev, Vanderbilt University Medical Center
NO RECORDING AVAILABLE FOR THIS TALK
Speaker bio
Dr. Georgiev received his Ph.D. in Computer Science from Duke University, after which he served as a staff scientist and co-head of the Structural Bioinformatics Core Section at the Vaccine Research Center, NIH. Dr. Georgiev is currently a Professor in the Departments of Pathology, Microbiology, and Immunology and of Computer Science at Vanderbilt. Since 2020, he has served as the Director of Graduate Studies for the Vanderbilt Program in Chemical and Physical Biology. Dr. Georgiev is the founding Director of the Vanderbilt Program in Computational Microbiology and Immunology, a program focused on supporting research, educational, and strategic efforts that aim to employ the power of computation toward the development and application of innovative approaches for treatment and prevention of disease and for enhancement of human health. Dr. Georgiev’s research is focused on the development and application of technologies related to problems in the fields of immunology and virology, with a special emphasis on structure-based vaccine design and antibody discovery and optimization.
Biochemical and biophysical characterization of natural polyreactivity in antibodies
Early Career Speaker: Marta T. Borowska, Stanford University
Speaker bio
Marta grew up in Łódź and studied in Poland and Austria before moving to the University of Chicago on a year-long fellowship to complete her master’s studies. She then joined the University of Chicago as a Ph.D. candidate to pair her interests in structural biology and immunology. She naturally gravitated to the area of unconventional immune recognition because its mechanisms are largely unknown. Now, she continues to use this foundation as a springboard for engineering designer approaches in immunotherapy at Stanford University. Her long-term goal is to be an independent investigator running a research laboratory at an academic institute. Marta is broadly interested in complex immune signaling and aims to develop protein engineering approaches to control and modulate it. She enjoys playing chess and exploring science through art and is a proud member of the LBGTQ+ community.
March 28th, 2024
The Hidden Diversity of Antibody Heavy Chains: Implications for Autoantibody Mediated Disease
Established Speaker: Melissa Smith, University of Louisville
Speaker bio
Dr. Melissa Smith obtained her PhD in Virology at Harvard University, and continued her training in Immunology at the Institut Pastuer, Paris. She initially became interested in the potential of long-read sequencing for mapping viral evolution and immune escape in response to antibody-mediated neutralization. Dr. Smith pursued this goal, working briefly at Pacific Biosciences, developing targeted microbiology, virology, and immunology methods for single-molecule sequencing. She returned to academic research in 2016, first as Associate Director of Technology Development at the Icahn School of Medicine at Mount Sinai (New York, USA), and now as an Assistant Professor at the University of Louisville (Kentucky, USA). Today Dr. Smith focuses on utilizing innovative long-read methods for highly accurate resolution of complex genomic regions, specifically those that encode immune receptors where high levels of genomic variation can influence response to vaccination, susceptibility to autoimmunity, or development of adverse events in the context of immunotherapy.
Genome poises antibody repertoire from early B cell development
Early Career Speaker: Oscar Rodriguez, University of Louisville
Speaker bio
Oscar Rodriguez is a postdoctoral fellow in the lab of Corey Watson at the University of Louisville School of Medicine. His research focuses on population-level germline immunoglobulin genetic variation and its impact on inter-individual antibody repertoire differences. Recently, he has demonstrated for the first time the significant effect of extensive immunoglobulin germline variation on the antibody repertoire. As a Ph.D. student at the Icahn School of Medicine at Mount Sinai, he developed the first comprehensive tool suite for long-read-based assembly and curation of genetic variation in the immunoglobulin (IG) and T-cell receptor (TCR) loci.
February 22nd, 2024
Learning to read and write antibody evolution
Speaker bio
Brian Hie is an Assistant Professor of Chemical Engineering and Data Science at Stanford University and an Innovation Investigator at Arc Institute, where he conducts research at the intersection of biology and machine learning. He was previously a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI. He completed his Ph.D. at MIT CSAIL and was an undergraduate at Stanford University.
Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity
Speaker bio
Mamie Wang is a PhD student in Computational Biology at Yale University, working in the lab of Steven Kleinstein. Her research interests involve developing and applying computational methods for understanding B cell specificity and repertoire biology during human immune response.
January 25th, 2024
Integration of Clinical, Laboratory, and Multi-omics Data to Leverage Machine Learning for Diagnostics
Established Speaker: Enkelejda Miho, University of Applied Sciences and Arts, Northwestern Switzerland
Talk abstract
Early and accurate disease diagnosis is crucial for preventing disease development and defining therapy strategies. Due to predominantly unspecific symptoms, diagnosis of autoimmune diseases is notoriously challenging. However, multiple types of data are not leveraged for precision diagnostics due to the difficulties of integrating and encoding multi-omics data with clinical and laboratory values, as well as a lack of standardization; clinical decision support systems are often limited to only certain data types. Accordingly, even sophisticated data models fall short when supporting accurate diagnoses and presenting data analyses in a user-friendly form. Therefore, the integration of various data types is not only an opportunity but also a competitive advantage in research and for the industry. We have developed an integration pipeline to enable the use of machine learning for patient classification based on multi-omics data such as genetics, immunomics and metabolomics, in combination with clinical values and laboratory results. Machine learning models resulted in 95% prediction accuracy of autoimmune diseases using integrated data. Our results deliver insights into autoimmune disease research and have the potential to be adapted for applications across disease conditions.
Speaker bio
Enkelejda Miho is a professor of Digital Life Sciences and team leader of the Laboratory of Artificial Intelligence in Health (aiHealthLab) as well as group leader at Swiss Bioinformatics Institute. Her research focuses on the use of computer science and artificial intelligence for drug discovery and personalized medicine. The mission of aiHealthLab is to apply artificial intelligence in order to set standards, understand mechanisms and guide decisions in healthcare. The group uses analytics for personalized medicine, drug discovery and development, and support systems in clinics.
Spatiotemporal development of the human T follicular helper cell response to Influenza vaccination
Early Career Speaker: Stefan Schattgen, St. Jude Children’s Research Hospital, USA
Talk abstract
We profiled blood and draining lymph node (LN) samples from human volunteers after influenza vaccination over two years to define evolution in the T follicular helper cell (TFH) response. We show LN TFH cells expanded in a clonal-manner during the first two weeks after vaccination and persisted within the LN for up to six months. LN and circulating TFH (cTFH) clonotypes overlapped but had distinct kinetics. LN TFH cell phenotypes were heterogeneous and mutable, first differentiating into pre-TFH during the month after vaccination before maturing into GC and IL-10+ TFH cells. TFH expansion, upregulation of glucose metabolism, and redifferentiation into GC TFH cells occurred with faster kinetics after re-vaccination in the second year. We identified several influenza-specific TFH clonal lineages, including multiple responses targeting internal influenza proteins, and show each TFH state is attainable within a lineage. This study demonstrates that human TFH cells form a durable and dynamic multi-tissue network.
Speaker bio
Stefan Schattgen is a research scientist and group leader in the lab of Paul Thomas at St Jude Children’s Research Hospital, USA. In 2015 he received his doctorate in immunology and virology at the University of Massachusetts Medical School where he worked on innate immune sensing of viral infections. He turned his focus towards T cell repertoire biology during his postdoctoral training with Paul Thomas from 2015-2020. His current research interests blend computational and wet lab methods for understanding underlying relationships between a T cell’s specificity and their phenotype and functions during homeostasis, infection, vaccination, and cancer.
November 30th, 2023
Temporal Development of T cell receptor Repertoires during Childhood in Health and Type 1 Diabetes
Speaker bio
Aaron Michels MD, is a physician-scientist at the Barbara Davis Center for Diabetes, which is part of the University of Colorado. He has lived with type 1 diabetes for more than three decades and is committed to caring for patients with diabetes along with conducting research to prevent and ultimately cure the disease. His research focuses on understanding the basic immunology of type 1 diabetes to monitor diabetes-specific T cell receptor sequences during the stages of type 1 diabetes development and design safe and specific therapies to stop the autoimmune destruction of insulin producing pancreatic beta cells.
Diet and Microbiota effects on the B cell repertoire
Early Career Speaker: Julien Limenitakis, University of Bern, Switzerland
Talk abstract
Colonization by the microbiota causes a marked stimulation of B cells and induction of immunoglobulin, but mammals colonized with many taxa have highly complex and individualized immunoglobulin repertoires. To study this, we opted for a simplified model of defined transient exposures to different microbial taxa in germ-free mice to deconstruct how the microbiota shapes the B cell pool and its functional responsiveness. We previously showed that microbial exposure at the intestinal mucosa generated oligoclonal responses that differed from those in germ-free mice, and from the diverse repertoire that was generated after intravenous systemic exposure to microbiota. Our results reflected a contrast between a flexible response to systemic exposure with the need to avoid fatal sepsis, and a restricted response to mucosal exposure that reflects the generic nature of host–microbial mutualism in the mucosa. A hallmark of mucosal IgA is the high mutational load that accumulates throughout life. This has been mainly interpreted in terms of differences in microbial induction, although once established in early life, the microbiome of humans and experimental mice is relatively stable and mutational activity has been shown to be independent of B cell receptor signaling. Using germ-free and colonized mice provided with different diets formulated with proprietary grain-based processing or from purified chemicals with different principal macronutrient calorie sources, we show that diet affects IgA induction, its repertoire and mutational diversification independently of microbial exposure.
Speaker bio
Dr. Julien Limenitakis received his PhD in Microbiology from the University of Geneva. He then joined the Mucosal Immunology group of Andrew Macpherson with a transitional Post-doctoral fellowship from the Swiss Systems Biology Initiative, switching fields to apply systems biology approaches and computational methods to study interactions of gut microbes with the immune system. Currently he is a senior scientist at the University Hospital Bern. His work focuses on how exposure to intestinal microbes, in particular during early life development, shapes B-cell repertoires.
September 28th, 2023
On the application of TCR-epitope prediction models
Established Speaker: Pieter Meysman, University of Antwerp, Belgium
Talk abstract
The recognition of a T-cell epitope target is driven by the unique sequence of the T-cell receptor (TCR). As the TCR sequence theoretically contains all the information that determines its target, it must be possible to infer its target from this sequence. Indeed, within specific settings, we now have performant machine learning models that can address this challenge. It has therefore become necessary to start considering how we can apply these models to gain novel immunological understanding.
In this talk, I want to focus on the application of TCR-epitope prediction models to identify epitope-specific T-cells in full repertoire data, and the additional challenges that are often missed by current evaluation efforts. I also aim to highlight the biggest advancements and milestones that we can expect in the coming years in the field, especially in regard to the unseen epitope prediction problem.
Speaker bio
Prof. Pieter Meysman is an associate professor at the University of Antwerp at the ADREM data lab and leads the immunoinformatics activities of the AUDACIS consortium. He graduated as a PhD in bioscience engineering from the KULeuven in 2012, and has published more than 80 research articles and patents. In addition to his academic research, he is part-time CTO of ImmuneWatch, an AI company aiming to decode the T-cell receptor repertoire.
His main research focus is on the use of artificial intelligence to gain understanding into the adaptive immune system. To this end, he has supervised the development of several immunoinformatics tools to link T-cell receptors to their (seen or unseen) targets, including TCRex, ClusTCR and ImRex. He has won a number of awards for his research into the human T-cell receptor repertoire, including most recently the GSK Vaccines award.
Predicting T Cell Receptor Functionality against Mutant Epitopes
Early Career Speaker: Felix Drost, Helmholtz Munich, Germany
Talk abstract
Pathogens and cancer cells can escape recognition by T cell receptors (TCRs) through mutations of immunogenic epitopes. TCR cross-reactivity, i.e., recognition of multiple epitopes with sequence similarities, can counteract such mutational escape, but also may cause severe side effects in cell-based immunotherapies. To predict the effect of epitope mutations on T cell functionality in silico, we present “Predicting T cell Epitope-specific Activation against Mutant versions” (P-TEAM). The Random Forest based model was developed on two comprehensive datasets of murine and human TCRs in response to systematic single-amino acid mutations of their target epitopes to predict T cell reactivity for unobserved mutations, or even unseen TCRs. P-TEAM is complemented with an active learning framework to guide experimental design to minimize primary data acquisition costs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.
Speaker bio
Felix Drost is a doctoral researcher at the Computational Health Center at the Helmholtz Centre Munich under the supervision of Dr. Benjamin Schubert. His goal is to develop effective computational tools for the development of vaccines and biotherapeutics. In his research, he applies machine learning based methods to investigate the T cell Receptor-Epitope landscape through prediction, multimodal integration, and representation learning.
June 29th, 2023
Contemplating MHC peptidomes to better predict them
Established Speaker: David Gfeller, University of Lausanne, Switzerland
Talk abstract
T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC molecules. The heterogeneity of the MHC peptidome, including the high polymorphism of MHC genes, is influencing TCR repertoires and represents an important challenge towards accurate prediction and identification of T-cell epitopes in different individuals and different species. Here we generated and curated a dataset of more than a million unique MHC-I and MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of >200 MHC alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC motifs and revealed alternative binding modes of MHC ligands. We then developed machine learning frameworks to accurately predict binding specificities and ligands of any MHC-I (MixMHCpred) and MHC-II (MixMHC2pred) allele, and further integrated TCR recognition into our epitope prediction pipeline (PRIME). Prospectively applying our tools to SARS-CoV-2 proteins identified several epitopes and TCR sequencing revealed a monoclonal response in effector/memory CD8+ T cells against one of these epitopes with cross-reactivity against the homologous peptides from other coronaviruses. Overall, our work shows how in depth characterization of MHC motifs can help mapping the targets of T cells and understanding TCR cross-reactivity.
Speaker bio
David Gfeller studied Physics and did his PhD in Theoretical Physics at EPFL. He then transitioned into biology with a post-doc in Toronto. After a second post-doc at the Swiss Institute of Bioinformatics, he was recruited as Assistant Professor at the Department of Oncology at the University of Lausanne. In 2019, he was promoted to Associate Professor. His research is focused on using computational biology to understand and predict cancer immune cell interactions.Germline-encoded amino acid–binding motifs drive immunodominant public antibody responses
Early Career Speaker: Ellen Shrock, Harvard University, USA
Talk abstract
Despite the vast diversity of the antibody repertoire, infected individuals often mount antibody responses to precisely the same epitopes within antigens. The immunological mechanisms underpinning this phenomenon remain unknown. By mapping 376 immunodominant “public epitopes” at high resolution and characterizing several of their cognate antibodies, we concluded that germline-encoded sequences in antibodies drive recurrent recognition. Systematic analysis of antibody-antigen structures uncovered 18 human and 21 partially overlapping mouse germline-encoded amino acid–binding (GRAB) motifs within heavy and light V gene segments that in case studies proved critical for public epitope recognition. GRAB motifs represent a fundamental component of the immune system’s architecture that promotes recognition of pathogens and leads to species-specific public antibody responses that can exert selective pressure on pathogens.
Speaker bio
Ellen Shrock received her Ph.D. in Biological and Biomedical Sciences from Harvard University, where she worked in the laboratory of Prof. Stephen Elledge using high-throughput profiling techniques to study the human antibody response to viruses and the mechanisms underlying antigen immunodominance. Ellen received her A.B. in Integrative Biology from Harvard College, where she conducted research with Prof. George Church on genome editing for porcine-to-human xenotransplantation and on the assembly of a synthetic, radically recoded E. coli genome.
May 25th, 2023
Collectively deciphering the rules of immune receptor-antigen binding: deeply analyse the problem, obtain the necessary data, define standardised benchmarks, and ensure effective method comparison
Established Speaker: Geir Kjetil Sandve, University of Oslo, Norway
Talk abstract
As individual researchers, we are well-trained in designing research projects that address specific and moderately challenging questions. However, when faced with a grand challenge such as understanding antigen recognition by T- and B-cell receptors, how can we, both individually and collectively, effectively tackle this problem? We currently have antigen binding data for only a minuscule proportion of the immune receptor sequence space, and despite the rapid emergence of new machine learning methods, consensus on what constitutes the most promising directions forward remains scarce.
In my talk, I will offer my perspective on this strategic question and discuss some recently published and ongoing work aligned with this strategy. Briefly, I believe the first step should be a rigorous characterisation of the computational challenges of the immune receptor-antigen binding prediction problem. Second, we must ensure that sufficient data is available to guide methodology development, where we in the foreseeable future need to rely on the combined use of experimental and simulated data. Third, we must prioritise interoperability and reproducibility of methods, along with the development of standardised benchmarks, to effectively compare performance and identify limitations of current approaches.
Speaker bio
Professor in machine learning at Department of Informatics, University of Oslo. Currently working on deciphering antigen recognition by immune receptors, mainly focused at the sequence level.
LZGraphs – From Theory to Immunity: Merging Compression Theory and Immunology to Enhance Our Understanding of the Adaptive Immune Receptor Repertoire
Early Career Speaker: Thomas Konstantinovsky, Bar Ilan University, Israel
Talk abstract
A new approach that utilizes the Lempel-Ziv 76 algorithm (LZ-76) to encode and represent AIRRs without relying on sequence annotation. The approach involves creating a graph-like model, which enables a wide range of potential applications, including generation probability inference, informative feature vector derivation, sequence generation, sequence analysis, and a new measure for repertoire diversity estimation.
Speaker bio
Thomas Konstantinovsky is a computer scientist with a passion for bridging the fields of theoretical and classic computer science with computational immunology. After devoting three years to conducting computational research at the Sagol Research Center of Epigenetics and Aging, He joined Gur Yaari’s lab to pursue his Ph.D. and the goal of developing methods to uncover the mysteries of the adaptive immune system.
April 27th, 2023
Modelling dynamics of CD8 T cell response to SARS-CoV-2’s emerging variants of concern
Established Speaker: Hashem Koohy, Oxford University, UK
Talk abstract
T cells play a crucial role in our immunity by recognizing and eliminating infected and abnormal cells. T cell response is triggered upon T cell recognition of specific antigens presented by MHC molecules on the surface of target cells. However, the underlying rules of the interactions are incompletely understood. Over the past few years, we have been investigating T cell cross-reactivity and common specificity as two key drivers of T cell antigen specificity. In this seminar, I will focus on T cell cross-reactivity, and will introduce a new deep neural network model that we have developed for accurate and reliable prediction of CD8 T cell targets. I will illustrate how we have been using this model to model the dynamics of CD8 T cell response to emerging SARS-CoV-2 mutant variants.
Speaker bio
Hashem Koohy is an Associate Professor of Systems Immunology at the MRC Weatherall Institute of Molecular Medicine (MRC WIMM), Oxford University. He leads a research group that aims to understand the basic principles of adaptive immunity using integrative machine learning approaches applied to emerging single-cell sequencing data. His group is specifically interested in decoding the antigen-specific T cell response in time and space.
Hashem was awarded his PhD in Systems Biology from the University of Warwick. He then completed two postdoctoral fellowships at the Sanger and Babraham Institute in Cambridge. In 2017, he moved to Oxford to establish his research group, where he currently leads a research programme.
Germline-encoded specificities and the predictability of the B cell response in models and experiments
Early Career Speaker: Marcos Vieria, University of Chicago, USA
Talk abstract
Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors are encoded by particular immunoglobulin genes. This raises the possibility that the presence of particular germline genes in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline genes’ propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first show how this can happen in simulations: even when fitness differences between germline genes lead to similar gene usage across individuals early on, gene usage can become increasingly dissimilar over time. We next find that mice experimentally infected with influenza virus demonstrate the same pattern of divergence in the weeks following infection. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest germline-encoded specificities might enable fast recognition of specific antigens early in the response, but diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination in the long term.
Speaker bio
Marcos Vieira is a Senior Research Scientist in the Cobey Lab at the University of Chicago, where he also obtained his PhD. He uses computational, statistical and mathematical tools to study immunity at different scales, from the within-host evolution of B cells to the effects of host immune history on the epidemiology and evolution of viruses.
