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
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.
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
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.
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.
June 29th, 2023
Contemplating MHC peptidomes to better predict them
Established Speaker: David Gfeller, University of Lausanne, Switzerland
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.
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
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.
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.
September 28th, 2023
Established Speaker: Pieter Meysman, University of Antwerp, Belgium
Early Career Speaker: TBD
October 26th, 2023
Established Speaker: Enkelejda Miho, University of Applied Sciences and Arts, Northwestern Switzerland
Early Career Speaker: Anastasia Minervina, St. Judes Children’s Research Hospital, USA
November 30th, 2023
Established Speaker: Aaron Michels, University of Colorado Anschutz Medical Campus, USA
Early Career Speaker: TBD
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
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.
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
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.
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.