Antibody repertoire diversity and plasticity is crucial for broad protective immunity. Repertoires change in size and diversity across multiple B cell developmental stages and in response to antigen exposure. However, we still lack fundamental quantitative understanding of the extent to which repertoire diversity is predetermined. Therefore, we implemented a systems immunology framework for quantifying repertoire predetermination on three distinct levels: (1) B cell development (pre-B cell, naive B cell, plasma cell), (2) antigen exposure (three structurally different proteins), and (3) four antibody repertoire components (V-gene usage, clonal expansion, clonal diversity, repertoire size) extracted from antibody repertoire sequencing data (400 million reads). Across all three levels, we detected a dynamic balance of high genetic (e.g., >90% for V-gene usage and clonal expansion in naive B cells) and antigen-driven (e.g., 40% for clonal diversity in plasma cells) predetermination and stochastic variation. Our study has implications for the prediction and manipulation of humoral immunity.
The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which may be defined as Ab or TCR clonal sequences shared across individuals. It has remained unclear whether public clones possess predictable sequence features that differentiate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by CDR3), we detected predictive public clone and private clone-specific immunogenomic differences concentrated in CDR3's N1-D-N2 region, which allowed the prediction of public and private status with 80% accuracy in humans and mice. Our results unexpectedly demonstrate that public, as well as private, clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across individuals and studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way for the construction of a comprehensive atlas of public mouse and human immune repertoires with potential applications in rational vaccine design and immunotherapeutics.
Highlights d Prediction of antibody-antigen binding is a central question in immunology d A motif vocabulary of paratope-epitope interactions governs antibody specificity d Proof of principle that antibody-antigen binding is predictable d Implications for de novo antibody and (neo-)epitope design
Antibody recognition of antigen relies on the specific interaction of amino acids at the paratopeepitope interface. A long-standing question in the fields of immunology and structural biology is whether paratope-epitope interaction is predictable. A fundamental premise for the predictability of paratope-epitope binding is the existence of structural units that are universally shared among antibody-antigen binding complexes. Here, we identified structural interaction motifs, which together compose a vocabulary of paratope-epitope binding that is shared among investigated antibody-antigen complexes. The vocabulary (i) is finite with less than 10 4 motifs, (ii) mediates specific and non-redundant interactions between paratope-epitope pairs, (iii) is immunity-specific (distinct from the motif vocabulary used by non-immune protein-protein interactions), and (iv) enables the machine learning prediction of paratope or epitope. The discovery of a vocabulary of paratope-epitope interaction demonstrates the learnability and predictability of paratope-epitope interaction.
Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML ( immuneml.uio.no ) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML. 1.
150 words) 13 Therapeutic antibody optimization is time and resource intensive, largely because it requires 14 low-throughput screening (10 3 variants) of full-length IgG in mammalian cells, typically resulting 15 in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-16 specificity from a massively diverse sequence space to identify globally optimized antibody 17 variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, 18 rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-19 mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small 20 libraries (10 4 ) produced high quality data capable of training deep neural networks that 21 accurately predict antigen-binding based on antibody sequence. Deep learning is then used to 22 predict millions of antigen binders from an in silico library of ~10 8 variants, where experimental 23 testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set 24 of in silico predicted binders is then subjected to multiple developability filters, resulting in 25 thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate 26 high-dimensional protein sequence space, deep learning offers great potential for antibody 27 engineering and optimization. 28 29 31 hybridomas, phage or yeast display libraries typically result in a number of potential lead candidates. 32However, the time and costs associated with lead candidate optimization often take up the majority of 33 the preclinical discovery and development cycle 1 . This is largely due to the fact that lead optimization 34 of antibody molecules consists of addressing multiple parameters in parallel, including expression level, 35 viscosity, pharmacokinetics, solubility, and immunogenicity 2,3 . Once a lead candidate is discovered, 36 additional engineering is often required; phage and yeast display offer a powerful method for high-37 throughput screening of large mutagenesis libraries (>10 9 ), however they are primarily only used for 38 increasing affinity or specificity to the target antigen 4 . The fact that nearly all therapeutic antibodies 39 require expression in mammalian cells as full-length IgG means that the remaining development and 40 optimization steps must occur in this context. Since mammalian cells lack the capability to stably 41 Deep learning enables therapeutic antibody optimization in mammalian cells 1 replicate plasmids, this last stage of development is done at very low-throughput, as elaborate cloning, 42 transfection and purification strategies must be implemented to screen libraries in the max range of 10 3 , 43 meaning only minor changes (e.g., point mutations) are screened 5 . Interrogating such a small fraction 44 of protein sequence space also implies that addressing one development issue will frequently cause 45 rise of another or even diminish antigen binding altogether, making multi-parameter optimization ve...
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