Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires

Abstract

The adaptive immune system is a natural diagnostic and therapeutic. It recognizes threats earlier than clinical symptoms manifest and neutralizes antigen with exquisite specificity. Recognition specificity and broad reactivity is enabled via adaptive B-and T-cell receptors: the immune receptor repertoire. The human immune system, however, is not omnipotent. Our natural defense system sometimes loses the battle to parasites and microbes and even turns against us in the case of cancer and (autoimmune) inflammatory disease. A long-standing dream of immunoengineers has been, therefore, to mechanistically understand how the immune system "sees", "reacts" and "remembers" (auto)antigens. Only very recently, experimental and computational methods have achieved sufficient quantitative resolution to start querying and engineering adaptive immunity with great precision. In specific, these innovations have been applied with the greatest fervency and success in immunotherapy, autoimmunity and vaccine design. The work here highlights advances, challenges and future directions of quantitative approaches which seek to advance the fundamental understanding of immunological phenomena, and reverse engineer the immune system to produce auspicious biopharmaceutical drugs and immunodiagnostics. Our review indicates that the merger of fundamental immunology, computational immunology and (digital) biotechnology minimizes black box engineering, thereby advancing both immunological knowledge and as well immunoengineering methodologies. Introduction 3Advancing immunology through engineering innovations 3Adaptive immune receptors are natural diagnostics and therapeutics 3Engineering the vast immune receptor sequence space requires quantitative approaches 4Current approaches for immune repertoire analysis and immunoengineering 4Computational immunology and immunoinformatics of adaptive immunity 4 B-and T-cell pattern mining using machine and deep learning 6Mathematical modeling of immune receptor recognition 8Computational modeling of immune receptor 3D structure 9Computational modeling of antibody-epitope interaction 10Genomic sequencing of immune repertoires 12Identifying candidate TCRs or antibodies via high-throughput library screens 13Proteomic sequencing and serological profiling of antibody repertoires 14 Future directions for quantitative immunoengineering and immune receptor analysis 15Setting targets on public and private immune receptors 15Efficient modification of immune receptor activity in vitro and in vivo 16De novo design of immune receptor sequences 18Closing the data gap between immune receptor sequence and cognate epitope for immune receptor and epitope engineering 21Challenges in machine learning analysis on immune receptor repertoires 21Relating immune receptor antigen specificity to cellular transcriptomic profile 24 Conclusion 25 Conflicts of Interest 25 Focus Boxes 26Focus Box 1: Brief summary of deep learning and its architectures. 26Focus Box 2: Recognition holes in the immune repertoire 26 Notes and references 30mo...

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

“…Although there have been several attempts to predict paratope-epitope binding, prediction accuracy and generalization capacity have been generally suboptimal thus far (Brown et al, 2019). More fundamentally, the problem of antibody-antigen binding is generally regarded as too high-dimensional.…”

“…Adaptive immune receptor repertoires represent a major target area for the application of machine learning in the hope that it may fast-track the in silico discovery and development of immunereceptor based immunotherapies and immunodiagnostics (Brown et al, 2019;Greiff et al, 2012;Mason et al, 2018Mason et al, , 2019Miho et al, 2018). The complexity of sequence dependencies that determine antigen binding (Dash et al, 2017;Glanville et al, 2017), immune receptor publicity (Greiff et al, 2017b) and immune status (immunodiagnostics) (Ostmeyer et al, 2019;Thomas et al, 2014) represent a perfect application ground for machine learning analysis (Arora et al, 2019;Cinelli et al, 2017;Greiff et al, 2017b;Liu et al, 2019;Mason et al, 2019;Sidhom et al, 2018;Sun et al, 2017).…”

“…The complexity of sequence dependencies that determine antigen binding (Dash et al, 2017;Glanville et al, 2017), immune receptor publicity (Greiff et al, 2017b) and immune status (immunodiagnostics) (Ostmeyer et al, 2019;Thomas et al, 2014) represent a perfect application ground for machine learning analysis (Arora et al, 2019;Cinelli et al, 2017;Greiff et al, 2017b;Liu et al, 2019;Mason et al, 2019;Sidhom et al, 2018;Sun et al, 2017). As discussed extensively in a recent literature review by us (Brown et al, 2019) the development of ML approaches for immune receptor datasets was and is still hampered by the lack of ground truth datasets. Ground truth datasets are defined by the property that the link between the class of a sequence (class = disease/antigen specificity) or repertoire and the underlying sequence structure is known a priori.…”

“…Indeed, the discovery of general interaction sequence motifs was encoding-dependent and machine-learning-independent. As previously described at length (Brown et al, 2019), we believe that machine learning approaches adapted to the complexity and intricacies of immune receptor sequence data will have to be combined with sequence encodings that aim to answer specific questions. In fact, prediction tasks may be aided or complicated by a given encoding.…”

“…The predictability of paratope-epitope binding is a prerequisite for predicting antibody specificity and in silico antibody and vaccine design. So far, however, it remains unclear whether paratope-epitope binding is predictable (Brown et al, 2019;Raybould et al, 2019a;Sela-Culang et al, 2013).…”