Engineering immunity with quantitative tools

Christopher M. Jewell

Christopher M. Jewell is an Associate Professor and the Associate Chair for Research in the Fischell Department of Bioengineering at the University of Maryland. He is also the Director of the University's BioWorkshop Core Instrument Facility, and a fellow of the American Institute for Medical and Biological Engineering (AIMBE). His research harnesses engineering and immunology to study and manipulate immune function, with a focus on therapeutic vaccines for cancer and autoimmune disease. Dr Jewell has received numerous awards, including the Damon Runyon-Rachleff Innovator Award, the NSF CAREER Award, and selection as the state of Maryland's Outstanding Young Engineer. Dr Jewell graduated from Lehigh University in 2003 with high honors, earning dual degrees in Chemical Engineering and Molecular Biology. He received his PhD in 2008 from the University of Wisconsin – Madison then joined the Boston Consulting Group, where he worked in R&D pharma strategy. Dr Jewell carried out his postdoctoral training as a Ragon Institute Fellow working with Dr Darrell Irvine at MIT and as a visiting scientist at Harvard with Dr Dan Barouch. Further information about Prof. Jewell and his research group is available at http://jewell.umd.edu/

Jeffrey J. Gray

Jeffrey J. Gray is Professor of Chemical and Biomolecular Engineering at the Johns Hopkins University, with joint appointments in the Sidney Kimmel Comprehensive Cancer Center (Oncology) and the Program in Molecular Biophysics. He earned his BSE in chemical engineering at the University of Michigan and his PhD in chemical engineering at the University of Texas at Austin, and he completed post-doctoral training at the University of Washington. His research focuses on computational protein structure prediction and design, particularly protein–protein docking, antibody engineering, protein–surface interactions, and protein–carbohydrate interactions. Gray is a fellow of the AIMBE, and his awards include the AIChE's David Himmelblau Award, the Beckman Young Investigator Award, and the Johns Hopkins Alumni Association Excellence in Teaching Award. He serves on the editorial board of Proteins and on the Rosetta Commons Executive Board as the Diversity Chair. Further information about Prof. Gray and his research group is available at http://graylab.jhu.edu.

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Immunology is being revolutionized today because of two dramatic developments: i) the ability to gain an unprecedented scale of data and ii) the ability to capture, model, and design using advanced computational tools based on physics, statistics, and big data. Thus, while vaccines and immunotherapies already have tremendous public health benefits, these data revolutions are putting new solutions in reach that will help tackle infectious disease, cancer, and immune defects. Many hurdles across these diverse settings are underpinned by the enormous complexity and dynamics inherent in immune function. This complexity makes quantitative approaches challenging to develop, but this goal is within reach with new simulation and computational strategies, techniques for generating high-content data, and quantitative tools that enable more insightful and efficient analysis.

In this themed collection we bring together exciting new work in these areas, all aimed at better understanding and controlling immune function. Beginning with the molecular scale, Cisneros et al. examine the molecular interface between the antibody heavy and light chain (DOI: 10.1039/C8ME00080H). In particular, they probe how somatic mutations influence the domain orientations and affect antigen binding as the antibody evolves in response to HIV. As incredible data sets are gathered of repertoires of antibody sequences, there is an interest in also examining structures for these data sets.^1–3 Structural laboratory experiments are impossible at this scale, but computation offers an alternative. Raybould et al. survey the available tools for generating repertoires of structures (DOI: 10.1039/C9ME00034H), and Schritt et al. introduce a new tool based on overlapping multiple sequence alignments (DOI: 10.1039/C9ME00020H). Schritt et al.'s method can generate tens of thousands of reasonably accurate structures in half an hour. And then Xu et al. provide a method that uses support vector machines to cluster multiple antibody structures into groups that bind different epitopes (DOI: 10.1039/C9ME00021F). Together with Schritt et al.'s work, these are two key steps toward being able to computationally decipher a person's immunological history from a repertoire of sequences. Summarizing advances like these, Brown et al. comprehensively review immunome sequencing technologies (B and T cells), computational methods for receptor and repertoire analysis (including deep learning approaches), and possibilities in immunoengineering (DOI: 10.1039/C9ME00071B).

The next larger length scale is examining immune monitoring over tissues, particularly looking for cancer cells which might emerge anywhere in the body. Ozik et al. combine agent-based simulations with an extreme-scale modeling platform to examine the spatial dynamics of the interaction between a tumor and the immune system (DOI: 10.1039/C9ME00036D). Finally, Chin et al. focus on the identification of useful biomarkers for targeting immunotherapies (DOI: 10.1039/C9ME00029A). They review the computational strategies for using high-throughput sequence data.

These and other studies included in this themed collection highlight the potential of combining designed, engineering approaches with immunological expertise. As this exciting interface develops, the resulting insight will undoubtedly support better ways to maintain healthy immune function, diagnose disease earlier, and provide more effective or selective options for combating disease.

References

1. Frontiers Research Topics, https://www.frontiersin.org/research-topics/5944/next-generation-sequencing-of-human-antibody-repertoires-for-exploring-b-cell-landscape-antibody-dis (accessed June 2019).
2. G. Georgiou, et al. , Nat. Biotechnol., 2014, 32, 158–168.
3. X. Liu and J. Wu, Cell Biol. Toxicol., 2018, 34(6), 441–457.

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