Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data
Name
PhysRevE.105.024401.pdf
Description
Published version
Size
1.33 MB
Format
Adobe PDF
Checksum (MD5)
3928cb51478ef69c6e54db98b5f188f0
Author(s)
Doelger, Julia
Kardar, Mehran
Chakraborty, Arup K
Date Issued
February 2022
Journal
Physical Review E
Publisher
American Physical Society (APS)
Citation
Doelger, Julia, Kardar, Mehran and Chakraborty, Arup K. 2022. "Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data." Physical Review E, 105 (2).
Version
Final published version
Abstract
There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. To find such a broadly protective immunization strategy, it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenzalike evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method, we are able to infer single as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for the design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. In the future, this approach may be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Ragon Institute of MGH, MIT and Harvard
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DOI of Published Version
https://doi.org/10.1103/physreve.105.024401
