Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data
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...
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| Format: | Article |
| Language: | English |
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American Physical Society (APS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/141984 |
| _version_ | 1811086125447512064 |
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| author | Doelger, Julia Kardar, Mehran Chakraborty, Arup K |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Doelger, Julia Kardar, Mehran Chakraborty, Arup K |
| author_sort | Doelger, Julia |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:21:14Z |
| format | Article |
| id | mit-1721.1/141984 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:21:14Z |
| publishDate | 2022 |
| publisher | American Physical Society (APS) |
| record_format | dspace |
| spelling | mit-1721.1/1419842024-03-19T14:16:00Z Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data Doelger, Julia Kardar, Mehran Chakraborty, Arup K 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 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. 2022-04-20T17:54:43Z 2022-04-20T17:54:43Z 2022-02 2022-04-20T17:47:27Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/141984 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). en 10.1103/physreve.105.024401 Physical Review E Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Physical Society (APS) APS |
| spellingShingle | Doelger, Julia Kardar, Mehran Chakraborty, Arup K Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title | Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title_full | Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title_fullStr | Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title_full_unstemmed | Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title_short | Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| title_sort | inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data |
| url | https://hdl.handle.net/1721.1/141984 |
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