Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes
Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as t...
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| Format: | Article |
| Language: | en_US |
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American Physical Society
2014
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| Online Access: | http://hdl.handle.net/1721.1/85205 https://orcid.org/0000-0003-1467-421X https://orcid.org/0000-0003-1268-9602 https://orcid.org/0000-0002-1112-5912 |
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| author | Shekhar, Karthik Ruberman, Claire Ferguson, Andrew L. Kardar, Mehran Barton, John P. Chakraborty, Arup K |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Shekhar, Karthik Ruberman, Claire Ferguson, Andrew L. Kardar, Mehran Barton, John P. Chakraborty, Arup K |
| author_sort | Shekhar, Karthik |
| collection | MIT |
| description | Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of nonequilibrium viral evolution driven by patient-specific immune responses and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses. |
| first_indexed | 2024-09-23T15:06:04Z |
| format | Article |
| id | mit-1721.1/85205 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:06:04Z |
| publishDate | 2014 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/852052022-10-02T00:36:03Z Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes Shekhar, Karthik Ruberman, Claire Ferguson, Andrew L. Kardar, Mehran Barton, John P. Chakraborty, Arup K Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Chemical Engineering Massachusetts Institute of Technology. Department of Physics Ragon Institute of MGH, MIT and Harvard Shekhar, Karthik Barton, John P. Kardar, Mehran Chakraborty, Arup K. Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of nonequilibrium viral evolution driven by patient-specific immune responses and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses. Massachusetts Institute of Technology. Ragon Institute of MGH, MIT and Harvard National Institutes of Health (U.S.) (Director's Pioneer Award) Poitras Foundation (Pre-Doctoral Fellowship) 2014-03-03T13:50:21Z 2014-03-03T13:50:21Z 2013-12 2013-06 Article http://purl.org/eprint/type/JournalArticle 1539-3755 1550-2376 http://hdl.handle.net/1721.1/85205 Shekhar, Karthik, Claire Ruberman, Andrew Ferguson, John Barton, Mehran Kardar, and Arup Chakraborty. “Spin Models Inferred from Patient-Derived Viral Sequence Data Faithfully Describe HIV Fitness Landscapes.” Phys. Rev. E 88, no. 6 (December 2013). © 2013 American Physical Society https://orcid.org/0000-0003-1467-421X https://orcid.org/0000-0003-1268-9602 https://orcid.org/0000-0002-1112-5912 en_US http://dx.doi.org/10.1103/PhysRevE.88.062705 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 American Physical Society |
| spellingShingle | Shekhar, Karthik Ruberman, Claire Ferguson, Andrew L. Kardar, Mehran Barton, John P. Chakraborty, Arup K Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title | Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title_full | Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title_fullStr | Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title_full_unstemmed | Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title_short | Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes |
| title_sort | spin models inferred from patient derived viral sequence data faithfully describe hiv fitness landscapes |
| url | http://hdl.handle.net/1721.1/85205 https://orcid.org/0000-0003-1467-421X https://orcid.org/0000-0003-1268-9602 https://orcid.org/0000-0002-1112-5912 |
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