Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates
<div><p>Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, vira...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , , , , , , , |
| Published: |
2019
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513513982001152 |
|---|---|
| author | Reda Rawi (391865) |
| author2 | Raghvendra Mall (581171) Chen-Hsiang Shen (1577383) S. Katie Farney (6806957) Andrea Shiakolas (18618385) Jing Zhou (168494) Halima Bensmail (10400) Tae-Wook Chun (73430) Nicole A. Doria-Rose (8026886) Rebecca M. Lynch (11434669) John R. Mascola (229761) Peter D. Kwong (161423) Gwo-Yu Chuang (758832) |
| author2_role | author author author author author author author author author author author author |
| author_facet | Reda Rawi (391865) Raghvendra Mall (581171) Chen-Hsiang Shen (1577383) S. Katie Farney (6806957) Andrea Shiakolas (18618385) Jing Zhou (168494) Halima Bensmail (10400) Tae-Wook Chun (73430) Nicole A. Doria-Rose (8026886) Rebecca M. Lynch (11434669) John R. Mascola (229761) Peter D. Kwong (161423) Gwo-Yu Chuang (758832) |
| author_role | author |
| dc.creator.none.fl_str_mv | Reda Rawi (391865) Raghvendra Mall (581171) Chen-Hsiang Shen (1577383) S. Katie Farney (6806957) Andrea Shiakolas (18618385) Jing Zhou (168494) Halima Bensmail (10400) Tae-Wook Chun (73430) Nicole A. Doria-Rose (8026886) Rebecca M. Lynch (11434669) John R. Mascola (229761) Peter D. Kwong (161423) Gwo-Yu Chuang (758832) |
| dc.date.none.fl_str_mv | 2019-10-11T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-019-50635-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Accurate_Prediction_for_Antibody_Resistance_of_Clinical_HIV-1_Isolates/25907239 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Information and computing sciences Machine learning HIV-1 Envelope glycoprotein (Env) Prevention Treatment Clinical trials Viral isolates Resistance Machine learning |
| dc.title.none.fl_str_mv | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine – a tree-based machine learning method – enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Scientific Reports<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-019-50635-w" target="_blank">https://dx.doi.org/10.1038/s41598-019-50635-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_26d5d10c15d890b20bc576699aefbd71 |
| identifier_str_mv | 10.1038/s41598-019-50635-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25907239 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Accurate Prediction for Antibody Resistance of Clinical HIV-1 IsolatesReda Rawi (391865)Raghvendra Mall (581171)Chen-Hsiang Shen (1577383)S. Katie Farney (6806957)Andrea Shiakolas (18618385)Jing Zhou (168494)Halima Bensmail (10400)Tae-Wook Chun (73430)Nicole A. Doria-Rose (8026886)Rebecca M. Lynch (11434669)John R. Mascola (229761)Peter D. Kwong (161423)Gwo-Yu Chuang (758832)Biomedical and clinical sciencesClinical sciencesInformation and computing sciencesMachine learningHIV-1Envelope glycoprotein (Env)PreventionTreatmentClinical trialsViral isolatesResistanceMachine learning<div><p>Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to specific bNAbs. Currently, resistant isolates are commonly identified by time-consuming and expensive in vitro neutralization assays. Here, we report machine learning classifiers that accurately predict resistance of HIV-1 isolates to 33 bNAbs. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of gradient boosting machine – a tree-based machine learning method – enabled us to identify critical features, which had high accordance with epitope residues that distinguished between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor should facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Scientific Reports<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-019-50635-w" target="_blank">https://dx.doi.org/10.1038/s41598-019-50635-w</a></p>2019-10-11T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-019-50635-whttps://figshare.com/articles/journal_contribution/Accurate_Prediction_for_Antibody_Resistance_of_Clinical_HIV-1_Isolates/25907239CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259072392019-10-11T03:00:00Z |
| spellingShingle | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates Reda Rawi (391865) Biomedical and clinical sciences Clinical sciences Information and computing sciences Machine learning HIV-1 Envelope glycoprotein (Env) Prevention Treatment Clinical trials Viral isolates Resistance Machine learning |
| status_str | publishedVersion |
| title | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| title_full | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| title_fullStr | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| title_full_unstemmed | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| title_short | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| title_sort | Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates |
| topic | Biomedical and clinical sciences Clinical sciences Information and computing sciences Machine learning HIV-1 Envelope glycoprotein (Env) Prevention Treatment Clinical trials Viral isolates Resistance Machine learning |