Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features.
The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amin...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC6459550?pdf=render |
| _version_ | 1818097387771527168 |
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| author | Craig A Magaret David C Benkeser Brian D Williamson Bhavesh R Borate Lindsay N Carpp Ivelin S Georgiev Ian Setliff Adam S Dingens Noah Simon Marco Carone Christopher Simpkins David Montefiori Galit Alter Wen-Han Yu Michal Juraska Paul T Edlefsen Shelly Karuna Nyaradzo M Mgodi Srilatha Edugupanti Peter B Gilbert |
| author_facet | Craig A Magaret David C Benkeser Brian D Williamson Bhavesh R Borate Lindsay N Carpp Ivelin S Georgiev Ian Setliff Adam S Dingens Noah Simon Marco Carone Christopher Simpkins David Montefiori Galit Alter Wen-Han Yu Michal Juraska Paul T Edlefsen Shelly Karuna Nyaradzo M Mgodi Srilatha Edugupanti Peter B Gilbert |
| author_sort | Craig A Magaret |
| collection | DOAJ |
| description | The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons. |
| first_indexed | 2024-12-10T23:19:43Z |
| format | Article |
| id | doaj.art-3019dcf47cb24cdaa070a934c89ffc44 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T23:19:43Z |
| publishDate | 2019-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-3019dcf47cb24cdaa070a934c89ffc442022-12-22T01:29:44ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-04-01154e100695210.1371/journal.pcbi.1006952Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features.Craig A MagaretDavid C BenkeserBrian D WilliamsonBhavesh R BorateLindsay N CarppIvelin S GeorgievIan SetliffAdam S DingensNoah SimonMarco CaroneChristopher SimpkinsDavid MontefioriGalit AlterWen-Han YuMichal JuraskaPaul T EdlefsenShelly KarunaNyaradzo M MgodiSrilatha EdugupantiPeter B GilbertThe broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.http://europepmc.org/articles/PMC6459550?pdf=render |
| spellingShingle | Craig A Magaret David C Benkeser Brian D Williamson Bhavesh R Borate Lindsay N Carpp Ivelin S Georgiev Ian Setliff Adam S Dingens Noah Simon Marco Carone Christopher Simpkins David Montefiori Galit Alter Wen-Han Yu Michal Juraska Paul T Edlefsen Shelly Karuna Nyaradzo M Mgodi Srilatha Edugupanti Peter B Gilbert Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. PLoS Computational Biology |
| title | Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. |
| title_full | Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. |
| title_fullStr | Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. |
| title_full_unstemmed | Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. |
| title_short | Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features. |
| title_sort | prediction of vrc01 neutralization sensitivity by hiv 1 gp160 sequence features |
| url | http://europepmc.org/articles/PMC6459550?pdf=render |
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