HIV-1 neutralizing antibodies elicited in humans by a prefusion-stabilized envelope trimer form a reproducible class targeting fusion peptide

Wang, Shuishu; Matassoli, Flávio Lemos; Zhang, Baoshan; Liu, Tracy; Shen, Chen‐Hsiang; Bylund, Tatsiana; Johnston, Timothy S.; Henry, Amy R.; Teng, I-Ting; Tripathi, Prabhanshu; Becker, Jürgen; Changela, Anita; Chaudhary, R; Cheng, Cheng; Gaudinski, Martin R.; Gorman, Jason; Harris, Darcy R.; Lee, Myungjin; Morano, Nicholas C.; Novik, Laura; O’Dell, Sijy; Olia, Adam S.; Parchment, Danealle K.; Rawi, Reda; Roberts-Torres, Jesmine; Stephens, Tyler; Tsybovsky, Yaroslav; Wang, Danyi; Wazer, David J. Van; Zhou, Tongqing; Doria‐Rose, Nicole A.; Koup, Richard A.; Shapiro, Lawrence; Douek, Daniel C.; McDermott, Adrian B.; Kwong, Peter D.

doi:10.1016/j.celrep.2023.112755

Cited by 8 publications

(5 citation statements)

References 73 publications

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“…To confirm the epitope and explore the binding mode of bNAb4251, we decided to perform cryo-electron microscopy (cryo-EM) of the antigen-binding fragment (Fab)4251 in complex with the soluble native-like trimer (BG505 DS-SOSIP) 51 . After several rounds of 2D and 3D classifications ( Extended Data Fig 9 ), we could segregate trimers with zero, or one Fab attached and solved the structure of the complex at the resolution of 3.7Å ( Figure 5a and, Supplementary Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

RAIN: a Machine Learning-based identification for HIV-1 bNAbs

Perez,

Foglierini

2024

Preprint

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Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infection. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoire is still lacking. Here, we developed a straightforward computational method for Rapid Automatic Identification of bNAbs (RAIN) based on Machine Learning methods. In contrast to other approaches using one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of novel HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

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Section: Resultsmentioning

confidence: 99%

RAIN: a Machine Learning-based identification for HIV-1 bNAbs

Perez,

Foglierini

2024

Preprint

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“…We identified that both Fabs bound to the soluble trimers in a manner similar to CD4bs-directed bNAbs, approaching the gp120 protomers from the side. To understand the molecular mechanism of the broad neutralization capacity by bNAb4251, we decided to perform cryo-EM of the Fab4251 in complex with the soluble native-like trimer BG505 DS-SOSIP 55 . After several rounds of 2D and 3D classification (Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

RAIN: machine learning-based identification for HIV-1 bNAbs

Foglierini,

Nortier,

Schelling

et al. 2024

Nat Commun

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Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

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“…Three doses of immunization with the same immunogen in humans yield autologous neutralizing antibodies that target the fusion‐peptide site of vulnerability. [ 10 , 11 ] Short‐term immunization of BG505 DS‐SOSIP seems to direct most of the antibody response to the glycan‐free base region, partially due to the inaccessibility of broadly neutralizing epitopes covered by surface glycans. It is possible that initially elicited, base‐targeting non‐neutralizing antibodies cover the highly immunogenic glycan‐free base, allowing antibody responses against neutralizing epitopes to develop after extended periods of immunizations.…”

Section: Discussionmentioning

confidence: 99%

“…[ 10 ] From one donor, by sorting B cells that can bind to glycan‐base covered BG505 trimer, we identified autologous neutralizing antibodies against the fusion‐peptide site of vulnerability. [ 11 ] In this study, we explore the ability of BG505 DS‐SOSIP to elicit a neutralizing response in llama by repetitive immunizations and isolate broadly neutralizing nanobodies using Env trimer and subdomains of trimer. We further explore generalizable strategies for nanobody improvement and combine the most potent nanobodies with CAP256V2LS to create ultrapotent human‐llama bispecific antibodies against HIV‐1.…”

Section: Introductionmentioning

confidence: 99%

Ultrapotent Broadly Neutralizing Human‐llama Bispecific Antibodies against HIV‐1

Xu,

Zhou,

McKee

et al. 2024

Advanced Science

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Broadly neutralizing antibodies are proposed as therapeutic and prophylactic agents against HIV‐1, but their potency and breadth are less than optimal. This study describes the immunization of a llama with the prefusion‐stabilized HIV‐1 envelope (Env) trimer, BG505 DS‐SOSIP, and the identification and improvement of potent neutralizing nanobodies recognizing the CD4‐binding site (CD4bs) of vulnerability. Two of the vaccine‐elicited CD4bs‐targeting nanobodies, G36 and R27, when engineered into a triple tandem format with llama IgG2a‐hinge region and human IgG1‐constant region (G36×3‐IgG2a and R27×3‐IgG2a), neutralized 96% of a multiclade 208‐strain panel at geometric mean IC80s of 0.314 and 0.033 µg mL−1, respectively. Cryo‐EM structures of these nanobodies in complex with Env trimer revealed the two nanobodies to neutralize HIV‐1 by mimicking the recognition of the CD4 receptor. To enhance their neutralizing potency and breadth, nanobodies are linked to the light chain of the V2‐apex‐targeting broadly neutralizing antibody, CAP256V2LS. The resultant human‐llama bispecific antibody CAP256L‐R27×3LS exhibited ultrapotent neutralization and breadth exceeding other published HIV‐1 broadly neutralizing antibodies, with pharmacokinetics determined in FcRn‐Fc mice similar to the parent CAP256V2LS. Vaccine‐elicited llama nanobodies, when combined with V2‐apex broadly neutralizing antibodies, may therefore be able to fulfill anti‐HIV‐1 therapeutic and prophylactic clinical goals.

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HIV-1 neutralizing antibodies elicited in humans by a prefusion-stabilized envelope trimer form a reproducible class targeting fusion peptide

Cited by 8 publications

References 73 publications

RAIN: a Machine Learning-based identification for HIV-1 bNAbs

RAIN: a Machine Learning-based identification for HIV-1 bNAbs

RAIN: machine learning-based identification for HIV-1 bNAbs

Ultrapotent Broadly Neutralizing Human‐llama Bispecific Antibodies against HIV‐1

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