RAIN: machine learning-based identification for HIV-1 bNAbs.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 Jun 2024
24 Jun 2024
Historique:
received:
29
02
2024
accepted:
17
06
2024
medline:
25
6
2024
pubmed:
25
6
2024
entrez:
24
6
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38914562
doi: 10.1038/s41467-024-49676-1
pii: 10.1038/s41467-024-49676-1
doi:
Substances chimiques
HIV Antibodies
0
Antibodies, Neutralizing
0
CD4 Antigens
0
HIV Envelope Protein gp120
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5339Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 310030_20467
Informations de copyright
© 2024. The Author(s).
Références
Landovitz, R. J., Scott, H. & Deeks, S. G. Prevention, treatment and cure of HIV infection. Nat. Rev. Microbiol. 21, 657–670 (2023).
pubmed: 37344551
doi: 10.1038/s41579-023-00914-1
Haynes, B. F. & Burton, D. R. Developing an HIV vaccine. Science 355, 1129–1130 (2017).
pubmed: 28302812
pmcid: 5569908
doi: 10.1126/science.aan0662
Sok, D. & Burton, D. R. Recent progress in broadly neutralizing antibodies to HIV. Nat. Immunol. 19, 1179–1188 (2018).
pubmed: 30333615
pmcid: 6440471
doi: 10.1038/s41590-018-0235-7
Bailey, J., Blankson, J. N., Wind-Rotolo, M. & Siliciano, R. F. Mechanisms of HIV-1 escape from immune responses and antiretroviral drugs. Curr. Opin. Immunol. 16, 470–476 (2004).
pubmed: 15245741
doi: 10.1016/j.coi.2004.05.005
Malim, M. H. & Emerman, M. HIV-1 sequence variation: drift, shift, and attenuation. Cell 104, 469–472 (2001).
pubmed: 11239404
doi: 10.1016/S0092-8674(01)00234-3
Liao, H. X. et al. Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496, 469–476 (2013).
pubmed: 23552890
pmcid: 3637846
doi: 10.1038/nature12053
Zhou, T. & Xu, K. Structural features of broadly neutralizing antibodies and rational design of vaccine. Adv. Exp. Med. Biol. 1075, 73–95 (2018).
pubmed: 30030790
doi: 10.1007/978-981-13-0484-2_4
Roskin, K. M. et al. Aberrant B cell repertoire selection associated with HIV neutralizing antibody breadth. Nat. Immunol. 21, 199–209 (2020).
pubmed: 31959979
pmcid: 7223457
doi: 10.1038/s41590-019-0581-0
Pantaleo, G., Correia, B., Fenwick, C., Joo, V. S. & Perez, L. Antibodies to combat viral infections: development strategies and progress. Nat. Rev. Drug Discov. 21, 676–696 (2022).
pubmed: 35725925
pmcid: 9207876
doi: 10.1038/s41573-022-00495-3
Shingai, M. et al. Antibody-mediated immunotherapy of macaques chronically infected with SHIV suppresses viraemia. Nature 503, 277–280 (2013).
pubmed: 24172896
pmcid: 4133787
doi: 10.1038/nature12746
Barouch, D. H. et al. Therapeutic efficacy of potent neutralizing HIV-1-specific monoclonal antibodies in SHIV-infected rhesus monkeys. Nature 503, 224–228 (2013).
pubmed: 24172905
pmcid: 4017780
doi: 10.1038/nature12744
Parsons, M. S. et al. Partial efficacy of a broadly neutralizing antibody against cell-associated SHIV infection. Sci. Transl. Med. 9, eaaf1483 (2017).
pubmed: 28794282
doi: 10.1126/scitranslmed.aaf1483
Gautam, R. et al. A single injection of anti-HIV-1 antibodies protects against repeated SHIV challenges. Nature 533, 105–109 (2016).
pubmed: 27120156
pmcid: 5127204
doi: 10.1038/nature17677
Halper-Stromberg, A. et al. Broadly neutralizing antibodies and viral inducers decrease rebound from HIV-1 latent reservoirs in humanized mice. Cell 158, 989–999 (2014).
pubmed: 25131989
pmcid: 4163911
doi: 10.1016/j.cell.2014.07.043
Caskey, M., Klein, F. & Nussenzweig, M. C. Broadly neutralizing anti-HIV-1 monoclonal antibodies in the clinic. Nat. Med 25, 547–553 (2019).
pubmed: 30936546
pmcid: 7322694
doi: 10.1038/s41591-019-0412-8
Mendoza, P. et al. Combination therapy with anti-HIV-1 antibodies maintains viral suppression. Nature 561, 479–484 (2018).
pubmed: 30258136
pmcid: 6166473
doi: 10.1038/s41586-018-0531-2
Gaebler, C. et al. Prolonged viral suppression with anti-HIV-1 antibody therapy. Nature 606, 368–374 (2022).
pubmed: 35418681
pmcid: 9177424
doi: 10.1038/s41586-022-04597-1
McCoy, L. E. The expanding array of HIV broadly neutralizing antibodies. Retrovirology 15, 70 (2018).
pubmed: 30326938
pmcid: 6192334
doi: 10.1186/s12977-018-0453-y
Krebs, S. J. et al. Longitudinal analysis reveals early development of three MPER-directed neutralizing antibody lineages from an HIV-1-infected individual. Immunity 50, 677–691.e613 (2019).
pubmed: 30876875
pmcid: 6555550
doi: 10.1016/j.immuni.2019.02.008
Schriek, A. I., Aldon, Y. L. T., van Gils, M. J. & de Taeye, S. W. Next-generation bNAbs for HIV-1 cure strategies. Antivir. Res. 222, 105788 (2023).
pubmed: 38158130
doi: 10.1016/j.antiviral.2023.105788
Mahomed, S., Garrett, N., Baxter, C., Abdool Karim, Q. & Abdool Karim, S. S. Clinical trials of broadly neutralizing monoclonal antibodies for human immunodeficiency virus prevention: a review. J. Infect. Dis. 223, 370–380 (2021).
pubmed: 32604408
doi: 10.1093/infdis/jiaa377
Sneller, M. C. et al. Combination anti-HIV antibodies provide sustained virological suppression. Nature 606, 375–381 (2022).
pubmed: 35650437
pmcid: 11059968
doi: 10.1038/s41586-022-04797-9
Karuna, S. T. & Corey, L. Broadly neutralizing antibodies for HIV prevention. Annu Rev. Med 71, 329–346 (2020).
pubmed: 31986089
doi: 10.1146/annurev-med-110118-045506
Marks, C. & Deane, C. M. How repertoire data are changing antibody science. J. Biol. Chem. 295, 9823–9837 (2020).
pubmed: 32409582
pmcid: 7380193
doi: 10.1074/jbc.REV120.010181
Kim, J., McFee, M., Fang, Q., Abdin, O. & Kim, P. M. Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol. Sci. 44, 175–189 (2023).
pubmed: 36669976
doi: 10.1016/j.tips.2022.12.005
Akbar, R. et al. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs 14, 2008790 (2022).
pubmed: 35293269
pmcid: 8928824
doi: 10.1080/19420862.2021.2008790
Scheid, J. F. et al. Sequence and structural convergence of broad and potent HIV antibodies that mimic CD4 binding. Science 333, 1633–1637 (2011).
pubmed: 21764753
pmcid: 3351836
doi: 10.1126/science.1207227
West, A. P. Jr., Diskin, R., Nussenzweig, M. C. & Bjorkman, P. J. Structural basis for germ-line gene usage of a potent class of antibodies targeting the CD4-binding site of HIV-1 gp120. Proc. Natl. Acad. Sci. USA 109, E2083–E2090 (2012).
pubmed: 22745174
pmcid: 3409792
doi: 10.1073/pnas.1208984109
Jardine, J. G. et al. HIV-1 VACCINES. Priming a broadly neutralizing antibody response to HIV-1 using a germline-targeting immunogen. Science 349, 156–161 (2015).
pubmed: 26089355
pmcid: 4669217
doi: 10.1126/science.aac5894
Liao, H. et al. Contribution of V(H) replacement products to the generation of anti-HIV antibodies. Clin. Immunol. 146, 46–55 (2013).
pubmed: 23220404
doi: 10.1016/j.clim.2012.11.003
Willis, J. R. et al. Human immunoglobulin repertoire analysis guides design of vaccine priming immunogens targeting HIV V2-apex broadly neutralizing antibody precursors. Immunity 55, 2149–2167.e2149 (2022).
pubmed: 36179689
pmcid: 9671094
doi: 10.1016/j.immuni.2022.09.001
Yoon, H. et al. CATNAP: a tool to compile, analyze and tally neutralizing antibody panels. Nucleic Acids Res. 43, W213–W219 (2015).
pubmed: 26044712
pmcid: 4489231
doi: 10.1093/nar/gkv404
Dunbar, J. & Deane, C. M. ANARCI: antigen receptor numbering and receptor classification. Bioinformatics 32, 298–300 (2016).
pubmed: 26424857
doi: 10.1093/bioinformatics/btv552
Shen, C. H. et al. VRC34-antibody lineage development reveals how a required rare mutation shapes the maturation of a broad HIV-neutralizing lineage. Cell Host Microbe 27, 531–543.e536 (2020).
pubmed: 32130953
pmcid: 7467872
doi: 10.1016/j.chom.2020.01.027
Wiehe, K. et al. Functional relevance of improbable antibody mutations for HIV broadly neutralizing antibody development. Cell Host Microbe 23, 759–765.e756 (2018).
pubmed: 29861171
pmcid: 6002614
doi: 10.1016/j.chom.2018.04.018
Ye, J., Ma, N., Madden, T. L. & Ostell, J. M. IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res. 41, W34–W40 (2013).
pubmed: 23671333
pmcid: 3692102
doi: 10.1093/nar/gkt382
Nouri, N. & Kleinstein, S. H. A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data. Bioinformatics 34, i341–i349 (2018).
pubmed: 29949968
pmcid: 6022594
doi: 10.1093/bioinformatics/bty235
Foglierini, M., Pappas, L., Lanzavecchia, A., Corti, D. & Perez, L. AncesTree: an interactive immunoglobulin lineage tree visualizer. PLoS Comput. Biol. 16, e1007731 (2020).
pubmed: 32649725
pmcid: 7375605
doi: 10.1371/journal.pcbi.1007731
Phad, G. E. et al. Clonal structure, stability and dynamics of human memory B cells and circulating plasmablasts. Nat. Immunol. 23, 1076–1085 (2022).
pubmed: 35761085
pmcid: 9276532
doi: 10.1038/s41590-022-01230-1
Steinwart, I., Hush, D. & Scovel, C. A classification framework for anomaly detection. J. Mach. Learn. Res. 6, 211–232 (2005).
Zhou, T. et al. Multidonor analysis reveals structural elements, genetic determinants, and maturation pathway for HIV-1 neutralization by VRC01-class antibodies. Immunity 39, 245–258 (2013).
pubmed: 23911655
pmcid: 3985390
doi: 10.1016/j.immuni.2013.04.012
Gorny, M. K. et al. Preferential use of the VH5-51 gene segment by the human immune response to code for antibodies against the V3 domain of HIV-1. Mol. Immunol. 46, 917–926 (2009).
pubmed: 18952295
doi: 10.1016/j.molimm.2008.09.005
van der Laan M. J., Polley E. C., Hubbard A. E. Super Learner. Stat. Appl. Genet. Mol. Biol. https://doi.org/10.2202/1544-6115.1309 (2007).
deCamp, A. et al. Global panel of HIV-1 Env reference strains for standardized assessments of vaccine-elicited neutralizing antibodies. J. Virol. 88, 2489–2507 (2014).
pubmed: 24352443
pmcid: 3958090
doi: 10.1128/JVI.02853-13
Schommers, P. et al. Restriction of HIV-1 escape by a highly broad and potent neutralizing antibody. Cell 180, 471–489.e422 (2020).
pubmed: 32004464
pmcid: 7042716
doi: 10.1016/j.cell.2020.01.010
Horns, F., Dekker, C. L. & Quake, S. R. Memory B cell activation, broad anti-influenza antibodies, and bystander activation revealed by single-cell transcriptomics. Cell Rep. 30, 905–913.e906 (2020).
pubmed: 31968262
pmcid: 7891556
doi: 10.1016/j.celrep.2019.12.063
Chuang, G.-Y. et al. Structure-based design of a soluble prefusion-closed HIV-1 Env trimer with reduced CD4 affinity and improved immunogenicity. J. Virol. 91, e02268–16 (2017).
pubmed: 28275193
pmcid: 5411596
doi: 10.1128/JVI.02268-16
Gulla, K. et al. A non-affinity purification process for GMP production of prefusion-closed HIV-1 envelope trimers from clades A and C for clinical evaluation. Vaccine 39, 3379–3387 (2021).
pubmed: 34020817
pmcid: 8243839
doi: 10.1016/j.vaccine.2021.04.063
Sanders, R. W. et al. A next-generation cleaved, soluble HIV-1 Env trimer, BG505 SOSIP.664 gp140, expresses multiple epitopes for broadly neutralizing but not non-neutralizing antibodies. PLoS Pathog. 9, e1003618 (2013).
pubmed: 24068931
pmcid: 3777863
doi: 10.1371/journal.ppat.1003618
Kwon, Y. D. et al. A matrix of structure-based designs yields improved VRC01-class antibodies for HIV-1 therapy and prevention. mAbs 13, 1946918 (2021).
pubmed: 34328065
pmcid: 8331036
doi: 10.1080/19420862.2021.1946918
Zhou, T. et al. Structural basis for broad and potent neutralization of HIV-1 by antibody VRC01. Science 329, 811–817 (2010).
pubmed: 20616231
pmcid: 2981354
doi: 10.1126/science.1192819
Charles, T. P. et al. The C3/465 glycan hole cluster in BG505 HIV-1 envelope is the major neutralizing target involved in preventing mucosal SHIV infection. PLoS Pathog. 17, e1009257 (2021).
pubmed: 33556148
pmcid: 7895394
doi: 10.1371/journal.ppat.1009257
Bianchi, M. et al. Electron-microscopy-based epitope mapping defines specificities of polyclonal antibodies elicited during HIV-1 BG505 envelope trimer immunization. Immunity 49, 288–300.e288 (2018).
pubmed: 30097292
pmcid: 6104742
doi: 10.1016/j.immuni.2018.07.009
Guenaga, J. et al. Well-ordered trimeric HIV-1 subtype B and C soluble spike mimetics generated by negative selection display native-like properties. PLoS Pathog. 11, e1004570 (2015).
pubmed: 25569572
pmcid: 4287557
doi: 10.1371/journal.ppat.1004570
Wang, S. et al. HIV-1 neutralizing antibodies elicited in humans by a prefusion-stabilized envelope trimer form a reproducible class targeting fusion peptide. Cell Rep. 42, 112755 (2023).
pubmed: 37436899
pmcid: 10491024
doi: 10.1016/j.celrep.2023.112755
Li, W. et al. HIV-1 Env trimers asymmetrically engage CD4 receptors in membranes. Nature 623, 1026–1033 (2023).
pubmed: 37993716
pmcid: 10686830
doi: 10.1038/s41586-023-06762-6
Zhou, T. et al. Structural definition of a conserved neutralization epitope on HIV-1 gp120. Nature 445, 732–737 (2007).
pubmed: 17301785
pmcid: 2584968
doi: 10.1038/nature05580
Kwong, P. D. et al. HIV-1 evades antibody-mediated neutralization through conformational masking of receptor-binding sites. Nature 420, 678–682 (2002).
pubmed: 12478295
doi: 10.1038/nature01188
Zhou, T. et al. Structural repertoire of HIV-1-neutralizing antibodies targeting the CD4 supersite in 14 donors. Cell 161, 1280–1292 (2015).
pubmed: 26004070
pmcid: 4683157
doi: 10.1016/j.cell.2015.05.007
Irvine, E. B. & Reddy, S. T. Advancing antibody engineering through synthetic evolution and machine learning. J. Immunol. 212, 235–243 (2024).
pubmed: 38166249
doi: 10.4049/jimmunol.2300492
Xiao, Z. X., Miller, J. S. & Zheng, S. G. An updated advance of autoantibodies in autoimmune diseases. Autoimmun. Rev. 20, 102743 (2021).
pubmed: 33333232
doi: 10.1016/j.autrev.2020.102743
Wang, M., Patsenker, J., Li, H., Kluger, Y. & Kleinstein, S. H. Language model-based B cell receptor sequence embeddings can effectively encode receptor specificity. Nucleic Acids Res. 52, 548–557 (2024).
pubmed: 38109302
doi: 10.1093/nar/gkad1128
Burbach, S. M. & Briney, B. Improving antibody language models with native pairing. Preprint at https://arxiv.org/abs/2308.14300 (2023).
Bozhanova, N. G. et al. Computational identification of HCV neutralizing antibodies with a common HCDR3 disulfide bond motif in the antibody repertoires of infected individuals. Nat. Commun. 13, 3178 (2022).
pubmed: 35676279
pmcid: 9177688
doi: 10.1038/s41467-022-30865-9
Schneider, C., Buchanan, A., Taddese, B. & Deane, C. M. DLAB: deep learning methods for structure-based virtual screening of antibodies. Bioinformatics 38, 377–383 (2022).
pubmed: 34546288
doi: 10.1093/bioinformatics/btab660
Hummer, A. M., Abanades, B. & Deane, C. M. Advances in computational structure-based antibody design. Curr. Opin. Struct. Biol. 74, 102379 (2022).
pubmed: 35490649
doi: 10.1016/j.sbi.2022.102379
Klein, F. et al. Somatic mutations of the immunoglobulin framework are generally required for broad and potent HIV-1 neutralization. Cell 153, 126–138 (2013).
pubmed: 23540694
pmcid: 3792590
doi: 10.1016/j.cell.2013.03.018
Bonsignori, M. et al. Antibody-virus co-evolution in HIV infection: paths for HIV vaccine development. Immunol. Rev. 275, 145–160 (2017).
pubmed: 28133802
pmcid: 5302796
doi: 10.1111/imr.12509
Karlsson Hedestam, G. B., Guenaga, J., Corcoran, M. & Wyatt, R. T. Evolution of B cell analysis and Env trimer redesign. Immunol. Rev. 275, 183–202 (2017).
pubmed: 28133805
pmcid: 5301504
doi: 10.1111/imr.12515
Perez, L.-H. et al. Direct bacterial killing in vitro by recombinant Nod2 is compromised by Crohn’s disease-associated mutations. PLoS ONE 5, e10915 (2010).
pubmed: 20531959
pmcid: 2879363
doi: 10.1371/journal.pone.0010915
De Domenico, E. et al. Optimized workflow for single-cell transcriptomics on infectious diseases including COVID-19. STAR Protoc. 1, 100233 (2020).
pubmed: 33377120
pmcid: 7757730
doi: 10.1016/j.xpro.2020.100233
Dura, B. et al. scFTD-seq: freeze-thaw lysis based, portable approach toward highly distributed single-cell 3’ mRNA profiling. Nucleic Acids Res. 47, e16 (2019).
pubmed: 30462277
doi: 10.1093/nar/gky1173
Perotti, M., Marcandalli, J., Demurtas, D., Sallusto, F. & Perez, L. Rationally designed human cytomegalovirus gB nanoparticle vaccine with improved immunogenicity. PLoS Pathog. 16, e1009169 (2021).
doi: 10.1371/journal.ppat.1009169
Kschonsak, M. et al. Structural basis for HCMV Pentamer receptor recognition and antibody neutralization. Sci. Adv. 8, eabm2536 (2022).
pubmed: 35275719
pmcid: 8916737
doi: 10.1126/sciadv.abm2536
Kwon, Y. D. et al. Crystal structure, conformational fixation and entry-related interactions of mature ligand-free HIV-1 Env. Nat. Struct. Mol. Biol. 22, 522–531 (2015).
pubmed: 26098315
pmcid: 4706170
doi: 10.1038/nsmb.3051
Kong, R. et al. Antibody lineages with vaccine-induced antigen-binding hotspots develop broad HIV neutralization. Cell 178, 567–584.e519 (2019).
pubmed: 31348886
pmcid: 6755680
doi: 10.1016/j.cell.2019.06.030
Shu, Y. et al. Efficient protein boosting after plasmid DNA or recombinant adenovirus immunization with HIV-1 vaccine constructs. Vaccine 25, 1398–1408 (2007).
pubmed: 17113201
doi: 10.1016/j.vaccine.2006.10.046
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
pubmed: 28165473
doi: 10.1038/nmeth.4169
Pancera, M. et al. Structure and immune recognition of trimeric pre-fusion HIV-1 Env. Nature 514, 455–461 (2014).
pubmed: 25296255
pmcid: 4348022
doi: 10.1038/nature13808
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. Sect. D. Biol. Crystallogr. 66, 486–501 (2010).
doi: 10.1107/S0907444910007493
Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. Sect. D. Struct. Biol. 75, 861–877 (2019).
doi: 10.1107/S2059798319011471
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
pubmed: 32881101
doi: 10.1002/pro.3943
Wu, T. T. & Kabat, E. A. An analysis of the sequences of the variable regions of Bence Jones proteins and myeloma light chains and their implications for antibody complementarity. J. Exp. Med. 132, 211–250 (1970).
pubmed: 5508247
pmcid: 2138737
doi: 10.1084/jem.132.2.211
Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
pubmed: 17681537
doi: 10.1016/j.jmb.2007.05.022
Dunbar, J. et al. SAbPred: a structure-based antibody prediction server. Nucleic Acids Res. 44, W474–W478 (2016).
pubmed: 27131379
pmcid: 4987913
doi: 10.1093/nar/gkw361
Patil, I. Visualizations with statistical details: the ’ggstatsplot’ approach. J. Open Source Softw. 6, 3167 (2021).
doi: 10.21105/joss.03167
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
pubmed: 27207943
doi: 10.1093/bioinformatics/btw313