Learning the language of antibody hypervariability Article Swipe
Rohit Singh
,
Chiho Im
,
Yu Qiu
,
Brian C. Mackness
,
Abhinav Gupta
,
Taylor Joren
,
Samuel Sledzieski
,
Lena Erlach
,
Maria Wendt
,
Yves Fomekong Nanfack
,
Bryan D. Bryson
,
Bonnie Berger
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1073/pnas.2418918121
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1073/pnas.2418918121
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose “foundational” PLMs have limited performance in modeling antibodies due to the latter’s hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP’s transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1073/pnas.2418918121
- OA Status
- green
- Cited By
- 16
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405909579
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405909579Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1073/pnas.2418918121Digital Object Identifier
- Title
-
Learning the language of antibody hypervariabilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-30Full publication date if available
- Authors
-
Rohit Singh, Chiho Im, Yu Qiu, Brian C. Mackness, Abhinav Gupta, Taylor Joren, Samuel Sledzieski, Lena Erlach, Maria Wendt, Yves Fomekong Nanfack, Bryan D. Bryson, Bonnie BergerList of authors in order
- Landing page
-
https://doi.org/10.1073/pnas.2418918121Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11725859Direct OA link when available
- Concepts
-
Paratope, Computational biology, Computer science, Antibody, Mutagenesis, Set (abstract data type), Artificial intelligence, Epitope, Biology, Mutation, Immunology, Genetics, Programming language, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405909579 |
|---|---|
| doi | https://doi.org/10.1073/pnas.2418918121 |
| ids.doi | https://doi.org/10.1073/pnas.2418918121 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39793083 |
| ids.openalex | https://openalex.org/W4405909579 |
| fwci | 13.10518109 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | Q000276 |
| mesh[1].descriptor_ui | D000086402 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | immunology |
| mesh[1].descriptor_name | SARS-CoV-2 |
| mesh[2].qualifier_ui | Q000276 |
| mesh[2].descriptor_ui | D000086382 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | immunology |
| mesh[2].descriptor_name | COVID-19 |
| mesh[3].qualifier_ui | Q000821 |
| mesh[3].descriptor_ui | D000086382 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | virology |
| mesh[3].descriptor_name | COVID-19 |
| mesh[4].qualifier_ui | Q000276 |
| mesh[4].descriptor_ui | D000914 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | immunology |
| mesh[4].descriptor_name | Antibodies, Viral |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D016296 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Mutagenesis |
| mesh[6].qualifier_ui | Q000276 |
| mesh[6].descriptor_ui | D000906 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | immunology |
| mesh[6].descriptor_name | Antibodies |
| mesh[7].qualifier_ui | Q000737 |
| mesh[7].descriptor_ui | D000906 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | chemistry |
| mesh[7].descriptor_name | Antibodies |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D006801 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Humans |
| mesh[9].qualifier_ui | Q000276 |
| mesh[9].descriptor_ui | D000086402 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | immunology |
| mesh[9].descriptor_name | SARS-CoV-2 |
| mesh[10].qualifier_ui | Q000276 |
| mesh[10].descriptor_ui | D000086382 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | immunology |
| mesh[10].descriptor_name | COVID-19 |
| mesh[11].qualifier_ui | Q000821 |
| mesh[11].descriptor_ui | D000086382 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | virology |
| mesh[11].descriptor_name | COVID-19 |
| mesh[12].qualifier_ui | Q000276 |
| mesh[12].descriptor_ui | D000914 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | immunology |
| mesh[12].descriptor_name | Antibodies, Viral |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D016296 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Mutagenesis |
| mesh[14].qualifier_ui | Q000276 |
| mesh[14].descriptor_ui | D000906 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | immunology |
| mesh[14].descriptor_name | Antibodies |
| mesh[15].qualifier_ui | Q000737 |
| mesh[15].descriptor_ui | D000906 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | chemistry |
| mesh[15].descriptor_name | Antibodies |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D006801 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Humans |
| mesh[17].qualifier_ui | Q000276 |
| mesh[17].descriptor_ui | D000086402 |
| mesh[17].is_major_topic | True |
| mesh[17].qualifier_name | immunology |
| mesh[17].descriptor_name | SARS-CoV-2 |
| mesh[18].qualifier_ui | Q000276 |
| mesh[18].descriptor_ui | D000086382 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | immunology |
| mesh[18].descriptor_name | COVID-19 |
| mesh[19].qualifier_ui | Q000821 |
| mesh[19].descriptor_ui | D000086382 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | virology |
| mesh[19].descriptor_name | COVID-19 |
| mesh[20].qualifier_ui | Q000276 |
| mesh[20].descriptor_ui | D000914 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | immunology |
| mesh[20].descriptor_name | Antibodies, Viral |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D016296 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Mutagenesis |
| mesh[22].qualifier_ui | Q000276 |
| mesh[22].descriptor_ui | D000906 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | immunology |
| mesh[22].descriptor_name | Antibodies |
| mesh[23].qualifier_ui | Q000737 |
| mesh[23].descriptor_ui | D000906 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | chemistry |
| mesh[23].descriptor_name | Antibodies |
| type | article |
| title | Learning the language of antibody hypervariability |
| awards[0].id | https://openalex.org/G8009519932 |
| awards[0].funder_id | https://openalex.org/F4320307781 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2022 iDEA |
| awards[0].funder_display_name | Sanofi |
| awards[1].id | https://openalex.org/G8355252386 |
| awards[1].funder_id | https://openalex.org/F4320337354 |
| awards[1].display_name | |
| awards[1].funder_award_id | 1R35GM141861 |
| awards[1].funder_display_name | National Institute of General Medical Sciences |
| biblio.issue | 1 |
| biblio.volume | 122 |
| biblio.last_page | e2418918121 |
| biblio.first_page | e2418918121 |
| topics[0].id | https://openalex.org/T11016 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Monoclonal and Polyclonal Antibodies Research |
| topics[1].id | https://openalex.org/T12576 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9983000159263611 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1312 |
| topics[1].subfield.display_name | Molecular Biology |
| topics[1].display_name | vaccines and immunoinformatics approaches |
| topics[2].id | https://openalex.org/T10521 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9965000152587891 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | RNA and protein synthesis mechanisms |
| funders[0].id | https://openalex.org/F4320307781 |
| funders[0].ror | https://ror.org/027vj4x92 |
| funders[0].display_name | Sanofi |
| funders[1].id | https://openalex.org/F4320337354 |
| funders[1].ror | https://ror.org/04q48ey07 |
| funders[1].display_name | National Institute of General Medical Sciences |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C9086966 |
| concepts[0].level | 4 |
| concepts[0].score | 0.8064619302749634 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q489135 |
| concepts[0].display_name | Paratope |
| concepts[1].id | https://openalex.org/C70721500 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6548757553100586 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q177005 |
| concepts[1].display_name | Computational biology |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5734285712242126 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C159654299 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5389159321784973 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q79460 |
| concepts[3].display_name | Antibody |
| concepts[4].id | https://openalex.org/C16318435 |
| concepts[4].level | 4 |
| concepts[4].score | 0.43954554200172424 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q149299 |
| concepts[4].display_name | Mutagenesis |
| concepts[5].id | https://openalex.org/C177264268 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4155467748641968 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[5].display_name | Set (abstract data type) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.400208055973053 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C195616568 |
| concepts[7].level | 3 |
| concepts[7].score | 0.3323444724082947 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q128711 |
| concepts[7].display_name | Epitope |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.29746419191360474 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C501734568 |
| concepts[9].level | 3 |
| concepts[9].score | 0.2295204997062683 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q42918 |
| concepts[9].display_name | Mutation |
| concepts[10].id | https://openalex.org/C203014093 |
| concepts[10].level | 1 |
| concepts[10].score | 0.1992652416229248 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q101929 |
| concepts[10].display_name | Immunology |
| concepts[11].id | https://openalex.org/C54355233 |
| concepts[11].level | 1 |
| concepts[11].score | 0.16251307725906372 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[11].display_name | Genetics |
| concepts[12].id | https://openalex.org/C199360897 |
| concepts[12].level | 1 |
| concepts[12].score | 0.09709000587463379 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[12].display_name | Programming language |
| concepts[13].id | https://openalex.org/C104317684 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[13].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/paratope |
| keywords[0].score | 0.8064619302749634 |
| keywords[0].display_name | Paratope |
| keywords[1].id | https://openalex.org/keywords/computational-biology |
| keywords[1].score | 0.6548757553100586 |
| keywords[1].display_name | Computational biology |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5734285712242126 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/antibody |
| keywords[3].score | 0.5389159321784973 |
| keywords[3].display_name | Antibody |
| keywords[4].id | https://openalex.org/keywords/mutagenesis |
| keywords[4].score | 0.43954554200172424 |
| keywords[4].display_name | Mutagenesis |
| keywords[5].id | https://openalex.org/keywords/set |
| keywords[5].score | 0.4155467748641968 |
| keywords[5].display_name | Set (abstract data type) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.400208055973053 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/epitope |
| keywords[7].score | 0.3323444724082947 |
| keywords[7].display_name | Epitope |
| keywords[8].id | https://openalex.org/keywords/biology |
| keywords[8].score | 0.29746419191360474 |
| keywords[8].display_name | Biology |
| keywords[9].id | https://openalex.org/keywords/mutation |
| keywords[9].score | 0.2295204997062683 |
| keywords[9].display_name | Mutation |
| keywords[10].id | https://openalex.org/keywords/immunology |
| keywords[10].score | 0.1992652416229248 |
| keywords[10].display_name | Immunology |
| keywords[11].id | https://openalex.org/keywords/genetics |
| keywords[11].score | 0.16251307725906372 |
| keywords[11].display_name | Genetics |
| keywords[12].id | https://openalex.org/keywords/programming-language |
| keywords[12].score | 0.09709000587463379 |
| keywords[12].display_name | Programming language |
| language | en |
| locations[0].id | doi:10.1073/pnas.2418918121 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S125754415 |
| locations[0].source.issn | 0027-8424, 1091-6490 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0027-8424 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Proceedings of the National Academy of Sciences |
| locations[0].source.host_organization | https://openalex.org/P4310320052 |
| locations[0].source.host_organization_name | National Academy of Sciences |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320052 |
| locations[0].source.host_organization_lineage_names | National Academy of Sciences |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the National Academy of Sciences |
| locations[0].landing_page_url | https://doi.org/10.1073/pnas.2418918121 |
| locations[1].id | pmid:39793083 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Proceedings of the National Academy of Sciences of the United States of America |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39793083 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11725859 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Proc Natl Acad Sci U S A |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11725859 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5081688191 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4084-7340 |
| authorships[0].author.display_name | Rohit Singh |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[0].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[0].affiliations[1].raw_affiliation_string | Computer Science and Artificial Intelligence Laboratory |
| authorships[0].institutions[0].id | https://openalex.org/I63966007 |
| authorships[0].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Rohit Singh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology |
| authorships[1].author.id | https://openalex.org/A5014466580 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Chiho Im |
| authorships[1].countries | US |
| authorships[1].affiliations[0].raw_affiliation_string | Computer Science and Artificial Intelligence Laboratory |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I63966007 |
| authorships[1].affiliations[1].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[1].institutions[0].id | https://openalex.org/I63966007 |
| authorships[1].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chiho Im |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology |
| authorships[2].author.id | https://openalex.org/A5063031996 |
| authorships[2].author.orcid | https://orcid.org/0009-0005-5240-9144 |
| authorships[2].author.display_name | Yu Qiu |
| authorships[2].countries | FR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[2].affiliations[0].raw_affiliation_string | Sanofi R&D Large Molecule Research |
| authorships[2].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[2].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[2].institutions[0].type | company |
| authorships[2].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[2].institutions[0].country_code | FR |
| authorships[2].institutions[0].display_name | Sanofi (France) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yu Qiu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Sanofi R&D Large Molecule Research |
| authorships[3].author.id | https://openalex.org/A5036506264 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3587-5603 |
| authorships[3].author.display_name | Brian C. Mackness |
| authorships[3].countries | FR |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[3].affiliations[0].raw_affiliation_string | Sanofi R&D Large Molecule Research |
| authorships[3].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[3].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[3].institutions[0].country_code | FR |
| authorships[3].institutions[0].display_name | Sanofi (France) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Brian Mackness |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Sanofi R&D Large Molecule Research |
| authorships[4].author.id | https://openalex.org/A5108102436 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Abhinav Gupta |
| authorships[4].countries | FR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[4].affiliations[0].raw_affiliation_string | Sanofi R&D Large Molecule Research |
| authorships[4].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[4].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[4].institutions[0].country_code | FR |
| authorships[4].institutions[0].display_name | Sanofi (France) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Abhinav Gupta |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Sanofi R&D Large Molecule Research |
| authorships[5].author.id | https://openalex.org/A5115708980 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Taylor Joren |
| authorships[5].countries | FR |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[5].affiliations[0].raw_affiliation_string | Sanofi R&D Data and Data Science |
| authorships[5].affiliations[1].raw_affiliation_string | Artificial Intelligence and Deep Analytics |
| authorships[5].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[5].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[5].institutions[0].type | company |
| authorships[5].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[5].institutions[0].country_code | FR |
| authorships[5].institutions[0].display_name | Sanofi (France) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Taylor Joren |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Artificial Intelligence and Deep Analytics, Sanofi R&D Data and Data Science |
| authorships[6].author.id | https://openalex.org/A5020346838 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-0170-3029 |
| authorships[6].author.display_name | Samuel Sledzieski |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[6].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[6].affiliations[1].raw_affiliation_string | Computer Science and Artificial Intelligence Laboratory |
| authorships[6].institutions[0].id | https://openalex.org/I63966007 |
| authorships[6].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Samuel Sledzieski |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology |
| authorships[7].author.id | https://openalex.org/A5013597492 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Lena Erlach |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Biosystems Science and Engineering |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Lena Erlach |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Biosystems Science and Engineering |
| authorships[8].author.id | https://openalex.org/A5110011885 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | Maria Wendt |
| authorships[8].countries | FR |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[8].affiliations[0].raw_affiliation_string | Sanofi R&D Large Molecule Research |
| authorships[8].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[8].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[8].institutions[0].type | company |
| authorships[8].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[8].institutions[0].country_code | FR |
| authorships[8].institutions[0].display_name | Sanofi (France) |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Maria Wendt |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Sanofi R&D Large Molecule Research |
| authorships[9].author.id | https://openalex.org/A5048221148 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-2637-6423 |
| authorships[9].author.display_name | Yves Fomekong Nanfack |
| authorships[9].countries | FR |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I3018075036 |
| authorships[9].affiliations[0].raw_affiliation_string | Sanofi R&D Large Molecule Research |
| authorships[9].institutions[0].id | https://openalex.org/I3018075036 |
| authorships[9].institutions[0].ror | https://ror.org/02n6c9837 |
| authorships[9].institutions[0].type | company |
| authorships[9].institutions[0].lineage | https://openalex.org/I3018075036 |
| authorships[9].institutions[0].country_code | FR |
| authorships[9].institutions[0].display_name | Sanofi (France) |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Yves Fomekong Nanfack |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Sanofi R&D Large Molecule Research |
| authorships[10].author.id | https://openalex.org/A5063430862 |
| authorships[10].author.orcid | https://orcid.org/0000-0003-1716-6712 |
| authorships[10].author.display_name | Bryan D. Bryson |
| authorships[10].countries | US |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[10].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[10].affiliations[1].raw_affiliation_string | Department of Biological Engineering |
| authorships[10].institutions[0].id | https://openalex.org/I63966007 |
| authorships[10].institutions[0].ror | https://ror.org/042nb2s44 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I63966007 |
| authorships[10].institutions[0].country_code | US |
| authorships[10].institutions[0].display_name | Massachusetts Institute of Technology |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Bryan Bryson |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | Department of Biological Engineering, Massachusetts Institute of Technology |
| authorships[11].author.id | https://openalex.org/A5044078921 |
| authorships[11].author.orcid | https://orcid.org/0000-0002-2724-7228 |
| authorships[11].author.display_name | Bonnie Berger |
| authorships[11].countries | CA, US |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I63966007 |
| authorships[11].affiliations[0].raw_affiliation_string | Massachusetts Institute of Technology |
| authorships[11].affiliations[1].institution_ids | https://openalex.org/I530967 |
| authorships[11].affiliations[1].raw_affiliation_string | Department of Mathematics |
| authorships[11].affiliations[2].raw_affiliation_string | Computer Science and Artificial Intelligence Laboratory |
| authorships[11].institutions[0].id | https://openalex.org/I530967 |
| authorships[11].institutions[0].ror | https://ror.org/05g13zd79 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I530967 |
| authorships[11].institutions[0].country_code | CA |
| authorships[11].institutions[0].display_name | Toronto Metropolitan University |
| authorships[11].institutions[1].id | https://openalex.org/I63966007 |
| authorships[11].institutions[1].ror | https://ror.org/042nb2s44 |
| authorships[11].institutions[1].type | education |
| authorships[11].institutions[1].lineage | https://openalex.org/I63966007 |
| authorships[11].institutions[1].country_code | US |
| authorships[11].institutions[1].display_name | Massachusetts Institute of Technology |
| authorships[11].author_position | last |
| authorships[11].raw_author_name | Bonnie Berger |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | Computer Science and Artificial Intelligence Laboratory, Department of Mathematics, Massachusetts Institute of Technology |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11725859 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Learning the language of antibody hypervariability |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11016 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Monoclonal and Polyclonal Antibodies Research |
| related_works | https://openalex.org/W2972990938, https://openalex.org/W2100617793, https://openalex.org/W2037745559, https://openalex.org/W4210635245, https://openalex.org/W2103053267, https://openalex.org/W2521166570, https://openalex.org/W4250653039, https://openalex.org/W4283010788, https://openalex.org/W4241393631, https://openalex.org/W2355952544 |
| cited_by_count | 16 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 15 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:11725859 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | other-oa |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Proc Natl Acad Sci U S A |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11725859 |
| primary_location.id | doi:10.1073/pnas.2418918121 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S125754415 |
| primary_location.source.issn | 0027-8424, 1091-6490 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0027-8424 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Proceedings of the National Academy of Sciences |
| primary_location.source.host_organization | https://openalex.org/P4310320052 |
| primary_location.source.host_organization_name | National Academy of Sciences |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320052 |
| primary_location.source.host_organization_lineage_names | National Academy of Sciences |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the National Academy of Sciences |
| primary_location.landing_page_url | https://doi.org/10.1073/pnas.2418918121 |
| publication_date | 2024-12-30 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2999958881, https://openalex.org/W2070360032, https://openalex.org/W3139307114, https://openalex.org/W4285010209, https://openalex.org/W4220798957, https://openalex.org/W4214886481, https://openalex.org/W4383957026, https://openalex.org/W2910384850, https://openalex.org/W3146944767, https://openalex.org/W4286500588, https://openalex.org/W4400231442, https://openalex.org/W3166142427, https://openalex.org/W4287724045, https://openalex.org/W6763868836, https://openalex.org/W3195577433, https://openalex.org/W4280625391, https://openalex.org/W4200079908, https://openalex.org/W4307348807, https://openalex.org/W1855237549, https://openalex.org/W2143467722, https://openalex.org/W3038869230, https://openalex.org/W2127552260, https://openalex.org/W2801965704, https://openalex.org/W2110505462, https://openalex.org/W2990052415, https://openalex.org/W3041220471, https://openalex.org/W2097816699, https://openalex.org/W2343191045, https://openalex.org/W2913208793, https://openalex.org/W3207843597, https://openalex.org/W3007865917, https://openalex.org/W2256987374, https://openalex.org/W2974982237, https://openalex.org/W2023490488, https://openalex.org/W4300802462, https://openalex.org/W1994891218, https://openalex.org/W3015784495, https://openalex.org/W4206087583, https://openalex.org/W4396966002, https://openalex.org/W3159865332, https://openalex.org/W4207069608, https://openalex.org/W4392968035, https://openalex.org/W4400482950, https://openalex.org/W4281758439, https://openalex.org/W6739901393, https://openalex.org/W1995875735, https://openalex.org/W2074231493, https://openalex.org/W2951433247, https://openalex.org/W4206948363, https://openalex.org/W4385245566 |
| referenced_works_count | 50 |
| abstract_inverted_index.a | 46, 101, 108 |
| abstract_inverted_index.In | 41 |
| abstract_inverted_index.We | 89, 165 |
| abstract_inverted_index.an | 113 |
| abstract_inverted_index.be | 157 |
| abstract_inverted_index.by | 62, 96 |
| abstract_inverted_index.do | 28 |
| abstract_inverted_index.in | 8, 18, 121, 142, 162 |
| abstract_inverted_index.it | 98 |
| abstract_inverted_index.of | 103, 129, 137, 175, 180, 187 |
| abstract_inverted_index.on | 64, 79 |
| abstract_inverted_index.to | 22, 31, 99, 107, 118, 160 |
| abstract_inverted_index.up | 117 |
| abstract_inverted_index.we | 44 |
| abstract_inverted_index.82% | 114 |
| abstract_inverted_index.Our | 71 |
| abstract_inverted_index.and | 67, 84, 111, 116, 148, 173, 183 |
| abstract_inverted_index.can | 156 |
| abstract_inverted_index.due | 21 |
| abstract_inverted_index.for | 59, 93 |
| abstract_inverted_index.key | 86 |
| abstract_inverted_index.not | 29 |
| abstract_inverted_index.on. | 40 |
| abstract_inverted_index.our | 185 |
| abstract_inverted_index.set | 102 |
| abstract_inverted_index.the | 23, 32, 170, 178 |
| abstract_inverted_index.PLMs | 14 |
| abstract_inverted_index.also | 125 |
| abstract_inverted_index.bind | 106 |
| abstract_inverted_index.have | 4, 15 |
| abstract_inverted_index.rely | 39 |
| abstract_inverted_index.such | 37 |
| abstract_inverted_index.that | 36, 105, 133 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.will | 168 |
| abstract_inverted_index.AbMAP | 92, 124, 167 |
| abstract_inverted_index.PLMs. | 164 |
| abstract_inverted_index.other | 85 |
| abstract_inverted_index.which | 27, 55 |
| abstract_inverted_index.while | 139 |
| abstract_inverted_index.(PLMs) | 3 |
| abstract_inverted_index.B-cell | 134 |
| abstract_inverted_index.called | 50 |
| abstract_inverted_index.deepen | 184 |
| abstract_inverted_index.design | 172 |
| abstract_inverted_index.immune | 130 |
| abstract_inverted_index.inputs | 61 |
| abstract_inverted_index.models | 2, 38, 58 |
| abstract_inverted_index.obtain | 112 |
| abstract_inverted_index.refine | 100 |
| abstract_inverted_index.study, | 43 |
| abstract_inverted_index.toward | 145 |
| abstract_inverted_index.22-fold | 119 |
| abstract_inverted_index.Protein | 0 |
| abstract_inverted_index.adapted | 159 |
| abstract_inverted_index.antigen | 80 |
| abstract_inverted_index.binding | 68, 122 |
| abstract_inverted_index.conform | 30 |
| abstract_inverted_index.effects | 78 |
| abstract_inverted_index.feature | 73 |
| abstract_inverted_index.humoral | 188 |
| abstract_inverted_index.learned | 72 |
| abstract_inverted_index.limited | 16 |
| abstract_inverted_index.predict | 76 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.readily | 158 |
| abstract_inverted_index.similar | 146 |
| abstract_inverted_index.success | 7 |
| abstract_inverted_index.unlocks | 126 |
| abstract_inverted_index.(AbMAP), | 54 |
| abstract_inverted_index.Antibody | 51 |
| abstract_inverted_index.However, | 11 |
| abstract_inverted_index.advances | 161 |
| abstract_inverted_index.analyses | 128 |
| abstract_inverted_index.antibody | 65, 87, 94 |
| abstract_inverted_index.applying | 97 |
| abstract_inverted_index.approach | 155 |
| abstract_inverted_index.binding, | 81 |
| abstract_inverted_index.converge | 144 |
| abstract_inverted_index.expedite | 177 |
| abstract_inverted_index.hit-rate | 115 |
| abstract_inverted_index.increase | 120 |
| abstract_inverted_index.language | 1 |
| abstract_inverted_index.learning | 48, 154 |
| abstract_inverted_index.modeling | 9, 19, 174 |
| abstract_inverted_index.paratope | 82 |
| abstract_inverted_index.peptide, | 110 |
| abstract_inverted_index.receptor | 135 |
| abstract_inverted_index.regions, | 26 |
| abstract_inverted_index.transfer | 47, 153 |
| abstract_inverted_index.validate | 91 |
| abstract_inverted_index.AbMAP’s | 152 |
| abstract_inverted_index.affinity. | 123 |
| abstract_inverted_index.coverage. | 150 |
| abstract_inverted_index.different | 141 |
| abstract_inverted_index.discovery | 179 |
| abstract_inverted_index.efficient | 171 |
| abstract_inverted_index.examples. | 70 |
| abstract_inverted_index.framework | 49 |
| abstract_inverted_index.immunity. | 189 |
| abstract_inverted_index.proteins. | 10 |
| abstract_inverted_index.revealing | 132 |
| abstract_inverted_index.sequence, | 143 |
| abstract_inverted_index.structure | 66 |
| abstract_inverted_index.Processing | 53 |
| abstract_inverted_index.SARS-CoV-2 | 109 |
| abstract_inverted_index.accelerate | 169 |
| abstract_inverted_index.accurately | 75 |
| abstract_inverted_index.antibodies | 20, 104 |
| abstract_inverted_index.anticipate | 166 |
| abstract_inverted_index.fine-tunes | 56 |
| abstract_inverted_index.functional | 149 |
| abstract_inverted_index.impressive | 6 |
| abstract_inverted_index.latter’s | 24 |
| abstract_inverted_index.mutational | 77 |
| abstract_inverted_index.principles | 35 |
| abstract_inverted_index.remarkably | 140 |
| abstract_inverted_index.structural | 147 |
| abstract_inverted_index.antibodies, | 176 |
| abstract_inverted_index.large-scale | 127 |
| abstract_inverted_index.performance | 17 |
| abstract_inverted_index.properties. | 88 |
| abstract_inverted_index.repertoires | 136 |
| abstract_inverted_index.specificity | 69 |
| abstract_inverted_index.supervising | 63 |
| abstract_inverted_index.Importantly, | 151 |
| abstract_inverted_index.conservation | 34 |
| abstract_inverted_index.demonstrated | 5 |
| abstract_inverted_index.evolutionary | 33 |
| abstract_inverted_index.foundational | 57, 163 |
| abstract_inverted_index.individuals, | 138 |
| abstract_inverted_index.optimization | 95 |
| abstract_inverted_index.repertoires, | 131 |
| abstract_inverted_index.hypervariable | 25 |
| abstract_inverted_index.therapeutics, | 182 |
| abstract_inverted_index.understanding | 186 |
| abstract_inverted_index.antibody-based | 181 |
| abstract_inverted_index.experimentally | 90 |
| abstract_inverted_index.general-purpose | 12 |
| abstract_inverted_index.identification, | 83 |
| abstract_inverted_index.representations | 74 |
| abstract_inverted_index.antibody-sequence | 60 |
| abstract_inverted_index.“foundational” | 13 |
| abstract_inverted_index.Mutagenesis-Augmented | 52 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.98313175 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |