Back

Single-antibody mutational scanning reveals poly-specificity and immune constraints in vaccination responses

Schlotheuber, L. J.; Agersnap, S. N.; Greis, D.; Streuli, A.; Yamauchi, Y.; Eyer, K.

2026-01-07 immunology
10.64898/2026.01.07.697678 bioRxiv
Show abstract

Current repertoire technologies with single-antibody resolution usually assess binding to only one or a few recombinant antigens, neglecting variant diversity of antigens and poly-specificity of antibodies. Here, we introduce a method for analyzing antibody repertoires at single-antibody resolution against a large library of SARS-CoV-2 receptor-binding domain (RBD) variants, enabling the identification and analysis of recognized RBD variants by rare, poly-specific antibodies. This technique provided unique insights into single-antibody binding and escape profiles within a murine immunization model with different RBDs, offering valuable data useful to optimize vaccine design against emerging variants and study antibody poly-specificity.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 5%
26.0%
Similar papers in this journal
2
Cell Reports
○ 1498 papers in training set
Top 5%
6.6%
Similar papers in this journal
3
Science Advances
● 1243 papers in training set
Top 5%
5.4%
Similar papers in this journal
4
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 9%
5.4%
Similar papers in this journal
5
Cell
○ 431 papers in training set
Top 2%
5.1%
Similar papers in this journal
6
eLife
● 5828 papers in training set
Top 31%
4.0%
50% of probability mass above
7
iScience
○ 1154 papers in training set
Top 6%
3.4%
8
Nature Immunology
○ 79 papers in training set
Top 0.9%
3.2%
9
Immunity
○ 67 papers in training set
Top 0.7%
2.6%
10
Nature Nanotechnology
○ 32 papers in training set
Top 0.2%
2.6%
11
Cell Reports Medicine
○ 153 papers in training set
Top 1%
2.4%
12
Nature Methods
○ 385 papers in training set
Top 4%
2.3%
13
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.1%
14
Cell Systems
○ 201 papers in training set
Top 2%
2.1%
15
Communications Biology
○ 993 papers in training set
Top 12%
1.9%
16
Cell Reports Methods
○ 165 papers in training set
Top 2%
1.7%
17
Nature Chemical Biology
○ 119 papers in training set
Top 2%
1.1%
18
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.1%
19
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
20
Nature
○ 645 papers in training set
Top 9%
1.0%
21
Science Immunology
● 88 papers in training set
Top 2%
1.0%
22
Cell Discovery
○ 57 papers in training set
Top 1%
0.8%
23
ACS Central Science
● 71 papers in training set
Top 2%
0.8%
24
Science
● 477 papers in training set
Top 9%
0.8%
25
mAbs
○ 32 papers in training set
Top 0.5%
0.8%
26
Frontiers in Immunology
○ 638 papers in training set
Top 10%
0.8%
27
Scientific Reports
○ 3612 papers in training set
Top 74%
0.8%
28
ACS Nano
● 113 papers in training set
Top 2%
0.6%
29
Nucleic Acids Research
◐ 1281 papers in training set
Top 15%
0.6%
30
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.