Neutralization fingerprinting panel-based antibody epitope prediction

PS Philipp Schommers
DK Dae Sung Kim
MS Maike Schlotz
CK Christoph Kreer
RE Ralf Eggeling
AH Anna Hake
MS Melanie Stecher
JP Juyeon Park
CR Caelan E. Radford
AD Adam S. Dingens
ME Meryem S. Ercanoglu
HG Henning Gruell
SO Stanley Odidika
MD Marten Dahlhaus
LG Lutz Gieselmann
EA Elvin Ahmadov
RL Rene Y. Lawong
EH Eva Heger
EK Elena Knops
CW Christoph Wyen
TK Tim Kümmerle
KR Katja Römer
SS Stefan Scholten
TW Timo Wolf
CS Christoph Stephan
IS Isabelle Suárez
NR Nagarajan Raju
AA Anurag Adhikari
SE Stefan Esser
HS Hendrik Streeck
RD Ralf Duerr
AN Aubin J. Nanfack
SZ Susan Zolla-Pazner
CG Christof Geldmacher
OG Otto Geisenberger
AK Arne Kroidl
WW Wiston William
LM Lucas Maganga
NN Nyanda Elias Ntinginya
IG Ivelin S. Georgiev
JV Jörg J. Vehreschild
MH Michael Hoelscher
GF Gerd Fätkenheuer
JL Jason J. Lavinder
JB Jesse D. Bloom
MS Michael S. Seaman
CL Clara Lehmann
NP Nico Pfeifer
GG George Georgiou
FK Florian Klein
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Computational epitope prediction of serum IgG-neutralizing activity was conducted as described previously61,80. In brief, the neutralizing IC50s of the respective participant’s IgGs were determined against the 20 pseudovirus f61 panel. Following, the similarity of the fingerprint of the tested participants’ IgGs is compared to the fingerprint of ten classes of reference-bNAbs, grouped by their specific epitope; for each class, shown is a prototypic bNAb member. The prevalence of these reference antibody epitopes in a respective participant’s IgG is computationally predicted and assigned a delineation score between 0 (low) and 1 (high). The scores as shown in Fig. Fig.2a2a represent the percentage ((initial delineation score) × 100).

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