Prediction of Single-Mutation Effects for Fluorescent Immunosensor Engineering with an End-to-End Trained Protein Language Model

A quenchbody (Q-body) is a fluorophore-labeled homogeneous
immunosensor
in which the fluorophore is quenched by tryptophan (Trp) residues
in the vicinity of the antigen-binding paratope and dequenched in
response to antigen binding. Developing Q-bodies against targets on
demand remains challenging due to the large sequence space of the
complementarity-determining regions (CDRs) related to antigen binding
and fluorophore quenching. In this study, we pioneered a strategy
using high-throughput screening and a protein language model (pLM)
to predict the effects of mutations on fluorophore quenching with
single amino acid resolution, thereby enhancing the performance of
Q-bodies. We collected yeasts displaying nanobodies with high- and
low-quenching properties for the TAMRA fluorophore from a modified
large synthetic nanobody library followed by next-generation sequencing.
The pretrained pLM, connected to a single-layer perceptron, was trained
end-to-end on the enriched CDR sequences. The achieved quenching prediction
model that focused on CDR1 + 3 performed best in the evaluation with
precision-recall curves. Using this model, we predicted and validated
the effective mutations in two anti-SARS-CoV-2 nanobodies, RBD1i13
and RBD10i14, which converted them into Q-bodies. For RBD1i13, three
Trp mutants were predicted to have high probability scores for quenching
through in silico Trp scanning. These mutants were verified via yeast
surface display, and all showed enhanced quenching. For RBD10i14,
mutations at four positions close to an existing Trp gave high scores
through in silico saturation mutagenesis scanning. Six of eight high-score
mutants, derived from two mutants at each of the four positions, exhibited
deeper quenching on the yeast surface. Next, combined with the investigation
of antigen binding of the mutants, we successfully achieved Q-bodies
with enhanced responses. Overall, our strategy allows the prediction
of fluorescence responses solely on the basis of the antibody sequence
and will be essential for the rational selection and design of antibodies
to achieve immunosensors with larger responses.