Prediction of single-mutation effects for fluorescent immunosensor engineering with an end-to-end trained protein language model

Akihito Inoue; Bo Zhu; Keisuke Mizutani; Ken Kobayashi; Takanobu Yasuda; Alon Wellner; Chang C. Liu; Kitaguchi, Tetsuya

doi:10.51094/jxiv.971

##article.authors##

Akihito Inoue Graduate School of Life Science and Technology, Institute of Science Tokyo
Bo Zhu Laboratory for Chemistry and Life Science, Institute of Integrated Research, Institute of Science Tokyo
Keisuke Mizutani School of Engineering, Institute of Science Tokyo
Ken Kobayashi School of Engineering, Institute of Science Tokyo
Takanobu Yasuda Laboratory for Chemistry and Life Science, Institute of Integrated Research, Institute of Science Tokyo
Alon Wellner Department of Biomedical Engineering, University of California, Irvine
Chang C. Liu Department of Biomedical Engineering, University of California, Irvine
Kitaguchi, Tetsuya Laboratory for Chemistry and Life Science, Institute of Integrated Research, Institute of Science Tokyo

DOI:

https://doi.org/10.51094/jxiv.971

キーワード:

immunosensor、 quenchbody、 nanobody、 deep learning、 protein language model

抄録

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 de-quenched in response to antigen binding. The development of 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 TAMRA fluorophore from a modified large synthetic nanobody library, followed by next-generation sequencing. The pre-trained pLM, connected with a single-layer perceptron, was trained end-to-end on the enriched CDR sequences. The achieved quenching prediction model focused on CDR1+3 performed best in evaluation with precision-recall curves. Using this model, we predicted and validated effective mutations in two anti-SARS-CoV-2 nanobodies, RBD1i13 and RBD10i14, that convert them into Q-bodies. For RBD1i13, three Trp mutants were predicted with high probability scores for quenching through in silico Trp scanning. These mutants were verified via yeast surface display to all show enhanced quenching. For RBD10i14, mutations at four positions close to an existing Trp gave high scores through in silico saturation mutagenesis scanning. Six out of eight high-score mutants, derived from two mutants at each of four positions, exhibited deeper quenching on 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 for the prediction of fluorescence responses based solely on the sequence of the antibody and will be essential for the rational selection and design of antibodies to achieve immunosensors with larger responses.

利益相反に関する開示

B.Z., T.Y., and T.K. received honoraria from HikariQ Health, Inc. for another unrelated project.

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引用文献

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