Exploiting sequence and stability information for directing nanobody stability engineering

By Patrick Kunz, T. Flock, Nicolas Soler, Moritz Zaiss, C. Vincke, Yann G. J. Sterckx, Damjana Kastelic, S. Muyldermans, Jörg D. Hoheisel. 2017

59 citations recorded by the source corpus.

Background Variable domains of camelid heavy-chain antibodies, commonly named nanobodies, have high biotechnological potential. In view of their broad range of applications in research, diagnostics and therapy, engineering their stability is of particular interest. One important aspect is the improvement of thermostability, because it can have immediate effects on conformational stability, protease resistance and aggregation propensity of the protein. Methods We analyzed the sequences and thermostabilities of 78 purified nanobody binders. From this data, potentially stabilizing amino acid variations were identified and studied experimentally. Results Some mutations improved the stability of nanobodies by up to 6.1 °C, with an average of 2.3 °C across eight modified nanobodies. The stabilizing mechanism involves an improvement of both conformational stability and aggregation behavior, explaining the variable degree of stabilization in individual molecules. In some instances, variations predicted to be stabilizing actually led to thermal destabilization of the proteins. The reasons for this contradiction between prediction and experiment were investigated. Conclusions The results reveal a mutational strategy to improve the biophysical behavior of nanobody binders and indicate a species-specificity of nanobody architecture. General significance This study illustrates the potential and limitations of engineering nanobody thermostability by merging sequence information with stability data, an aspect that is becoming increasingly important with the recent development of high-throughput biophysical methods.

Subfields: Biotechnology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Accelerating Protein Design Using Autoregressive Generative Models (2019, methodology): “Using this model, we ﬁnd that length-normalized log-probability calculations predict the thermostability of new llama nanobody sequences ( (cid:26) =0.53, N=17; Figure 3a; Kunz et al., 2017).”
Protein design and variant prediction using autoregressive generative models (2021, methodology): “Using this model, we ﬁ nd that the log-probability ﬁ tness calculations predict the thermostability of unseen llama nanobody sequences from four different stability experiments 69 – 72 (Fig.”