The isolation of nanobodies from pre-immune libraries by means of biopanning is a straightforward process. Nevertheless, the recovered candidates often require optimization to improve some of their ...biophysical characteristics. In principle, CDRs are not mutated because they are likely to be part of the antibody paratope, but in this work, we describe a mutagenesis strategy that specifically addresses CDR1. Its sequence was identified as an instability hot spot by the PROSS program, and the available structural information indicated that four CDR1 residues bound directly to the antigen. We therefore modified the loop flexibility with the addition of an extra glycine rather than by mutating single amino acids. This approach significantly increased the nanobody yields but traded-off with moderate affinity loss. Accurate modeling coupled with atomistic molecular dynamics simulations enabled the modifications induced by the glycine insertion and the rationale behind the engineering design to be described in detail.
Antibodies are irreplaceable reagents in both research and clinical practice. Despite their relevance, the structural complexity of conventional mono- and polyclonal antibodies (IgG) has always been ...a limit for their engineering towards reagents optimized for specific applications, such as in vivo diagnostics and therapy. Furthermore, their isolation is time consuming, their production expensive, and their functionalization results often in heterogeneous macromolecule populations. These drawbacks promoted the search for both innovative antibody isolation strategies and alternative scaffolds. In vitro panning of pre-immune collections of recombinant antibody fragments allows for the simple and fast recovery of binders. Since they did not undergo somatic maturation, their affinity for targets can be insufficient but on the other hand they can be rapidly mutated by standard molecular biology techniques to generate second-generation antibodies among which to identify clones with improved characteristics. Both stochastic and rational methods have been proposed for the optimization process. Random mutagenesis followed by panning at stringent conditions has been successful used to select binders with improved physical characteristics. Rational methods try to identify in silico key residues involved in the regulation of specific antibody features, such as stability or binding affinity. The accuracy of these methods usually depends on the calculation resources. In this perspective, smaller molecules can be analyzed “better” than larger because of their restricted number of residues. Nanobodies small dimensions have been long appreciated since enable better tissue penetration, shorter clearance time, higher yields. Now it becomes evident that this characteristic makes them also optimal objects for modeling.
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