Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

Abstract

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Unexpected Dynamic Binding May Rescue the Binding Affinity of Rivaroxaban in a Mutant of Coagulation Factor X

“…Conversely, a similar NOAC apixaban is much more rigid and compact, making it easier to be described, but at the same time, it may lose some possibility to bind FXa in alternative patterns as RIV, which may partially explain its lower binding affinity in some mutants, as discussed earlier ( Qu et al, 2019 ). From this example, we would again emphasize the recognition of the important influence of the flexibility of both the target protein and the drug molecule, which was often neglected in structure-based drug design and screening using static crystal structures but more and more considered in recent studies, especially in protein and antibody design ( Corbeil et al, 2021 ).…”

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

“…Also, deep sequencing explorations can help determine the representation of distinct Ab library features associated with antigen interactions. These observations can then be analyzed via computational models to determine Abantigen interactions and molecular docking predictions, ultimately enabling a holistic approach to big data assessments [68,69]. Moreover, such in silico predictive models can offer valuable insights to improve the designs of affinity maturation libraries.…”

Solvated interaction energy: from small-molecule to antibody drug design

“…For biologics, 5 internal publications yielded 150 antibody mutants with both predicted and experimental binding affinities relative to the respective parental antibodies ( Vivcharuk et al, 2017 ; Sulea et al, 2018 ; Zwaagstra et al, 2019 ; Sulea et al, 2020 ; Corbeil et al, 2021 ). When compared to the published results for small molecules, the quantitative prediction of relative binding affinity for antibodies is not as successful (S = 0.58, MUE = 0.90 kcal/mol).…”

“…Despite the low correlation observed, these predictions still proved useful ( Section 4.3 ). In Corbeil et al (2021) a mutational engineering endeavor was undertaken by attempting to redesign the entire CDR H3 loop of an antibody. The requirement of predicting the protein loop backbone conformation significantly increased affinity prediction errors.…”