Quantitative high-throughput methods for antibody fragment optimization and discovery

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Abstract Monoclonal antibodies and antibody fragments are an important class of therapeutics comprising a $150B industry. However, methods for discovering and optimizing antibodies to have desired affinity are generally laborious laboratory procedures that require months of hands-on research performed by highly skilled personnel (e.g. phage display, hybridoma, single cell). Additionally, the selection of leads to move forward in the therapeutic development pipeline often must be made with limited information that does not necessarily correspond to quantitative binding affinity. To address these challenges, Protillion has commercialized Prot- MaP, a platform for measuring quantitative protein binding across large libraries of 105 to 109 variants on automated instrumentation, with a time-to-result of approximately 2 days. We achieve this by generating immobilized proteins directly on Illumina DNA sequencing flow cells through a process of in-situ transcription and translation. This platform allows for direct, quantitative measurements of fluorescent antigen binding to entire protein libraries at unprecedented scale—a scale that is finally a match for the sparseness of protein function in amino acid mutation space. In our Phase I period, we adapted Prot-MaP to display VHHs (nanobodies) capable of binding the SARS-CoV-2 spike (S1) receptor binding domain (RBD) protein. Our multi-step optimization first comprehensively identified “beneficial” mutations, which were then combined into a second combinatorial library. This strategy identified tens of thousands of protein variants with affinity superior to wild type, with the best exhibiting the highest reported binding affinity for a VHH to this target, a 100-fold improvement from the starting point. We also developed a strategy to humanize this nanobody, producing a near-fully-human sequence that maintained high affinity. In Phase II, we will first improve automation and commercial scalability of our instrumentation, and develop deep learning models for library design and selection of therapeutic leads. We will next optimize other SARS-CoV-2 S1 RBD-binding nanobodies, as well as nanobodies capable of binding PD-L1, a target relevant to cancer immunotherapy. We will develop a universally applicable pipeline for identifying high-affinity, humanized, clinically-relevant VHH reagents. We will also extend our display capabilities to larger, scFv domains, and carry out scFv affinity optimization against two separate target ligands, including SARS-CoV-2 S1 RBD. Finally, we will adapt our methods to display up to 109 distinct protein variants on a NovaSeq sequencing chip, a scale sufficient to identify binders de novo from naïve humanized VHH libraries. The activities outlined in this proposal will enable display multiple types of antibody fragments, optimize affinity and humanize their sequences, and clearly define the landscape of functional protein sequences. The capability of de novo discovery of new binders from untargeted libraries will make the Protillion platform a vertically integrated “one stop shop” allowing both identification of “hits” from untargeted libraries, as well as detailed mutational analysis and optimization of these variants.