Optimizing Antibody Affinity and Developability Using a Framework–CDR Shuffling Approach—Application to an Anti-SARS-CoV-2 Antibody

Abstract

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework–CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

Machine Learning-Guided Antibody Engineering That Leverages Domain Knowledge To Overcome The Small Data Problem

“…"selected subset" in Figure 1B) on a 96-well plate will potentially result in affinity enhancing mutations in a single round. The second iteration starts with search for antibody scaffolds in the large antibody sequence databases (including OAS) to select appropriate FR regions that can be combined with the optimized CDRs from the first iteration as previously described 16 to generate sequences with various FR-CDR combinations. These second-round candidates were virtually screened to select candidates that have optimal developability scores (sequence liabilities, charge distribution, hydrophobic patches, T-cell epitopes, etc.)…”

“…A diverse set of FR regions were sampled from a database containing next-generation sequences from public repositories such as cAb-Rep 27 and OAS. As previously described 16 , FRs were filtered based on structural properties such as North CDR cluster 28 , CDR length (to accommodate the modified CDR loops), PTMs, rare amino acids, high-energy (PyRosetta) residues, and FR sequence diversity. Figure S3 shows the AbLang embeddings of the scaffold sequences, where the spread of the red crosses illustrates the diverse sequence space sampled.…”

“…Additionally, as described above due to the bias of AbRFC against G mutations, decisions for Glycine mutations were made based on AbLang and Rosetta ∆∆𝐺 scores in addition to AbRFC scores. CDR FR Shuffling: FRs were selected using an identical procedure to that described earlier 16 . Briefly, filters for the S309-based designs included:  H1 north cluster = H1-13-A, H2 north cluster = H2-10-A, H3 length = 18, maximum number of PTMS: 18, maximum rare amino acids: 1, minimum V/J-germline ID>= .8.…”

Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability

“…Recently, Gopal, et al . ( 48 ) showed CDR–framework compatibility strongly influenced both expression levels and receptor binding domain binding properties for a combinatorial panel of 84 sequence-related SARS-CoV2 antibodies designed from convalescent patients. The expanded panel of grafted antibodies in this study was specifically generated to expand our knowledge of these relationships and identify the impact on an antibody humanization campaign.…”

Status and Developing Strategies for Neutralizing Monoclonal Antibody Therapy in the Omicron Era of COVID-19

“…In addition, sophisticated bioinformatics tools can be employed to identify key amino acid residues involved in the interaction between an antibody and its target antigen [ 78 , 79 ]. These residues can then be targeted for mutagenesis to enhance antigen-binding affinity and other antibody functionalities [ 80 ].…”