Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models.

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Biophysical properties of the clinical-stage antibody landscape.

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.

Purification of common light chain IgG-like bispecific antibodies using highly linear pH gradients.

Monovalent bispecific antibodies (BsAbs) are projected to have broad clinical applications due to their ability to bind two different targets simultaneously. Although they can be produced using recombinant technologies, the correct pairing of heavy and light chains is a significant manufacturing problem. Various approaches exploit mutations or linkers to favor the formation of the desired BsAb, but a format using a single common light chain has the advantage that no other modification to the antibody is required. This strategy reduces the number of formed molecules to three (the BsAb and the two parent mAbs), but the separation of the BsAb from the two monovalent parent molecules still poses a potentially difficult purification challenge. Current methods employ ion exchange chromatography and linear salt gradients, but are only successful if the difference in the observed isoelectric points (pIs) of two parent molecules is relatively large. Here, we describe the use of highly linear pH gradients for the facile purification of common light chain BsAbs. The method is effective at separating molecules with differences in pI as little as 0.10, and differing in their sequence by only a single charged amino acid. We also demonstrate that purification resins validated for manufacturing are compatible with this approach.