Mechanistic principles of an ultra-long bovine CDR reveal strategies for antibody design

Antibodies bind antigens via flexible loops called complementarity-determining regions (CDRs). These are usually 6-20 residues long. However, some bovine antibodies have ultra-long CDRs comprising more than 50 residues organized in a stalk and a disulfide-rich knob. The design features of this structural unit and its influence on antibody stability remained enigmatic. Here, we show that the stalk length is critical for the folding and stability of antibodies with an ultra-long CDR and that the disulfide bonds in the knob do not contribute to stability; they are important for organizing the antigen-binding knob structure. The bovine ultra-long CDR can be integrated into human antibody scaffolds. Furthermore, mini-domains from de novo design can be reformatted as ultra-long CDRs to create unique antibody-based proteins neutralizing SARS-CoV-2 and the Alpha variant of concern with high efficiency. Our findings reveal basic design principles of antibody structure and open new avenues for protein engineering.

Improved antibody structure prediction by deep learning of side chain conformations

Antibody engineering is becoming increasingly popular in the medical field for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering our method particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of our model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

How Communication Pathways Bridge Local and Global Conformations in an IgG4 Antibody: a Molecular Dynamics Study

The affinity of an antibody for its antigen is primarily determined by the specific sequence and structural arrangement of the complementarity-determining regions (CDRs). Recently, however, evidence has accumulated that points toward a nontrivial relation between the CDR and distal sites on the antibody structure: variations in the binding strengths have been observed upon mutating amino acids separated from the paratope by several nanometers, thus suggesting the existence of a communication network within antibodies whose extension and relevance might be deeper than insofar expected. In this work, we test this hypothesis by means of molecular dynamics (MD) simulations of the IgG4 monoclonal antibody pembrolizumab, an approved drug that targets the programmed cell death protein 1 (PD-1). The molecule is simulated in both the apo and holo states, totalling 4 μs of MD trajectory. The analysis of these simulations shows that the bound antibody explores a restricted range of conformations with respect to the apo one, and that the global conformation of the molecule correlates with that of the CDR; a pivotal role in this relationship is played by the relatively short hinge, which mechanically couples Fab and Fc domains. These results support the hypothesis that pembrolizumab behaves as a complex machinery, with a multi-scale hierarchy of global and local conformational changes that communicate with one another. The analysis pipeline developed in this work is general, and it can help shed further light on the mechanistic aspects of antibody function.

Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

Hinge Influences in Murine IgG Binding to Cryptococcus neoformans Capsule

Decades of studies on antibody structure led to the tenet that the V region binds antigens while the C region interacts with immune effectors. In some antibodies, however, the C region affects affinity and/or specificity for the antigen. One such case is that of the 3E5 antibodies, a family of monoclonal murine IgGs in which the mIgG3 isotype has different fine specificity to the Cryptococcus neoformans capsule polysaccharide than the other mIgG isotypes. Our group serendipitously found another pair of mIgG1/mIgG3 antibodies based on the 2H1 hybridoma to the C. neoformans capsule that recapitulated the differences observed with 3E5. In this work, we report the molecular basis of the constant domain effects on antigen binding using recombinant antibodies. As with 3E5, immunofluorescence experiments show a punctate pattern for 2H1-mIgG3 and an annular pattern for 2H1-mIgG1. Also as observed with 3E5, 2H1-mIgG3 bound on ELISA to both acetylated and non-acetylated capsular polysaccharide, whereas 2H1-mIgG1 only bound well to the acetylated form, consistent with differences in fine specificity. In engineering hybrid mIgG1/mIgG3 antibodies, we found that switching the 2H1-mIgG3 hinge for its mIgG1 counterpart changed the immunofluorescence pattern to annular, but a 2H1-mIgG1 antibody with a mIgG3 hinge still had an annular pattern. The hinge is thus necessary but not sufficient for these changes in binding to the antigen. This important role for the constant region in binding of antibodies to the antigen could affect the design of therapeutic antibodies and our understanding of their function in immunity.Key pointsKey point 1- 2H1 antibodies recapitulate differences between mIgG isotypes observed with 3E5.Key point 2 – The hinge region is necessary but not sufficient for these differences.Key point 3 - The antibody constant region can also play a role in mIgG binding to antigen.

Antibody Humanization: Intra and Inter VH-VL Non-bond Energy Sort Best Humanized Antibody

Antibody humanization of non-human derived antibody reduces immunogenicity of antibody drug. Computer aided antibody humanization has became an efficient and rapid routine process. Our computational humanization pipeline includes CDR grafting onto antibody crystal structure and humanization of CDR grafted antibody structure. Then, intra and inter VH-VL non-bond energy is calculated and sorted for selection best humanized antibody. Compare to experimental dataset, result indicate that intra and inter VH-VL non-bond energy could rank humanized antibody.