Lecture 18: Jeliazko Jeliazkov (Jeff Gray Lab)

Computational Modeling and Docking of Antibody Structures.

The vertebrate adaptive immune system is capable of promoting cells to degranulate or phagocytose nearly any foreign pathogen by producing immunoglobulin G (IgG) proteins (antibodies) that recognize a specific region (epitope) of a pathogenic molecule (antigen). The ability to bind diverse antigens requires a diverse population of antibodies, which is achieved through complex processes in bone marrow and lymphatic tissues, namely V(D)J recombination and somatic hypermutation. The diversity of antibodies is astonishing; the size of the theoretical naive antibody repertoire is estimated to be >1e13 in humans. In addition to their biological importance, antibodies are routinely used in biotechnology as probes and diagnostics, and dozens of antibodies have been approved as therapeutics.

Therapeutic monoclonal antibodies are a genre of biopharmaceuticals which has benefitted healthcare in various fields from oncology to immune and inflammatory disorders. Development of successful novel therapeutic antibodies requires an understanding of drug and disease mechanisms and the ability to stabilize, affinity mature, and humanize antibodies. Antibody structures can help overcome these challenges by providing atomic-level insights into structure–function relationships and the antibody–antigen interaction [e.g. see refs. (1–4)]. However, experimental techniques for obtaining antibody structures, like X-ray crystallography and nuclear magnetic resonance, are laborious, time-consuming and costly. Computational antibody structure prediction provides a fast and inexpensive route to obtain structures, including those which are not obtainable otherwise.

A schematic[1] of the modeling protocols (full flowcharts
for Rosetta Antibody and Rosetta SnugDock
are available in the original publications).

In their recent Nature Protocols article, Jeliazko Jeliazkov and Jeff Gray from Johns Hopkins Department of Chemical and Biomolecular Engineering and their international collaborators describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from the sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL–VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers. In the figure, the structure on the left shows the FV antibody domains predicted by homology modeling (heavy chain in dark blue with CDR H1 and H2 loops in orange and CDR H3 loop in red; light chain in yellow with its CDR loops in light blue). The structure on the right depicts an antibody–antigen structure output by docking (antigen in green).

References:

[1]Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack Jr RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nature Protocols. 2017 Feb 1;12(2):401-16.

[2]Sircar A, Kim ET, Gray JJ. RosettaAntibody: antibody variable region homology modeling server. Nucleic acids research. 2009 May 20:gkp387.

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Jeliazko Jeliazkov received his B.S. in Physics from the University of Illinois at Urbana-Champaign. Since graduating, he joined the Program in Molecular Biophysics and is pursuing a Ph.D. under the tutelage of Prof. Jeffrey Gray. His research involves the computational prediction of protein–protein interactions, focusing in particular on antibody–antigen, disordered–ordered protein domain, and crystallographic (non-biological) protein–protein interactions.