Modeling and docking antibody structures with Rosetta

By Brian D. Weitzner, J. Jeliazkov, Sergey Lyskov, Nicholas A. Marze, Daisuke Kuroda, Rahel Frick, Jared Adolf-Bryfogle, Naireeta Biswas, Roland L. Dunbrack, Jeffrey J. Gray. 2017

253 citations recorded by the source corpus.

We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from 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 (http://rosie.rosettacommons.org/) 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.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs (2020, methodology): “In particular this is analogous to research in antibody modeling in which protocols have been developed to model the long loops of the complementary determining regions [37,38].”
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, methodology): “RosettaAntibody 235 structural models were created by identifying homologous templates for the CDRs, VH–VL 236 orientation, and FRs; assembling the templates into one model; de novo modeling the CDR-H3 loop; 237 rigid-body docking the VH–VL interface; side-chain packing; and minimizing in the Rosett”
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “This highly customizable framework enables the tailored design of antibody CDRs, frameworks, and antigens using highly expanded core components of the Roset-taAntibody framework [77–79] and our PyIgClassify clustering of antibody CDRs [53,54] as its base.”
Improved antibody structure prediction by deep learning of side chain conformations (2021, background): “Additionally, CDR H3 does not have a canonical fold like the other loops [2], making it challenging to model [3–5].”