SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models

By Aroop Sircar, Jeffrey J. Gray. 2010

179 citations recorded by the source corpus.

High resolution structures of antibody-antigen complexes are useful for analyzing the binding interface and to make rational choices for antibody engineering. When a crystallographic structure of a complex is unavailable, the structure must be predicted using computational tools. In this work, we illustrate a novel approach, named SnugDock, to predict high-resolution antibody-antigen complex structures by simultaneously structurally optimizing the antibody-antigen rigid-body positions, the relative orientation of the antibody light and heavy chains, and the conformations of the six complementarity determining region loops. This approach is especially useful when the crystal structure of the antibody is not available, requiring allowances for inaccuracies in an antibody homology model which would otherwise frustrate rigid-backbone docking predictions. Local docking using SnugDock with the lowest-energy RosettaAntibody homology model produced more accurate predictions than standard rigid-body docking. SnugDock can be combined with ensemble docking to mimic conformer selection and induced fit resulting in increased sampling of diverse antibody conformations. The combined algorithm produced four medium (Critical Assessment of PRediction of Interactions-CAPRI rating) and seven acceptable lowest-interface-energy predictions in a test set of fifteen complexes. Structural analysis shows that diverse paratope conformations are sampled, but docked paratope backbones are not necessarily closer to the crystal structure conformations than the starting homology models. The accuracy of SnugDock predictions suggests a new genre of general docking algorithms with flexible binding interfaces targeted towards making homology models useful for further high-resolution predictions.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “Sircar and Gray recently reported on an extension of the R OSETTA D OCK algorithm that allows for accurate modeling of antibody - antigen complexes in the absence of an antibody crystal structure ( 56 ).”
A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes (2013, methodology): “Our approach employs iterative Monte Carlo moves of rigid-body positions and side chain packing combined with refinement of CDR loop (and optionally peptide) backbone conformations, analogous to the SnugDock protocol developed for antibody/antigen docking.(29) Unlike SnugDock, we utilized the recent”
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies (2022, methodology): “For traditional docking methods, 336 the improvements to speed and accuracy by IgFold should be su cient to make them more e ective (49, 50).”
Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint (2016, background): “docking (10), as well as for computational prediction of binding”
Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2 (2011, methodology): “introduced explicit loop modeling and backbone minimization [6] while we added ensemble-based docking [14] and conformational move sets specific to antibody docking [15].”
Application of asymmetric statistical potentials to antibody-protein docking (2012, methodology): “, 2008) that an initial global FFT-based search is highly complementary to a subsequent local refinement using Monte Carlo-based approaches, as implemented in Rosetta (Gray et al., 2003; Sircar and Gray, 2010).”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, unknown): “51,59 Tox dock.”
Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires (2016, methodology): “Generation KIC algorithm while simultaneously refining VH:VL orientations via Rosetta SnugDock (14).”
Analysis and Modeling of the Variable Region of Camelid Single Domain Antibodies (2011, methodology): “Despite uncertainties in CDR H3 predictions, we have demonstrated that flexible backbone docking strategies like EnsembleDock (19) and SnugDock (20) can sometimes compensate for errors in RosettaAntibody homology models by optimizing the paratope for the successful prediction of high-resolution Ab–A”
Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. (2013, methodology): “SnugDock-H dataset consists of the 15 docking targets as described in Sircar and Gray (2010).”