Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization

Authors

  • Brian D. Weitzner,

    1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
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    • *Brian D. Weitzner and Daisuke Kuroda contributed equally to this work.

  • Daisuke Kuroda,

    1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
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    • *Brian D. Weitzner and Daisuke Kuroda contributed equally to this work.

  • Nicholas Marze,

    1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
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  • Jianqing Xu,

    1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
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  • Jeffrey J. Gray

    Corresponding author
    1. Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
    2. Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland
    • Correspondence to: Jeffrey J. Gray, 3400 North Charles Street, Baltimore, MD 21218. E-mail: jgray@jhu.edu

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ABSTRACT

Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub-Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL–VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL–VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody. Proteins 2014; 82:1611–1623. © 2014 Wiley Periodicals, Inc.

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