Improved prediction of antibody VL-VH orientation.
Antibodies are important immune molecules with high commercial value and therapeutic interest because of their ability to bind diverse antigens. Computational prediction of antibody structure can quickly reveal valuable information about the nature of these antigen-binding interactions, but only if the models are of sufficient quality. To achieve high model quality during complementarity-determining region (CDR) structural prediction, one must account for the VL-VH orientation. We developed a novel four-metric VL-VH orientation coordinate frame. Additionally, we extended the CDR grafting protocol in RosettaAntibody with a new method that diversifies VL-VH orientation by using 10 VL-VH orientation templates rather than a single one. We tested the multiple-template grafting protocol on two datasets of known antibody crystal structures. During the template-grafting phase, the new protocol improved the fraction of accurate VL-VH orientation predictions from only 26% (12/46) to 72% (33/46) of targets. After the full RosettaAntibody protocol, including CDR H3 remodeling and VL-VH re-orientation, the new protocol produced more candidate structures with accurate VL-VH orientation than the standard protocol in 43/46 targets (93%). The improved ability to predict VL-VH orientation will bolster predictions of other parts of the paratope, including the conformation of CDR H3, a grand challenge of antibody homology modeling.
Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
Nicholas A
NA
Marze
Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
Sergey
S
Lyskov
Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.|Program in Molecular Biophysics, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.|Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Jeffrey J
JJ
Gray
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D061905:Single-Domain Antibodies / Q000737:chemistry*
D061905:Single-Domain Antibodies
antibody modeling; antibody structure; computational structure prediction; domain orientation