Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking

By A. Sivasubramanian, Aroop Sircar, Sidhartha Chaudhury, Jeffrey J. Gray. 2009

189 citations recorded by the source corpus.

High‐resolution homology models are useful in structure‐based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of VL‐VH rigid‐body orientation and CDR backbone and side‐chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root mean square deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 Å with 80% of the targets having an rmsd lower than 2.0 Å. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6, 1.9, 2.4, 3.1, and 6.0 Å for very short (4–6 residues), short (7–9), medium (10–11), long (12–14) and very long (17–22) loops, respectively. When the set of ten top‐scoring antibody homology models are used in local ensemble docking to antigen, a moderate‐to‐high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high‐resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large‐scale antibody–antigen docking study using homology models reveals the level of “functional accuracy” of these structural models toward protein engineering applications. Proteins 2009; 74:497–514. © 2008 Wiley‐Liss, Inc.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding. (2010, background): “Other computational efforts have focused on predicting antibody structures from only their amino acid sequences (Morea et al., 2000; Whitelegg and Rees, 2000; Sivasubramanian et al., 2009).”
Improved prediction of antibody VL-VH orientation. (2016, methodology): “…structure, 1 of 10> -nstruct <200|| 1000> -constraints:cst_file < cter_constraint file> Preparation of antibody database set The RosettaAntibody database consists of 1040 antibody FV crystal structures culled from the Protein Data Bank using the methods described by Sivasubramanian et al. (2009).”
TCRmodel: high resolution modeling of T cell receptors from sequence (2018, methodology): “The algorithm to model TCRs from sequence was written as a new protocol in the program Rosetta (11), and reflects the general scheme used by template-based antibody structure modeling methods, such as RosettaAntibody (19), with several distinctions.”
Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. (2013, methodology): “This facilitates modeling, as demonstrated by RosettaAntibody, WAM and PIGS (Whitelegg and Rees, 2000; Marcatili et al., 2008; Sivasubramanian et al., 2009).”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, background): “44,45 Due to the increased complexity, modeling the challenging HCDR3 loop is provided as a separate option.”
Structural Bioinformatics (unknown): “…(Choi and Deane, 2011; Mandal et al., 1996; Marcatili et al., 2008) and template-free methods that try to predict the structure using ab initio conformation searches followed by ranking based on energy estimates and clash avoidance (Bruccoleri and Karplus, 1987; Sivasubramanian et al., 2009).”