RosettaAntibody: antibody variable region homology modeling server

By Aroop Sircar, Eric T. Kim, Jeffrey J. Gray. 2009

160 citations recorded by the source corpus.

The RosettaAntibody server (http://antibody.graylab.jhu.edu) predicts the structure of an antibody variable region given the amino-acid sequences of the respective light and heavy chains. In an initial stage, the server identifies and displays the most sequence homologous template structures for the light and heavy framework regions and each of the complementarity determining region (CDR) loops. Subsequently, the most homologous templates are assembled into a side-chain optimized crude model, and the server returns a picture and coordinate file. For users requesting a high-resolution model, the server executes the full RosettaAntibody protocol which additionally models the hyper-variable CDR H3 loop. The high-resolution protocol also relieves steric clashes by optimizing the CDR backbone torsion angles and by simultaneously perturbing the relative orientation of the light and heavy chains. RosettaAntibody generates 2000 independent structures, and the server returns pictures, coordinate files, and detailed scoring information for the 10 top-scoring models. The 10 models enable users to use rational judgment in choosing the best model or to use the set as an ensemble for further studies such as docking. The high-resolution models generated by RosettaAntibody have been used for the successful prediction of antibody–antigen complex structures.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014, methodology): “%) it is 1.56 ± 0.51 Å. Similar conclusions were derived from an independent evaluation in which our method was compared with other available methods (Chemical Computer Group 50 , Accelrys software 51 , and RosettaAntibody Modeling 52 ) and found to be extremely competitive 47–49 .”
PIGSPro: prediction of immunoGlobulin structures v2 (2017, methodology): “Different protocols for antibody modelling have also been developed, using strategies different from the one here described (20,27–31).”
The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, background): “RosettaAntibody (74) and PIGS (75) are two applications that model antibodies.”
Nonspecificity in a nonimmune human scFv repertoire (2017, methodology): “Antibody homology modeling was conducted using Rosetta Antibody through ROSIE using default settings.(20-22,40) For all clones, the grafted, relaxed model structure was chosen for further analysis.”
SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models (2010, methodology): “We test SnugDock using antibody homology models obtained from two accessible public servers, namely RosettaAntibody [28] and Web Antibody Modeling (WAM) [29].”
AbPredict 2: A server for accurate and unstrained structure prediction of antibody variable domains (2018, methodology): “Automated web-servers, such as RosettaAntibody(Sircar, Kim, & Gray, 2009), PIGS(Marcatili, Rosi, & Tramontano, 2008), and SAbPred(Dunbar et al., 2016), start by identifying the segments corresponding to the CDRs and framework in the query sequence; template structures from the Protein Data Bank…”
Analysis and Modeling of the Variable Region of Camelid Single Domain Antibodies (2011, methodology): “In a few cases, grafting creates a broken loop because of framework deviations [also observed in a few cases in canonical Abs submitted to the RosettaAntibody server (27)].”
Structural Bioinformatics (unknown): “We and others have developed effective methods to predict the structure of antibodies (Choi and Deane, 2010, 2011; Sircar et al., 2009; Sivasubramanian et al., 2009).”