Antibody modeling assessment

By J. C. Almagro, M. P. Beavers, F. Hernández-Guzmán, Johannes Maier, J. Shaulsky, K. Butenhof, Paul Labute, Nels Thorsteinson, K. Kelly, A. Teplyakov and 3 more. 2011

118 citations recorded by the source corpus.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

High-resolution de novo structure prediction from primary sequence (2022, background): “Antibody complementarity-determining regions (CDRs) are the most diverse and variable parts of the molecules ( 38 , 39 ).”
LYRA, a webserver for lymphocyte receptor structural modeling (2015, methodology): “A recent published paper assessed the accuracy of the majority of currently available antibody modeling software and servers, both commercial and academic (33).”
PIGSPro: prediction of immunoGlobulin structures v2 (2017, methodology): “The above protocol has been implemented in the original PIGS server, which has been positively evaluated in two blind assessments of the prediction accuracy of antibody modelling (24,25).”
A Review of Deep Learning Methods for Antibodies (2020, background): “been able to accurately predict antibody structures for a set of benchmarks, but the modeling of the H3 CDR loop continues to present a significant challenge [2].”
Antibody Structure and Function: The Basis for Engineering Therapeutics (2019, background): “These blinded studies [44,45] involved providing the antibody structure prediction software groups with the sequence of Fv regions for which structures had been determined but were not yet publicly available.”
Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization (2014, background): “As discussed in the previous antibody modeling assessment,41 relaxation to improve the physical realism of a model can destroy the accuracy originally present in a crystallographically-derived template.”
Structural Bioinformatics (unknown): “According to a recent blind assessment of the prediction accuracy of antibody modeling (Almagro et al., 2011), a good prediction can be obtained for non-H3 loops with an average root-mean square deviation (RMSD) of their C atoms close to 1 A ˚ .”
Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014, methodology): “The main advantages of PIGS over these methods are the better performance reported by the independent evaluations mentioned above 47–49 , and also that it is faster, free to use and does not require registration.”