Computational design of antibody-affinity improvement beyond in vivo maturation
By S. Lippow, K. Wittrup, B. Tidor. Nature Biotechnology, 2007
360 citations recorded by the source corpus.
Subfields: Biotechnology, Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/404fcd3cb0c14a46f09ed5d51cbe3cf0fa59775f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biotechnology
In Molecular Biology
- Torsten Schwede
- Yang Zhang
- Andrej Sali
- Ben M. Webb
- Andriy Kryshtafovych
- Krzysztof Fidelis
- David Baker
- Jeffrey J. Gray
In Radiology, Nuclear Medicine and Imaging
- Brian D. Weitzner
- Jeffrey J. Gray
- John Moult
- Andriy Kryshtafovych
- Krzysztof Fidelis
- Jared Adolf-Bryfogle
- William R. Schief
- Roland L. Dunbrack
Citations in the corpus
- Antibody Structure and Function: The Basis for Engineering Therapeutics (2019, background): “1, achieving 52 and 30 pM affinity, respectively [167,168].”
- Backbone flexibility in computational protein design. (2009, background): “Several ambitious engineering goals have recently been met, including designing proteins that catalyze reactions lacking natural enzymes [1,2], increasing the affinity of antibody–antigen interactions beyond in vivo levels [3], and engineering peptide transcription factors that oligomerize with high”
- Achievements and Challenges in Computational Protein Design. (2017, methodology): “In 2007 the Tidor computational lab and the Wittrup experimental lab joined forces to apply CPD for the improvement of antibody affinity [66].”
- Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization (2014, background): “41 In summary, three scenarios led to failures in CDR template selection: (1) no availability of low-RMSD templates (six cases); (2) inability to identify the best template (four cases); and (3) perturbation of the template away from the native structure by energy minimization and refinement (three ”
- RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “General protein design methods have been applied to affinity maturation [29–31], improving stability [32–34], humanization [35,36], and the design of phage/yeast display libraries [37–40], while three software programs have been developed specifically for antibody computational design.”
- Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014, methodology): “The protocol described here has been applied to address several problems, which range from the humanization of mouse antibodies for use in therapy 14 to the modeling of the antibody for re-design 11,13 and docking purposes 48 .”
- Computational structure-based redesign of enzyme activity (2009, methodology): “More accurate energy functions are sometimes used as a postprocessing step to reevaluate and rerank the top-scoring predictions from the initial model (18).”
- Outcome of a Workshop on Applications of Protein Models in Biomedical Research (2009, methodology): “Similar techniques have been used to engineer smaller antibody fragments with improved specificity, affinity, and half-life (Hinton et al., 2004; Lazar et al., 2006; Lippow et al., 2007).”