Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains.

By Colin A. Smith, Tanja Kortemme. 2010

109 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, background): “Flexible backbone design methods that use pre-generated ensembles as a starting point for design 92,115 perform poorly in benchmarks, likely because the ensemble does not accurately describe the unbound-to-bound conformational changes.”
Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity (2015, methodology): “While this approach improved prediction accuracy in a variety of applications including molecular recognition specificity [29] and amino acid covariation [27], it might not accurately capture how protein backbones respond to sequence mutations as the original backbone ensembles are created with the ”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Incorporating such back-rub moves into design simulations has led to considerable improvements in modeling structural changes in point mutants (49, 50, 52), protein dynamics on fast timescales (53, 54), prediction of molecular recognition speci ﬁ city (55), and the sequence design (56).”
Web‐accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE) (2018, unknown): “47,48 VIP—Core redesign to eliminate voids.”
Protein Engineering by Combined Computational and In Vitro Evolution Approaches. (2016, unknown): “Several studies have utilized slightly different CPD approaches to simulate observed sequence profiles obtained in PD binding selections [67–70].”
Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design (2011, methodology): “Scoring metrics are used as defined previously [35].”
Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair (2012, methodology): “Although the flexible backbone de-sign method has been benchmarked on existing data (23, 24), this application represents the first forward engineering test of the method ’ s efficacy (see Methods ).”
Protein sequence design with a learned potential (2020, methodology): “One failure mode of Rosetta-FixBB is its convergence to highly similar sequences, a deficiency that is handled somewhat but not entirely by protocols that interleave backbone relaxes during design, such as our baseline Rosetta-RelaxBB protocol and Backrub [51, 52, 53].”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, methodology): “Key results: Recognition specificity performance with Talaris is comparable to the originally published performance of the protocol with Score12 [39,40].”
Methods in Enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations” (2013, unknown): “In this protocol , the interface energy is given greater weight (Smith & Kortemme, 2010, 2011).”