Backbone-dependent rotamer library for proteins. Application to side-chain prediction.

By Roland L. Dunbrack, Martin Karplus. 1993

How researchers see this paper

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

Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “…energy—has been used to justify creating knowledge-based potentials from distribution of features from crystal structures (Miyazawa & Jernigan, 1985; Sippl, 1990); for example, rotamer libraries are often based on feature distributions (Dunbrack & Karplus, 1993; Ponder & Richards, 1987).”
High‐accuracy protein structure prediction in CASP14 (2021, background): “As expected from the relationship between side-chain and back-bone geometry, 29,30 the closer the geometry of the models' backbone, the more accurate were the side-chain conformations predicted (Figure 2D).”
A de novo designed protein–protein interface (2007, methodology): “Rotamer libraries used during the side-chain selection process were based on the backbonedependent library of Dunbrack Jr. and Karplus (1993) .”
De novo protein design, a retrospective (2020, background): “Rotameric preferences are in agreement with torsional potentials from molecular mechanics (Dunbrack and Karplus, 1993; Dunbrack and Karplus, 1994) and hence can be extended to design of non-natural foldamers (Shandler et al., 2010).”
Prediction of amino acid side chain conformation using a deep neural network (2017, methodology): “Current popular methods include the use of back-bone independent(19) and back-bone dependent rotamer libraries(7, 13).”
ROSETTALIGAND: Protein–small molecule docking with full side‐chain flexibility (2006, unknown): “38 For a database of 100 native protein–ligand complexes, 5000 models were generated as discussed in the methods section.”
Principles of Flexible Protein-Protein Docking (2008, background): “96 Usually, unbound conformations of side-chains are added to the set of conformers for each residue.”
Computational Methods in Drug Discovery (2014, methodology): “Methods such as dead-end elimination (Desmet et al., 1992) implemented in SCRWL (Dunbrack and Karplus, 1993; Dunbrack and Karplus, 1994; Bower et al., 1997) and Monte Carlo searches (Rohl et al., 2004) are used for side-chain conformation sampling.”