Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool.

By M. Bower, Fred E. Cohen, Roland L. Dunbrack. 1997

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, background): “…conformations (Novotny´, Bruccoleri, & Karplus, 1984; Park & Levitt, 1996; Simons et al., 1999), and predicting amino acid sidechain conformations (Bower, Cohen, & Dunbrack, 1997; Jacobson, Kaminski, Friesner, & Rapp, 2002) and free energy changes associated with protein mutations (Gilis &…”
Alternate states of proteins revealed by detailed energy landscape mapping (2010, positive): “28 We found this algorithm a highly effective way to locally minimize the energy of a structure, and it appears to be more efficient than Monte Carlo with minimization with small perturbations.”
Energy-based models for atomic-resolution protein conformations (2020, background): “Evaluations of molecular energy functions have used as a measure of ﬁdelity, the ability to identify native side chain conﬁgurations (rotamers) from crystal structures where the ground-truth conﬁguration has been masked out (Jacobson et al., 2002; Bower et al., 1997).”
Improvements to Robotics-Inspired Conformational Sampling in Rosetta (2013, methodology): “To ensure that no information from native rotamers was used, all starting structures were repacked using a fixed backbone protocol that performs simultaneous rotamer placement (using only a standard rotamer library expanded by one standard deviation around the side chain x1 and x2 torsion angles [44”
Predicting Molecular Conformation via Dynamic Graph Score Matching (2021, background): “Despite that there are some machine learning-based methods specialized in protein sidechain structure prediction [10], they are built upon rotamer libraries [5], which incorporates a lot of domain knowledge.”
A graph‐theory algorithm for rapid protein side‐chain prediction (2003, unknown): “For instance, 3D-PSSM (Kelley et al. 2000) uses SCWRL (Bower et al. 1997) to generate models of proteins from structure-derived profile alignments As the numbers of available sequences and experimentally determined protein structures both increase, improved models of proteins can be constructed on…”
MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology. (2004, methodology): “If only C or backbone coordinates are present, the SCWRL utility[30,38] is used to add side chains from a rotamer library, while missing hydrogens are added with the HBUILD facility in CHARMM [1].”
On the role of the crystal environment in determining protein side-chain conformations. (2002, background): “However, even methods that use only steric clash and/or statistical preferences to place side-chains perform reasonably well in the core, with x1 prediction accuracies ,90% in some cases, including results reported for the SCWRL program.(8) Although gratifying, these excellent results for core resid”
Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives (2012, methodology): “No homologous atoms are defined in the loop regions[94,95].”
Prediction of amino acid side chain conformation using a deep neural network (2017, methodology): “Second, a physics-based scoring function is used to evaluate the likelihood of the sampled conformations(8-11).”
Estimation of model accuracy in CASP13 (2019, methodology): “Non-statistical potential terms include a clash score as defined for SCWRL3, 45 the raw count of other residues within 15 Å and solvent accessibility in Å.”
Protein sidechain conformer prediction: a test of the energy function. (1998, background): “[27] report that ~6% of the > 40,000 residues studied had either χ1 or χ2 values that were not within ± 40° of any rotamer in their library.”