RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation

By W. Sheffler, David Baker. 2008

140 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Protein sequence design with a learned potential (2022, methodology): “…of interest for the designed sequences, we use the following three metrics: (1) packstat, a non-deterministic measure of tight core residue packing 59 , (2) exposed hydrophobics, which calculates the solvent-accessible surface area (SASA) of hydrophobic residues 60 , and (3) counts of buried…”
Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, neutral): “…on feature analysis: for example, the derivation of low-resolution (Simons et al., 1999) and high-resolution (Kortemme et al., 2003) knowledge-based potentials, and the analysis of core-packing quality (Sheffler & Baker, 2009) and surface hydrophobic patches (Jacak, Leaver-Fay, & Kuhlman, 2012).”
AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences (2015, methodology): “…of the design simulation we filter antibody structure models using four parameters: predicted binding energy, buried surface area, packing quality between the antibody’s variable light and heavy domains and the bound ligand,59 and shape complementary58 between the antibody and bound ligand (Fig.”
A New Generation of Crystallographic Validation Tools for the Protein Data Bank (2011, methodology): “Also plotted are the two components that make up RosettaHoles2, a regression based S RESL score that detects small ﬂaws correlated with X-ray resolution, and a discrimina-tion-based S DECOY score that detects major ﬂaws not typically observed in crystal structures of any resolution.”
Protein sequence design with a learned potential (2020, methodology): “…of interest for the designed sequences we use the following three metrics: (1) packstat, a non-deterministic measure of tight core residue packing [106], (2) exposed hydrophobics, which calculates the solvent-accessible surface area (SASA) of hydrophobic residues [73], and (3) counts of buried…”
Recent advances in de novo protein design: Principles, methods, and applications (2021, unknown): “To further eliminate flawed designs, the results from Monte Carlo samplers are often filtered by a set of properties such as core packing (93) and hydrogen bond satisfaction (32) (Fig.”
De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures (2017, background): “6.(13) These 31,093 trajectories converged to only 9 unique sequences.”
Structure prediction for CASP8 with all-atom refinement using Rosetta (2009, background): “The lowest energy models with good hydrophobic burial and packing, as assessed by RosettaHoles,18 were submitted.”
Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand (2015, unknown): “Specifically, the ratio of Rosetta binding energy to Solvent Accessible Surface Area (SASA), the total SASA, the Rosetta Hydrogen bonding energy, the number of residues in the complete protein and at the protein ligand interface, and the packing statistic [24].”
De Novo Enzyme Design Using Rosetta3 (2011, background): “contacts between residues that are far away in sequence, for both whole protein/catalytic residues) -score across the interface between protein/ligand -packstat [13] of the designed structure with and without ligand present if the option -out:file:o ,filename.”
Computational design of a symmetric homodimer using β-strand assembly (2011, methodology): “We then calculated additional metrics including interface energy density (ΔGbind∕SASA), RosettaHoles score (33), and number of buried-unsatisfied at the interface.”
Computational design of environmental sensors for the potent opioid fentanyl (2017, methodology): “Docking was restricted to binding pockets identified by preexisting ligands in the crystal structure, or by RosettaHoles (Sheffler and Baker, 2009), which was used to define a position file of residues in the pocket.”
Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, unknown): “and packing errors in protein structures (88).”
RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite (2011, methodology): “Finally, the extent to which the core of the structure is well-packed is evaluated and filtered by Rosetta-Holes[14] using the PackStat filter.”
A de novo protein binding pair by computational design and directed evolution (2011, background): “Despite this difference, the quality of packing around this residue is equally good in both the design model and the crystal structure, whether assessed via interaction energy or other complementary metrics (Sheffler and Baker, 2009).”
Recent developments in phasing and structure refinement for macromolecular crystallography (2009, methodology): “Recently, validation criteria have been developed for carbohydrates [60] and for RNA backbone [61], as well as a promising new evaluation of under-packing in proteins [ 62 ].”