De novo protein structure determination using sparse NMR data

By P. Bowers, Charles E.M. Strauss, David Baker. 2000

179 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Achievements and Challenges in Computational Protein Design. (2017, methodology): “Within RosettaDesign, finer grid searches were undertaken in the vicinity of these parameters, yieldingoptimizeddesigns.”
Pseudocontact Shift-Driven Iterative Resampling for 3D Structure Determinations of Large Proteins. (2016, methodology): “The Rosetta fragment assembly protocol employs Metropolis Monte Carlo assembly of nine-residue and three-residue fragments, which are generated using sequence and backbone diamagnetic chemical shift information of the target protein [39,40].”
Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “Bowers et al. showed that a sparse mixture of short-and long-range NOErestraints(approximatelyonerestraint per residue) in addition to the backbone chemical shifts enables R OSETTA to build models at atomic-detail accuracy ( 34 ).”
Modeling structurally variable regions in homologous proteins with rosetta (2004, methodology): “, random angle perturbation, “wobble” operations, and minimization), torsion angles are evaluated by using a secondary structure-dependent torsion potential.(38) This knowledge-based potential is derived from a nonredundant set of protein X-ray structures of 2.”
Rosetta FunFolDes – A general framework for the computational design of functional proteins (2018, methodology): “To check whether FunFolDes enhances sequence and structural sampling, we compared the simulations to constrained ab initio (cst-ab initio) simulations(Bowers et al., 2000).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “De novo backbones beyond helical bundles can be designed by a fragment assembly strategy originally used in structure prediction (35, 60).”
Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints (2019, methodology): “The original RosettaNMR method used backbone CSs to find structurally similar peptide fragments in the Protein Databank (PDB), which are assembled de novo guided by limited NOE distance restraints (Bowers et al., 2000).”
Consistent blind protein structure generation from NMR chemical shift data (2008, methodology): “H N or H N –CH 3 NOE interactions, which typically can easily be extracted from a 3D NOE data set on a perdeuterated protein sample with protonated amide and/or methyl protons, provides a considerable boost in this direction, and (12,17).”
Resolution-adapted recombination of structural features significantly improves sampling in restraint guided structure calculation (2011, unknown): “The individual communications are (1) (up) queuing of new batches (down) notification of finished results and underflow of the queue (2) (right) start jobs (left) report success/failure (3) write to file (4) lock/release files before reading with (6) to avoid competition with writing (5).”
Ab initio protein structure prediction: progress and prospects. (2001, methodology): “For example, quite accurate structures were produced by Rosetta in conjunction with NMR chemical shift data (to enhance fragment selection) and sparse NMR constraints (10).”
Local Protein Structures (2007, methodology): “Rosetta method has been extended to other protein modelling problems like de novo protein design [341, 342], protein-protein docking [343], protein modelling based on limited experimental data [344, 345] and loop modelling or modelling structurally variable regions in homologous proteins [346].”