Consistent blind protein structure generation from NMR chemical shift data

By Yang Shen, Oliver F. Lange, Frank Delaglio, P. Rossi, J. Aramini, Gaohua Liu, A. Eletsky, Yibing Wu, K. Singarapu, A. Lemak and 6 more. 2008

838 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

1. David Baker
2. Torsten Schwede
3. Andrej Sali
4. Yang Zhang
5. Nikolaos G. Sgourakis
6. Santrupti Nerli
7. Ben M. Webb
8. Andriy Kryshtafovych

In Molecular Biology

1. David Baker
2. Torsten Schwede
3. Yang Zhang
4. Andrej Sali
5. Nikolaos G. Sgourakis
6. Santrupti Nerli
7. Jeffrey J. Gray
8. Ben M. Webb

Citations in the corpus

• Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, neutral): “CS-Rosetta CS-Rosetta 39 models calculated from the chemical shifts (Fig.”
• Outcome of a Workshop on Applications of Protein Models in Biomedical Research (2009, methodology): “Perhaps most promising are methods that can dramatically accelerate NMR-based structural inference, by bringing together limited chemical shift data with modeling techniques to achieve structures with near-atomic resolution (Cavalli et al., 2007; Shen et al., 2008).”
• One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, critical): “In several cases the local structure in the models was incorrect; unlike the chemical shift information used in CS-Rosetta,5,6 the contact information does not directly improve sequence based fragment selection.”
• Alternate states of proteins revealed by detailed energy landscape mapping (2010, positive): “Rosetta has already been used to determine 14 or to improve 15 entire NMR structures, but the further use suggested here would concentrate on individual mobile loops.”
• Integrative NMR for biomolecular research (2016, positive): “The chemical-shift-based method utilizes CS-Rosetta calculations (Shen et al. 2008) carried out on a server at BMRB (https://csrosetta.bmrb. wisc.edu/csrosetta) that employs the Condor (Thain et al. 2003) grid computing system (Fig.”
• Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, methodology): “Morerecently,Shen et al.showed the use of a modified fragment selection protocol in R OSETTA to generate structures of a quality comparable to those from traditional NMR structure determination methods ( 37 ).”
• Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints (2019, methodology): “This concept has then been extended with CS-Rosetta for the structure determination of larger proteins from backbone-only CSs and RDCs (Raman et al., 2010; Shen et al., 2008).”
• Three-dimensional protein fold determination from backbone amide pseudocontact shifts generated by lanthanide tags at multiple sites. (2013, methodology): “higher quality, as in the standard CS-Rosetta protocol (Shen et al., 2008).”
• Protein model discrimination using mutational sensitivity derived from deep sequencing. (2012, background): “When the top 10% of residues with the highest RankScores are excluded, the RankScore showed enhanced correlation ( 0.65) with residue depth, rather than with accessibility ( 0.52).”
• Mutational tipping points for switching protein folds and functions (2012, methodology): “Further, a 3D structure was calculated from these experimental chemical shifts using the CS-Rosetta algorithm (Shen et al., 2008).”
• Resolution-adapted recombination of structural features significantly improves sampling in restraint guided structure calculation (2011, positive): “Because of the manual labor involved (1) the work is less reproducible, which hinders rigorous method development and (2) considerable barriers need to be overcome for adoption by others.”