Accurate protein structure modeling using sparse NMR data and homologous structure information

By James M. Thompson, Nikolaos G. Sgourakis, Gaohua Liu, P. Rossi, Yuefeng Tang, Jeffrey L. Mills, Thomas Szyperski, Gaetano T. Montelione, David Baker. 2012

42 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. Woonghee Lee
2. John L. Markley
3. Gabriel Cornilescu
4. Santrupti Nerli
5. Jaime L. Stark
6. C. Petit
7. Julia Koehler Leman
8. Jeffrey J. Gray

In Molecular Biology

1. Woonghee Lee
2. John L. Markley
3. Santrupti Nerli
4. Nikolaos G. Sgourakis
5. Julia Koehler Leman
6. Gabriel Cornilescu
7. Jaime L. Stark
8. C. Petit

Citations in the corpus

• Computational modeling of membrane proteins (2014, background): “159 Another interesting idea is the RASREC protocol160 that resamples low-energy features from intermediate-stage models, thereby taking advantage of “good” already-built substructures that can be recycled and combined in later stages.”
• CS-ROSETTA (2018, methodology): “CS-HM-Rosetta (or Chemical Shift-Homology Modeling-Rosetta) (Thompson et al., 2012) is a protocol whose primary purpose is to generate restraints from homologous proteins that can be combined with NMR chemical shifts within main structure modeling methods (Abrelax and RASREC).”
• Chemical shift-based methods in NMR structure determination (2018, methodology): “The first method which combined comparative modeling algorithms with backbone and 13Cβ chemical shifts to derive consistently accurate models of protein structures is CS-HMRosetta (Chemical Shift-Homology Modeling-Rosetta) [104].”
• The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data (2016, methodology): “The CS-HM-Rosetta using 4D data has extended this approach to larger proteins (Thompson et al. 2012).”