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NMR processing:
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MARS
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PINE
Side-chains:
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NOEs:
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UNIO Candid
ASDP
Structure from NMR restraints:
Ab initio:
GeNMR
Cyana
XPLOR-NIH
ASDP
UNIO ATNOS-Candid
UNIO Candid
Fragment-based:
BMRB CS-Rosetta
Rosetta-NMR (Robetta)
Template-based:
GeNMR
I-TASSER
Refinement:
Amber
Structure from chemical shifts:
Fragment-based:
WeNMR CS-Rosetta
BMRB CS-Rosetta
Homology-based:
CS23D
Simshift
Torsion angles from chemical shifts:
Preditor
TALOS
Promega- Proline
Secondary structure from chemical shifts:
CSI (via RCI server)
TALOS
MICS caps, β-turns
d2D
PECAN
Flexibility from chemical shifts:
RCI
Interactions from chemical shifts:
HADDOCK
Chemical shifts re-referencing:
Shiftcor
UNIO Shiftinspector
LACS
CheckShift
RefDB
NMR model quality:
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Vasco
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RDCs:
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Pseudocontact shifts:
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What-If
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PSVS
MolProbity
SAVES2 or SAVES4
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MetaMQAPII
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Ramachandran Plot
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Verify_3D
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NMR spectrum prediction:
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Flexibility from structure:
Backbone S2
Methyl S2
B-factor
Molecular dynamics:
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Antechamber
Chemical shifts prediction:
From structure:
Shiftx2
Sparta+
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CH3shift- Methyl
ArShift- Aromatic
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Proshift
PPM
CheShift-2- Cα
From sequence:
Shifty
Camcoil
Poulsen_rc_CS
Disordered proteins:
MAXOCC
Format conversion & validation:
CCPN
From NMR-STAR 3.1
Validate NMR-STAR 3.1
NMR sample preparation:
Protein disorder:
DisMeta
Protein solubility:
camLILA
ccSOL
Camfold
camGroEL
Zyggregator
Isotope labeling:
UPLABEL
Solid-state NMR:
sedNMR


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Old 11-24-2010, 09:16 PM
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Default Protein structural class identification directly from NMR spectra using averaged chem

Protein structural class identification directly from NMR spectra using averaged chemical shifts.

Related Articles Protein structural class identification directly from NMR spectra using averaged chemical shifts.

Bioinformatics. 2003 Nov 1;19(16):2054-64

Authors: Mielke SP, Krishnan VV

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.

PMID: 14594710 [PubMed - indexed for MEDLINE]



Source: PubMed
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