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NMR processing:
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NMR assignment:
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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
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Promega- Proline
Secondary structure from chemical shifts:
CSI (via RCI server)
TALOS
MICS caps, β-turns
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PECAN
Flexibility from chemical shifts:
RCI
Interactions from chemical shifts:
HADDOCK
Chemical shifts re-referencing:
Shiftcor
UNIO Shiftinspector
LACS
CheckShift
RefDB
NMR model quality:
NOEs, other restraints:
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RPF scores
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Chemical shifts:
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Vasco
iCing
RDCs:
DC
Anisofit
Pseudocontact shifts:
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Protein geomtery:
Resolution-by-Proxy
PROSESS
What-If
iCing
PSVS
MolProbity
SAVES2 or SAVES4
Vadar
Prosa
ProQ
MetaMQAPII
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Ramachandran Plot
Rampage
ERRAT
Verify_3D
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NMR spectrum prediction:
FANDAS
MestReS
V-NMR
Flexibility from structure:
Backbone S2
Methyl S2
B-factor
Molecular dynamics:
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Antechamber
Chemical shifts prediction:
From structure:
Shiftx2
Sparta+
Camshift
CH3shift- Methyl
ArShift- Aromatic
ShiftS
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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Unread 08-14-2010, 04:19 AM
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Default Detection of unrealistic molecular environments in protein structures based on expect

Abstract Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at.
  • Content Type Journal Article
  • DOI 10.1007/s10858-010-9408-x
  • Authors
    • Simon W. Ginzinger, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria
    • Christian X. Weichenberger, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria
    • Manfred J. Sippl, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria

Source: Journal of Biomolecular NMR
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