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NMR processing:
MDD
NMR assignment:
Backbone:
Autoassign
MARS
UNIO Match
PINE
Side-chains:
UNIO ATNOS-Ascan
NOEs:
UNIO ATNOS-Candid
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:
NOEs, other restraints:
PROSESS
PSVS
RPF scores
iCing
Chemical shifts:
PROSESS
CheShift2
Vasco
iCing
RDCs:
DC
Anisofit
Pseudocontact shifts:
Anisofit
Protein geomtery:
Resolution-by-Proxy
PROSESS
What-If
iCing
PSVS
MolProbity
SAVES2 or SAVES4
Vadar
Prosa
ProQ
MetaMQAPII
PSQS
Eval123D
STAN
Ramachandran Plot
Rampage
ERRAT
Verify_3D
Harmony
Quality Control Check
NMR spectrum prediction:
FANDAS
MestReS
V-NMR
Flexibility from structure:
Backbone S2
Methyl S2
B-factor
Molecular dynamics:
Gromacs
Amber
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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Old 09-04-2021, 10:34 AM
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Default DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained...

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