BioNMR
NMR aggregator & online community since 2003
BioNMR    
Learn or help to learn NMR - get free NMR books!
 

Go Back   BioNMR > Educational resources > Journal club
Advanced Search



Jobs Groups Conferences Literature Pulse sequences Software forums Programs Sample preps Web resources BioNMR issues


Webservers
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


Reply
Thread Tools Search this Thread Rate Thread Display Modes
  #1  
Unread 11-20-2016, 08:57 AM
nmrlearner's Avatar
Senior Member
 
Join Date: Jan 2005
Posts: 23,137
Points: 193,617, Level: 100
Points: 193,617, Level: 100 Points: 193,617, Level: 100 Points: 193,617, Level: 100
Level up: 0%, 0 Points needed
Level up: 0% Level up: 0% Level up: 0%
Activity: 50.7%
Activity: 50.7% Activity: 50.7% Activity: 50.7%
Last Achievements
Award-Showcase
NMR Credits: 0
NMR Points: 193,617
Downloads: 0
Uploads: 0
Default Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data

Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data

Abstract

Implementation of a new algorithm, SMILE, is described for reconstruction of non-uniformly sampled two-, three- and four-dimensional NMR data, which takes advantage of the known phases of the NMR spectrum and the exponential decay of underlying time domain signals. The method is very robust with respect to the chosen sampling protocol and, in its default mode, also extends the truncated time domain signals by a modest amount of non-sampled zeros. SMILE can likewise be used to extend conventional uniformly sampled data, as an effective multidimensional alternative to linear prediction. The program is provided as a plug-in to the widely used NMRPipe software suite, and can be used with default parameters for mainstream application, or with user control over the iterative process to possibly further improve reconstruction quality and to lower the demand on computational resources. For large data sets, the method is robust and demonstrated for sparsities down to ca 1%, and final all-real spectral sizes as large as 300Â*Gb. Comparison between fully sampled, conventionally processed spectra and randomly selected NUS subsets of this data shows that the reconstruction quality approaches the theoretical limit in terms of peak position fidelity and intensity. SMILE essentially removes the noise-like appearance associated with the point-spread function of signals that are a default of five-fold above the noise level, but impacts the actual thermal noise in the NMR spectra only minimally. Therefore, the appearance and interpretation of SMILE-reconstructed spectra is very similar to that of fully sampled spectra generated by Fourier transformation.



Source: Journal of Biomolecular NMR
Reply With Quote


Did you find this post helpful? Yes | No

Reply
Similar Threads
Thread Thread Starter Forum Replies Last Post
Accurate determination of rates from non-uniformly sampled relaxation data
Accurate determination of rates from non-uniformly sampled relaxation data Abstract The application of non-uniform sampling (NUS) to relaxation experiments traditionally used to characterize the fast internal motion of proteins is quantitatively examined. Experimentally acquired Poisson-gap sampled data reconstructed with iterative soft thresholding are compared to regular sequentially sampled (RSS) data. Using ubiquitin as a model system, it is shown that 25Â*% sampling is sufficient for the determination of quantitatively accurate relaxation...
nmrlearner Journal club 0 07-10-2016 10:50 AM
Analysis of non-uniformly sampled spectra with multi-dimensional decomposition
Analysis of non-uniformly sampled spectra with multi-dimensional decomposition October 2011 Publication year: 2011 Source:Progress in Nuclear Magnetic Resonance Spectroscopy, Volume 59, Issue 3</br> </br> </br> </br></br>
nmrlearner Journal club 0 12-15-2012 09:51 AM
Analysis of non-uniformly sampled spectra with multi-dimensional decomposition
Analysis of non-uniformly sampled spectra with multi-dimensional decomposition Publication year: 2011 Source:Progress in Nuclear Magnetic Resonance Spectroscopy, Volume 59, Issue 3</br> Vladislav Yu. Orekhov, Victor A. Jaravine</br> </br> </br></br>
nmrlearner Journal club 0 03-09-2012 09:16 AM
Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling
Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling Abstract The fast Fourier transformation has been the gold standard for transforming data from time to frequency domain in many spectroscopic methods, including NMR. While reliable, it has as a drawback that it requires a grid of uniformly sampled data points. This needs very long measuring times for sampling in multidimensional experiments in all indirect dimensions uniformly and even does not allow reaching optimal evolution times that would...
nmrlearner Journal club 0 02-16-2012 05:24 AM
Analysis of non-uniformly sampled spectra with Multi-Dimensional Decomposition
Analysis of non-uniformly sampled spectra with Multi-Dimensional Decomposition Publication year: 2011 Source: Progress in Nuclear Magnetic Resonance Spectroscopy, In Press, Accepted Manuscript, Available online 24 February 2011</br> Vladislav Yu., Orekhov , Victor A., Jaravine</br> More...
nmrlearner Journal club 0 02-26-2011 01:07 PM
FM reconstruction of non-uniformly sampled protein NMR data at higher dimensions and optimization by distillation
FM reconstruction of non-uniformly sampled protein NMR data at higher dimensions and optimization by distillation Abstract Non-uniform sampling (NUS) enables recording of multidimensional NMR data at resolutions matching the resolving power of modern instruments without using excessive measuring time. However, in order to obtain satisfying results, efficient reconstruction methods are needed. Here we describe an optimized version of the Forward Maximum entropy (FM) reconstruction method, which can reconstruct up to three indirect dimensions. For complex datasets, such as NOESY spectra,...
nmrlearner Journal club 0 01-09-2011 12:46 PM
[NMR paper] High-resolution iterative frequency identification for NMR as a general strategy for multidimensional data collection.
High-resolution iterative frequency identification for NMR as a general strategy for multidimensional data collection. Related Articles High-resolution iterative frequency identification for NMR as a general strategy for multidimensional data collection. J Am Chem Soc. 2005 Sep 14;127(36):12528-36 Authors: Eghbalnia HR, Bahrami A, Tonelli M, Hallenga K, Markley JL We describe a novel approach to the rapid collection and processing of multidimensional NMR data: "high-resolution iterative frequency identification for NMR" (HIFI-NMR). As with...
nmrlearner Journal club 0 12-01-2010 06:56 PM
Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR
Abstract Spectra obtained by application of multidimensional Fourier Transformation (MFT) to sparsely sampled nD NMR signals are usually corrupted due to missing data. In the present paper this phenomenon is investigated on simulations and experiments. An effective iterative algorithm for artifact suppression for sparse on-grid NMR data sets is discussed in detail. It includes automated peak recognition based on statistical methods. The results enable one to study NMR spectra of high dynamic range of peak intensities preserving benefits of random sampling, namely the superior resolution in...
nmrlearner Journal club 0 08-14-2010 04:19 AM


Thread Tools Search this Thread
Search this Thread:

Advanced Search
Display Modes Rate This Thread
Rate This Thread:

Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts

BB code is On
Smilies are On
[IMG] code is On
HTML code is On
Trackbacks are Off
Pingbacks are Off
Refbacks are Off



BioNMR advertisements to pay for website hosting and domain registration. Nobody does it for us.



Powered by vBulletin® Version 3.7.3
Copyright ©2000 - 2024, Jelsoft Enterprises Ltd.
Copyright, BioNMR.com, 2003-2013
Search Engine Friendly URLs by vBSEO 3.6.0

All times are GMT. The time now is 02:28 PM.


Map