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 03-25-2015, 10:58 PM
nmrlearner's Avatar
Senior Member
 
Join Date: Jan 2005
Posts: 23,134
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 Efficient and generalized processing of multidimensional NUS NMR data: the NESTA algorithm and comparison of regularization terms

Efficient and generalized processing of multidimensional NUS NMR data: the NESTA algorithm and comparison of regularization terms

Abstract

The advantages of non-uniform sampling (NUS) in offering time savings and resolution enhancement in NMR experiments have been increasingly recognized. The possibility of sensitivity gain by NUS has also been demonstrated. Application of NUS to multidimensional NMR experiments requires the selection of a sampling scheme and a reconstruction scheme to generate uniformly sampled time domain data. In this report, an efficient reconstruction scheme is presented and used to evaluate a range of regularization algorithms that collectively yield a generalized solution to processing NUS data in multidimensional NMR experiments. We compare l1-norm (L1), iterative re-weighted l1-norm (IRL1), and Gaussian smoothed l0-norm (Gaussian-SL0) regularization for processing multidimensional NUS NMR data. Based on the reconstruction of different multidimensional NUS NMR data sets, L1 is demonstrated to be a fast and accurate reconstruction method for both quantitative, high dynamic range applications (e.g. NOESY) and for all J-coupled correlation experiments. Compared to L1, both IRL1 and Gaussian-SL0 are shown to produce slightly higher quality reconstructions with improved linearity in peak intensities, albeit with a computational cost. Finally, a generalized processing system, NESTA-NMR, is described that utilizes a fast and accurate first-order gradient descent algorithm (NESTA) recently developed in the compressed sensing field. NESTA-NMR incorporates L1, IRL1, and Gaussian-SL0 regularization. NESTA-NMR is demonstrated to provide an efficient, streamlined approach to handling all types of multidimensional NMR data using proteins ranging in size from 8 to 32Â*kDa.



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
Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR
Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR Publication date: Available online 13 October 2014 Source:Progress in Nuclear Magnetic Resonance Spectroscopy</br> Author(s): Mehdi Mobli , Jeffrey C. Hoch</br> Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (the free induction decay, FID) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform (DFT), the intervals are kept...
nmrlearner Journal club 0 10-14-2014 04:35 AM
[NMR paper] A comparison of convex and non-convex compressed sensing applied to multidimensional NMR.
A comparison of convex and non-convex compressed sensing applied to multidimensional NMR. http://www.bionmr.com//www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--linkinghub.elsevier.com-ihub-images-PubMedLink.gif Related Articles A comparison of convex and non-convex compressed sensing applied to multidimensional NMR. J Magn Reson. 2012 Oct;223:1-10 Authors: Kazimierczuk K, Orekhov VY Abstract The resolution of multidimensional NMR spectra can be severely limited when regular sampling based on the Nyquist-Shannon theorem is used. The...
nmrlearner Journal club 0 02-23-2013 01:51 PM
[NMR paper] An efficient branch-and-bound algorithm for the assignment of protein backbone NMR pe
An efficient branch-and-bound algorithm for the assignment of protein backbone NMR peaks. Related Articles An efficient branch-and-bound algorithm for the assignment of protein backbone NMR peaks. Proc IEEE Comput Soc Bioinform Conf. 2002;1:165-74 Authors: Lin G, Xu D, Chen ZZ, Jiang T, Wen J, Xu Y NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and...
nmrlearner Journal club 0 11-24-2010 08:49 PM
[NMRpipe Yahoo group] processing 2D data
processing 2D data Hi NMRPipe users, I am yasin new user of nmrpipe. I have few problems while processing the 2D data to pipe. 1> when topspin 2.1 phase values kept in nmrpipe we More...
NMRpipe Yahoo group news News from other NMR forums 0 11-03-2010 11:27 AM
[NMR paper] A branch and bound algorithm for protein structure refinement from sparse NMR data se
A branch and bound algorithm for protein structure refinement from sparse NMR data sets. http://www.ncbi.nlm.nih.gov/corehtml/query/egifs/http:--linkinghub.elsevier.com-ihub-images-PubMedLink.gif Related Articles A branch and bound algorithm for protein structure refinement from sparse NMR data sets. J Mol Biol. 1999 Jan 29;285(4):1691-710 Authors: Standley DM, Eyrich VA, Felts AK, Friesner RA, McDermott AE We describe new methods for predicting protein tertiary structures to low resolution given the specification of secondary structure and a...
nmrlearner Journal club 0 08-21-2010 04:03 PM
NMR Acronyms, Abbreviations, and Terms
NMR Acronyms, Abbreviations, and Terms More...
nmrlearner NMR bookmarks 0 08-19-2010 02:34 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
Automated error-tolerant macromolecular structure determination from multidimensional nuclear Overhauser enhancement spectra and chemical shift assignments: improved robustness and performance of the PASD algorithm
Automated error-tolerant macromolecular structure determination from multidimensional nuclear Overhauser enhancement spectra and chemical shift assignments: improved robustness and performance of the PASD algorithm John J. Kuszewski, Robin Augustine Thottungal, G. Marius Clore and Charles D. Schwieters Journal of Biomolecular NMR; 2008; 41(4); pp 221-239 Abstract: We report substantial improvements to the previously introduced automated NOE assignment and structure determination protocol known as PASD (Kuszewski et al. (2004) J Am Chem Soc 26:6258–6273). The improved protocol includes...
Abe Journal club 0 09-21-2008 11:43 PM


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 02:22 PM.


Map