Numerical estimation of relaxation and diffusion distributions in two dimensions

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Numerical estimation of relaxation and diffusion distributions in two dimensions

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NMR processing:

MDD

NMR assignment:

Backbone:

Side-chains:

NOEs:

Structure from NMR restraints:

Ab initio:

Fragment-based:

Rosetta-NMR (Robetta)

Template-based:

GeNMR

I-TASSER

Refinement:

Amber

Structure from chemical shifts:

Fragment-based:

Homology-based:

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:

NMR model quality:

NOEs, other restraints:

Chemical shifts:

RDCs:

Pseudocontact shifts:

Protein geomtery:

SAVES2 or SAVES4

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:

Isotope labeling:

Solid-state NMR:

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03-09-2012, 09:16 AM

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Numerical estimation of relaxation and diffusion distributions in two dimensions

Numerical estimation of relaxation and diffusion distributions in two dimensions

Publication year: 2012
Source:Progress in Nuclear Magnetic Resonance Spectroscopy, Volume 62

J. Mitchell, T.C. Chandrasekera, L.F. Gladden

Graphical Abstract

Graphical abstract Highlights

?We review two-dimensional inversion methods for NMR data. ? Methods reviewed are applicable to relaxation time or diffusion data. ? Numerical methods for solving Fredholm integral equations of the first kind are given. ? Selection of optimum smoothing for Tikhonov regularisation is discussed. ? Tikhonov regularisation is found to provide the best results.

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