NMR paper Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data.

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[NMR paper] Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data.

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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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Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data.

Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data.

Related Articles Multimodality characterization of microstructure by the combination of diffusion NMR and time-domain diffuse optical data.

Phys Med Biol. 2014 May 1;59(11):2639-2658

Authors: Proverbio A, Siow BM, Lythgoe MF, Alexander DC, Gibson AP

Abstract
Combining datasets with a model of the underlying physics prior to mapping of tissue provides a novel approach improving the estimation of parameters. We demonstrate this approach by merging near infrared diffuse optical signal data with diffusion NMR data to inform a model describing the microstructure of a sample. The study is conducted on a homogeneous emulsion of oil in a dispersion medium of water and proteins. The use of a protein based background, rich in collagen, introduces a similarity to real tissues compared to other models such as intralipids. The sample is investigated with the two modalities separately. Then, the two datasets are used to inform a combined model, and to estimate the size of the microstructural elements and the volume fraction. The combined model fits the microstructural properties by minimizing the difference between experimental and modelled data. The experimental results are validated with confocal laser scanning microscopy. The final results demonstrate that the combined model provides improved estimates of microstructural parameters compared to either individual model alone.

PMID: 24786607 [PubMed - as supplied by publisher]

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