Computational approaches to the interpretation of NMR data for studying protein dynamics

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Computational approaches to the interpretation of NMR data for studying protein dynamics

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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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Computational approaches to the interpretation of NMR data for studying protein dynamics

Computational approaches to the interpretation of NMR data for studying protein dynamics

2 March 2012
Publication year: 2012
Source:Chemical Physics, Volume 396

Experimental studies of protein structure and dynamics with NMR provide the classical example of the power of theoretical approaches for the interpretation of experimental results. In this paper we review recent developments in experimental techniques extending the applicability of NMR to the study of protein structure and motion, and advances in the theoretical description.
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Highlights

? NMR data characterize protein motion on many timescales. ? Explicit motional models remain an important means to interpret NMR data remain important. ? Molecular dynamics simulations are increasingly valuable and ultimately more powerful. ? Extended or “accelerated” MD simulations can be directly compared with experimental data for longer timescales.

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