NMR paper Minimizing Pervasive Artifacts in 4D Covariance Maps for Protein Side Chain NMR Assignments

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[NMR paper] Minimizing Pervasive Artifacts in 4D Covariance Maps for Protein Side Chain NMR Assignments

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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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09-14-2021, 12:38 AM

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Minimizing Pervasive Artifacts in 4D Covariance Maps for Protein Side Chain NMR Assignments

Minimizing Pervasive Artifacts in 4D Covariance Maps for Protein Side Chain NMR Assignments

Nuclear magnetic resonance (NMR) is a mainstay of biophysical studies that provides atomic level readouts to formulate molecular mechanisms. Side chains are particularly important to derive mechanisms involving proteins as they carry functional groups, but NMR studies of side chains are often limited by challenges in assigning their signals. Here, we designed a novel computational method that combines spectral derivatives and matrix square-rooting to produce reliable 4D covariance maps from...

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