NMR paper High-throughput 3D structural homology detection via NMR resonance assignment.

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[NMR paper] High-throughput 3D structural homology detection via NMR resonance assignment.

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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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11-24-2010, 09:25 PM

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High-throughput 3D structural homology detection via NMR resonance assignment.

High-throughput 3D structural homology detection via NMR resonance assignment.

Related Articles High-throughput 3D structural homology detection via NMR resonance assignment.

Proc IEEE Comput Syst Bioinform Conf. 2004;:278-89

Authors: Langmead CJ, Donald BR

One goal of the structural genomics initiative is the identification of new protein folds. Sequence-based structural homology prediction methods are an important means for prioritizing unknown proteins for structure determination. However, an important challenge remains: two highly dissimilar sequences can have similar folds & how can we detect this rapidly, in the context of structural genomics? High-throughput NMR experiments, coupled with novel algorithms for data analysis, can address this challenge. We report an automated procedure, called HD, for detecting 3D structural homologies from sparse, unassigned protein NMR data. Our method identifies 3D models in a protein structural database whose geometries best fit the unassigned experimental NMR data. HD does not use, and is thus not limited by sequence homology. The method can also be used to confirm or refute structural predictions made by other techniques such as protein threading or homology modelling. The algorithm runs in O(pn + pn(5/2) log (cn)+p log p) time, where p is the number of proteins in the database, n is the number of residues in the target protein and c is the maximum edge weight in an integer-weighted bipartite graph. Our experiments on real NMR data from 3 different proteins against a database of 4,500 representative folds demonstrate that the method identifies closely related protein folds, including sub-domains of larger proteins, with as little as 10-30% sequence homology between the target protein (or sub-domain) and the computed model. In particular, we report no false-negatives or false-positives despite significant percentages of missing experimental data.

PMID: 16448021 [PubMed - indexed for MEDLINE]

Source: PubMed

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