NMR assignments of sparsely labeled proteins using a genetic algorithm

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NMR assignments of sparsely labeled proteins using a genetic algorithm

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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

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NMR spectrum prediction:

FANDAS

MestReS

V-NMR

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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

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NMR sample preparation:

Protein disorder:

DisMeta

Protein solubility:

Isotope labeling:

Solid-state NMR:

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03-14-2017, 10:36 PM

nmrlearner

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NMR assignments of sparsely labeled proteins using a genetic algorithm

NMR assignments of sparsely labeled proteins using a genetic algorithm

Abstract

Sparse isotopic labeling of proteins for NMR studies using single types of amino acid (15N or 13C enriched) has several advantages. Resolution is enhanced by reducing numbers of resonances for large proteins, and isotopic labeling becomes economically feasible for glycoproteins that must be expressed in mammalian cells. However, without access to the traditional triple resonance strategies that require uniform isotopic labeling, NMR assignment of crosspeaks in heteronuclear single quantum coherence (HSQC) spectra is challenging. We present an alternative strategy which combines readily accessible NMR data with known protein domain structures. Based on the structures, chemical shifts are predicted, NOE cross-peak lists are generated, and residual dipolar couplings (RDCs) are calculated for each labeled site. Simulated data are then compared to measured values for a trial set of assignments and scored. A genetic algorithm uses the scores to search for an optimal pairing of HSQC crosspeaks with labeled sites. While none of the individual data types can give a definitive assignment for a particular site, their combination can in most cases. Four test proteins previously assigned using triple resonance methods and a sparsely labeled glycosylated protein, Robo1, previously assigned by manual analysis, are used to validate the method and develop a criterion for identifying sites assigned with high confidence.

Source: Journal of Biomolecular NMR

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