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NOEs:
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UNIO Candid
ASDP
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Ab initio:
GeNMR
Cyana
XPLOR-NIH
ASDP
UNIO ATNOS-Candid
UNIO Candid
Fragment-based:
BMRB CS-Rosetta
Rosetta-NMR (Robetta)
Template-based:
GeNMR
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Refinement:
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Structure from chemical shifts:
Fragment-based:
WeNMR CS-Rosetta
BMRB CS-Rosetta
Homology-based:
CS23D
Simshift
Torsion angles from chemical shifts:
Preditor
TALOS
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Secondary structure from chemical shifts:
CSI (via RCI server)
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MICS caps, β-turns
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Flexibility from chemical shifts:
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Methyl S2
B-factor
Molecular dynamics:
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Chemical shifts prediction:
From structure:
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Sparta+
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CH3shift- Methyl
ArShift- Aromatic
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Proshift
PPM
CheShift-2- Cα
From sequence:
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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:
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Protein solubility:
camLILA
ccSOL
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camGroEL
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Isotope labeling:
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Default Error tolerant NMR backbone resonance assignment and automated structure generation.

Error tolerant NMR backbone resonance assignment and automated structure generation.

Error tolerant NMR backbone resonance assignment and automated structure generation.

J Bioinform Comput Biol. 2011 Feb;9(1):15-41

Authors: Alipanahi B, Gao X, Karakoc E, Li SC, Balbach F, Feng G, Donaldson L, Li M

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.

PMID: 21328705 [PubMed - in process]



Source: PubMed
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