An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data.

An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Research Abstract Details 

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An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Abstract Text:

Ling-Hong Hung,Ram Samudrala,

The identification of proton contacts from NOE spectra remains the major bottleneck in NMR protein structure calculations. We describe an automated assignment-free system for deriving proton contact probabilities from NOESY peak lists that can be viewed as a quantitative extension of manual assignment techniques. Rather than assigning contacts to NOESY crosspeaks, a rigorous Bayesian methodology is used to transform initial proton contact probabilities derived from a set of 2992 protein structures into posterior probabilities using the observed crosspeaks as evidence. Given a target protein, the Bayesian approach is used to derive probabilities for all possible proton contacts. We evaluated the accuracy of this approach at predicting proton contacts on 60 (15)N separated NOESY and (13)C separated NOESY datasets simulated from experimentally determined NMR structures and compared it to CYANA, an established method for proton constraint assignment. On average, at the highest confidence level, our method accurately identifies 3.16/3.17 long range contacts per residue and 12.11/12.18 interresidue proton contacts per residue. These accuracies represent a significant increase over the performance of CYANA on the same data set. On a difficult real dataset that is publicly available, the coverage is lower but our method retains its advantage in accuracy over CANDID/CYANA. The algorithm is publicly available via the Protinfo NMR webserver http://protinfo.compbio.washington.edu/protinfo_nmr .

An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Publishing Authors By Initials

LH Hung,R Samudrala,

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An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Journal of biomolecular NMR

VOLUME: 36

Page Numbers: 189-98

Journal Abbreviation: J. Biomol. NMR

ISSN: 0925-2738

DAY: 3

MONTH: 10

YEAR: 2006

An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 9110829

An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Keywords Mesh Terms:

KEYWORDS: Proteins

MESH TERMS: chemistry

Chemical & Substance for Abstract: An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data. Information

Substance Name: Nitrogen

Registry Number: 7727-37-9

Grant and Affiliation Information for An automated assignment-free Bayesian approach for accurately identifying proton contacts from NOESY data.

AFFILIATION: Department of Microbiology, University of Washington, Rosen Building, 960 Republican, Seattle, WA 98109, USA.

Country: Netherlands

AGENCY: United States NIGMS

GRANT: GM068152-01

ACRONYM: GM

MEDLINETA: J Biomol NMR

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