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

Abstract

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 .