Dataset Information

NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure.

ABSTRACT: MOTIVATION: A prerequisite for any protein study by NMR is the assignment of the resonances from the (15)N-(1)H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment strategy have the important drawbacks that they cannot guarantee a high assignment accuracy near to 100%. RESULTS: We propose here a new program, called NOEnet, implementing an efficient complete search algorithm that ensures the correctness of the assignment results. NOEnet exploits the network character of unambiguous NOE constraints to realize an exhaustive search of all matching possibilities of the NOE network onto the structural one. NOEnet has been successfully tested on EIN, a large protein of 28 kDa, using only NOE data. The complete search of NOEnet finds all possible assignments compatible with experimental data that can be defined as an assignment ensemble. We show that multiple assignment possibilities of large NOE networks are restricted to a small spatial assignment range (SAR), so that assignment ensembles, obtained from accessible experimental data, are precise enough to be used for functional proteins studies, like protein-ligand interaction or protein dynamics studies. We believe that NOEnet can become a major tool for the structure-based backbone resonance assignment strategy in NMR. AVAILABILITY: The NOEnet program will be available under: http://www.icsn.cnrs-gif.fr/download/nmr.

SUBMITTER: Stratmann D

PROVIDER: S-EPMC2642640 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure.

Stratmann Dirk D van Heijenoort Carine C Guittet Eric E

Bioinformatics (Oxford, England) 20081212 4

<h4>Motivation</h4>A prerequisite for any protein study by NMR is the assignment of the resonances from the (15)N-(1)H HSQC spectrum to their corresponding atoms of the protein backbone. Usually, this assignment is obtained by analyzing triple resonance NMR experiments. An alternative assignment strategy exploits the information given by an already available 3D structure of the same or a homologous protein. Up to now, the algorithms that have been developed around the structure-based assignment ...[more]

PMID: 19074506

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Project description:One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called NASCA: (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), NASCA: extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that NASCA: assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by NASCA: have backbone RMSD 0.8-1.5 Å from the reference structures determined by traditional NMR approaches.

Dataset Information

NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure.

Publications

NOEnet--use of NOE networks for NMR resonance assignment of proteins with known 3D structure.

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