Mass spectrometry (MS) is a fundamental technology of analytical chemistry for measuring the structure of molecules, with many application fields such as clinical biomarker analysis or pharmacokinetics. In the context of proteomic analysis with MS, the superposition of the isotopic patterns of different proteins, in various charge-states produces MS spectra difficult to decipher. The complexity of the pattern models and the large size of the data again increase the difficulty of the analysis step. In this paper, we propose to formulate the problem of proteins characterization as the estimation of a positive-valued sparse signal thanks to a dictionary-based approach relying on the protein averagine concept. A proximal primal-dual splitting convex optimization method is considered to solve the resulting variational problem. Moreover, the large size of the dictionary matrix is circumvented by proposing a suitable block circulant approximation of it, allowing to limit the computational burden of the method. Numerical experiments on synthetic and real MS datasets illustrate the good performance of our approach.

UNLABELLED: The development of NMR in structural proteomics requires the availability of automatic structure determination methods. Many researchers are commonly confronted with the lack of raw datasets during the validation step of such methods. In order to increase test possibilities, the NMRb web-site offers a database of NMR raw datasets, ordered by spectral characteristics. AVAILABILITY: NMRb is available from: http://nmrb.cbs.cnrs.fr. SUPPLEMENTARY INFORMATION: General organization of NMRb figure, relational model organization, and XML structure files are available from http://nmrb.cbs.cnrs.fr/nmrb-doc.html.

MOTIVATION: Peptide and protein structures are determined daily using NMR spectroscopy. Assignment of the NMR spectra is an important step within the procedure and is usually the limiting one. Computer-aided assignment tools should be user friendly with open architecture to communicate with other programs involved in the structure determination. RESULTS: Here we present an interactive NMR assignment module which provides numerous graphic tools for the user. The module is composed of a database management system-handling peaks, spins and spin-systems. The assignment information is maintained as a set of interrelated associative arrays, which serve as generic high-level data structures. The module is developed in the macro language embedded in the Gifa NMR processing program (Pons et al. , J. Biomol. NMR, 8 , 445-452, 1996). This provides the user with a consistent interface, a set of sophisticated tools, and an easily extendible and customizable environment. AVAILABILITY: The program is available on request from the authors. The Gifa package can be accessed at: ((http://www.cbs. univ-montp1.fr/GIFA)) CONTACT: [email protected]