MOTIVATION: Nowadays, virtual screening (VS) plays a major role in the process of drug development. Nonetheless, an accurate estimation of binding affinities, which is crucial at all stages, is not trivial and may require target-specific fine-tuning. Furthermore, drug design also requires improved predictions for putative secondary targets among which is Estrogen Receptor alpha (ERα). RESULTS: VS based on combinations of Structure-Based VS (SBVS) and Ligand-Based VS (LBVS) is gaining momentum to improve VS performances. In this study, we propose an integrated approach using ligand docking on multiple structural ensembles to reflect receptor flexibility. Then, we investigate the impact of the two different types of features (structure-based and ligand molecular descriptors) on affinity predictions using a random forest algorithm. We find that ligand-based features have lower predictive power (rP = 0.69, R2 = 0.47) than structure-based features (rP = 0.78, R2 = 0.60). Their combination maintains high accuracy (rP = 0.73, R2 = 0.50) on the internal test set, but it shows superior robustness on external datasets. Further improvement and extending the training dataset to include xenobiotics, leads to a novel high-throughput affinity prediction method for ERα ligands (rP = 0.85, R2 = 0.71). The presented prediction tool is provided to the community as a dedicated satellite of the @TOME server in which one can upload a ligand dataset in mol2 format and get ligand docked and affinity predicted. AVAILABILITY AND IMPLEMENTATION: http://edmon.cbs.cnrs.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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]