W486 Nucleic Acids Research, 2009, Vol. 37, Web Server issue consensus methods are needed and several tools have to be used for each task necessary for the completion of predic- tive modeling. This requires tedious changes of data for- mats as most software use speciﬁc input and output formats. This signiﬁcantly slows down or limits an eﬃcient use of the above-mentioned programs. Modeling protein– ligand complexes remains an even more diﬃcult task. Template searches generally focus on sequence similarities rather than on the presence or not of a ligand bound to the template. Nevertheless, in most protein families, one tem- plate may provide the best protein scaﬀold, while another related template may provide a valid ligand (or a similar compound). Meanwhile, the lower the sequence conserva- tion, the lower the model quality limiting the functional insights which can be derived. This means that at the end of an extensive sequence similarity search and molecular modeling study, virtual docking is unlikely to produce valid results. This currently limits extensive protein func- tion annotations. Indeed, even the identiﬁcation of the potential active site might be complicated. Paradoxically, indicating a potential ligand or bio- logical activity may help in validating the proposed sequence similarities. Improving sequence–structure align- ment and simultaneously analysing interactions with potentially relevant ligands may help in reﬁning template selection, molecular modeling and function predictions. Connecting structure analyses and functional annotations is more and more necessary for genome analysis. This has been exempliﬁed previously even at very low level of sequence identity (10–15%; see examples below). These works relied on expertise and long-term sequence studies. The rapidly rising pace of genome sequencing requires improved capabilities for eﬃcient structure modeling and function prediction (20). A new server, named @TOME-2, has been developed to bridge comparative modeling and comparative docking in order to extend sequence analysis from fold recognition to functional annotation. Various features have been implemented and numerous programs interfaced to con- nect sequence with 3D structure and putative ligands. A former version of the fold recognition and comparative modeling pipeline was described previously (21), while a web server dedicated to the analysis of ATP-binding pock- ets of protein kinases, has been developed and used to validate the so-called comparative docking approach (22). In the new server @TOME2, state-of-the-art pro- grams for fold recognition, structural alignment evalua- tion and editing as well as comparative modeling have been connected. Comparative docking can be performed on-the-ﬂy for any selected model built and any small ligand detected among the aligned templates. Ligands are posed according to protein–protein superposition. The resulting complexes are evaluated using the scoring function of MedusaScore (23) and of AUTODOCK (24). Fold recognition and in-depth evaluation of structural alignments are ﬁrst performed in background. Supplemen- tary evaluation, modeling and docking tasks can be performed interactively. The new server is made freely accessible to the academic community. Several examples are described to illustrate the use and the performance of @TOME-2. METHODS @TOME-2 allows one to submit a simple amino-acid sequence to perform various protein-structure analysis and prediction (Figure 1). It is a pipeline written in Perl wrapping numerous databases and software developed by the bioinformatic community. Two major steps are currently available. The ﬁrst step is a calculation run in background, to optimally detect sequence–structure com- patibility. An e-mail is sent upon completion of the fold- recognition search and structural-alignment evaluation. The second step is an interactive reﬁnement procedure to further validate and exploit the detected similarities. In both cases, the results are displayed as an HTML page with clickable menus, selection boxes, links and embedded JAVA applets. Results can be also downloaded in various formats. Starting page The front page allows the user to ﬁll in three mandatory ﬁelds: a title, the primary sequence (in one-letter code) and an e-mail address (see below). Clickable options and menus allow one to select the required modules and the corresponding software (e.g. for fold recognition) or data- bases (e.g. ligand types for comparative docking). Speciﬁc options can be also selected (e.g. ‘molecular replacement’ module to be described elsewhere). Background calculations Currently, @TOME-2 performs fold recognition using four distinct tools dedicated to sequence–sequence (Psi-BLAST and HHsearch) or sequence–structure com- parisons (FUGUE and SP3). These four programs use diﬀerent algorithms and/or distinct databases. This allows consensus calculations to optimally detect compat- ible templates. The fold-recognition searches are now performed on our 17-node LINUX cluster. Figure 1. Data ﬂow in the pipeline @TOME-2. Most software are free to use for academic users although MODELLER required a license key.