The output of the four programs corresponds to pair- wise sequence–structure alignments in various formats. Furthermore, the scoring functions vary from one fold-recognition tool to another making comparison diﬃ- cult. Sequence identity may be of limited value for low level of sequence conservation (say 10–15%) or for short sequence alignment (below 70–80 residues). It is rather a crude estimate of the quality of the SA (and potentially of the diﬃculty for a subsequent modeling study). Furthermore, to increase speed, the front-end soft- ware used for fold recognition (e.g. FUGUE, SP3) takes advantage of the so-called ‘frozen-approximation’ (25). In order to circumvent these limitations, additional scor- ing functions are used to evaluate the selected structural alignments (13). A ﬁrst round of evaluation is performed, for all the SAs produced by the fold-recognition search. Sequence identity and T-COFFEE score are computed. Then the common cores shared by each template (experimental 3D structures) and the query protein (according to each SA) are extracted using TITO and are evaluated using the PKB potential of mean force (using side-chain centroid pair contacts; 14). MaxCluster (18) is used to identify common substructures within the aligned regions given a distance threshold (0.8 A˚ by default, in @TOME-2). The Hierarchical Clustering algorithm (by default in @TOME-2) is used and the average linkage method permits to calculate distance between nodes. Then, the 3D-Jury score (19) is used to evaluate the corresponding partial models. These scores are combined to compute a new consensus scoring function in order to select the 20 best SAs. For speed, only the latter are submitted to a second round of evaluation. Using SCWRL 3.0 (15), sub- stituted side chains are built on a ﬁxed backbone. Strictly conserved residues are constrained during this conforma- tional search. The corresponding models of the 3D protein cores are evaluated using the software Verify3D. Finally, a new consensus scoring function is computed by @TOME- 2. The scoring scheme is currently based on a heuristic function (see online documentation). By default, the best 20 SAs (according to @TOME-2 ranking score) are displayed on the result page with a color scheme (helix in red, strands in blue and loops in green, while for the strictly conserved residues in loops are shown in black) corresponding to secondary structures assigned using P-SEA (26). They are all gathered in a multiple alignment (or a set of multiple alignments) with- out changing the original pairwise alignments while pro- pagating the necessary gaps. Upon completion of this background sequence– structure comparison, an overview of the results should indicate whether a set of signiﬁcantly similar structures has been detected or not. In the former case, molecular modeling and ligand docking may be reasonably envi- sioned and be obtained through the Web interface. If no consensus is highlighted, alternative clustering, SA editing or modeling are required to potentially establish more subtle similarities. These tasks can be performed interac- tively with no need to manually change the data format. When necessary, these tasks are carried out automatically Nucleic Acids Research, 2009, Vol. 37, Web Server issue W487 using dedicated scripts or through the server PAT (27) and openBabel (http://openbabel.sf.net). Interactive functionalities The main functionalities of @TOME-2 can be accessed through the menu below the results page. This requires selecting some SAs, and, if neccesary ligands. Selection is done by clicking boxes on the left side of each SA or on the left side of the line describing the corresponding ligand. The displayed SAs can be re-ranked according to any scoring function including the original alignment score from Psi-BLAST, Fugue, HHsearch and SP3 or the sequence identity. Another set of SAs (e.g. those cor- responding to protein–ligand complexes) can be selected and displayed using the left-panel menu. The necessary model building and structural evaluations are performed immediately. Similarly, the coloring scheme can be chan- ged (e.g. to highlight residues in contact with selected ligands). SA editing is the ﬁrst task proposed in the main menu. It sends the selected SAs to a JAVA editor, JalView (12) or to automatic tools for multiple sequence alignments. This corresponds to a rather expert use, especially in the case of manual editing. The T-COFFEE coloring scheme may help in identiﬁying locally incorrect alignment. This may also guide selection of complementary SAs to be com- bined at the step of model building (see below). Clustering by structure–structure comparisons can be re-run using other thresholds (0.6 A˚–1.0 A˚) and/or various sets of SAs. This may help gathering more distantly related structures using a slighly looser threshold (1.0 A˚ instead of 0.8 A˚ ). This would validate the recognition of a global fold or super-secondary structure type. Using a tighter threshold may indicate some structural variation among the detected templates or protein subfamily. This can lead to focused selection and reﬁned molecular mod- eling. Organization in domains with potential conforma- tional changes should be taken into account while using this tool. Obviously, convincing SAs should prompt the user to run MODELLER on several of the best templates either separately or in combination (using the multi-template option). Alternatively, extensive modeling may be per- formed to evaluate each selected SAs. Automatic building of valid 3D models (according to MODELLER energy and Verify3D score) would suggest that correct protein similarities have been detected at the fold-recognition step. This round of 3D-model building may serve to select the best possible templates. Then, the latter may be used in combination (as a multiple template) or provide a starting multiple SA to be edited and reﬁned (see below). Several models (up to 4) can be built for each template at each run. The best energy (or Verify3D score) is written in the main result page. The corresponding link brings the user to a speciﬁc result page containing further model evaluations, details on the modeling run and a model visualization module using the Jmol applet (http://www.jmol.org). @TOME-2 integrates an original interface to compar- ative docking of small molecules that are detected in the