Published online 13 May 2009 Nucleic Acids Research, 2009, Vol. 37, Web Server issue W485–W491 doi:10.1093/nar/gkp368 @TOME-2: a new pipeline for comparative modeling of protein–ligand complexes Jean-Luc Pons and Gilles Labesse* A.B.C.I.S., CNRS UMR5048—Universite´ s Montpellier 1 & Montpellier II and INSERM U554, 29, Rue de Navacelles 34090 Montpellier Cedex, France Received January 31, 2009; Revised April 17, 2009; Accepted April 24, 2009 ABSTRACT @TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural align- ment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also con- nected comparative docking of small ligands that is performed using protein–protein superposition. The input is a simple protein sequence in one- letter code with no comment. The resulting 3D model, protein–ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the func- tional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to high- light some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/ INTRODUCTION Sequence and structure comparisons are widely used to identify protein similarities and to derive functional or structural information (1). Indeed, the sequence–sequence software, Psi-BLAST (2), is now a standard for identifying protein homology (within ﬁve iterations by default). When various homologous sequence can be gathered, multiple alignments are also used to deﬁne conservation and deduce functional information. Distinct programs such as CLUSTALW (3), T-COFFEE (4) and MUSCLE (5) are available to perform this task rapidly and eﬃciently. Thanks to the gathering of protein 3D structures into available databases such as the PDB (6), sequence– structure relationships can be analysed to improve protein sequence comparisons. Remote similarities can be detected, routinely, using fold-recognition software such as FUGUE (7), SP3 (8) and many others. More recently, the eﬃciency of the proﬁle-based approach was greatly improved through proﬁle–proﬁle comparisons (9) or sim- ilar methods based on Hidden Markov-Model such as HHSEARCH (10). These tools take into account struc- tural information and/or protein evolution more accu- rately to provide better structural alignments (SAs). The latter can be evaluated and reﬁned using various software combining alignment editing and structure visualization such as ViTO (11) or JalView (12). Further validation and analysis of SAs can be obtained through molecular modeling. Crude and partial 3D models can be extracted from 3D templates using TITO (13) in order to validate the input SA with the potential of mean force PKB (14). Alternatively, side-chain conformations can be optimized on ﬁxed backbone using SCWRL (15). Complete model- ing is routinely obtained using MODELLER (16) for each compatible template separately, or using the multi- template option. These models can be evaluated using standard sequence–structure compatibility functions, such as Verify3D (17). Clustering of modeled 3D struc- tures using MaxCluster (18) and 3D-Jury (19) has been shown to eﬃciently highlight optimal 3D models or iden- tify similar regions in related templates. All these pro- grams (and many others) work eﬃciently, but their combination is often necessary in order to complete a predictive analysis. The use of multiple programs implies numerous data format changes, which tend to discourage or confuse many. Automatic procedures are now available to provide 3D models of good quality when the sequence identity is above 30% and databases of 3D models exist (1). Important functional prediction can be also performed at much lower level of sequence similarities in the so- called twilight zone (see examples below). Indeed, it was recently shown that threading or fold recognition can be used for functional annotation and ligand-docking predic- tions (20). However, at lower level of similarities, *To whom correspondence should be addressed. Tel: +33 4 67 41 77 12; Fax: +33 3 04 67 41 79 13; Email: labesse@cbs.cnrs.fr 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.