MOTIVATION: The HHsearch algorithm, implementing a hidden Markov model (HMM)-HMM alignment method, has shown excellent alignment performance in the so-called twilight zone (target-template sequence identity with ∼20%). However, an optimal alignment by HHsearch may contain small to large errors, leading to poor structure prediction if these errors are located in important structural elements. RESULTS: HHalign-Kbest server runs a full pipeline, from the generation of suboptimal HMM-HMM alignments to the evaluation of the best structural models. In the HHsearch framework, it implements a novel algorithm capable of generating k-best HMM-HMM suboptimal alignments rather than only the optimal one. For large proteins, a directed acyclic graph-based implementation reduces drastically the memory usage. Improved alignments were systematically generated among the top k suboptimal alignments. To recognize them, corresponding structural models were systematically generated and evaluated with Qmean score. The method was benchmarked over 420 targets from the SCOP30 database. In the range of HHsearch probability of 20-99%, average quality of the models (TM-score) raised by 4.1-16.3% and 8.0-21.0% considering the top 1 and top 10 best models, respectively. AVAILABILITY AND IMPLEMENTATION: http://bioserv.rpbs.univ-paris-diderot.fr/services/HHalign-Kbest/ (source code and server). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Meaningful scores to assess protein structure similarity are essential to decipher protein structure and sequence evolution. The mining of the increasing number of protein structures requires fast and accurate similarity measures with statistical significance. Whereas numerous approaches have been proposed for protein domains as a whole, the focus is progressively moving to a more local level of structure analysis for which similarity measurement still remains without any satisfactory answer. RESULTS: We introduce a new score based on Binet-Cauchy kernel. It is normalized and bounded between 1-maximal similarity that implies exactly the same conformations for protein fragments-and -1-mirror image conformations, the unrelated conformations having a null mean score. This allows for the search of both similar and mirror conformations. In addition, such score addresses two major issue of the widely used root mean square deviation (RMSD). First, it achieves length independent statistics even for short fragments. Second, it shows better performance in the discrimination of medium range RMSD values. Being simpler and faster to compute than the RMSD, it also provides the means for large-scale mining of protein structures. AVAILABILITY AND IMPLEMENTATION: The computer software implementing the score is available at http://bioserv.rpbs.univ-paris-diderot.fr/BCscore/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary:The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters. Availability: The FAF-Drugs2 server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/FAF-Drugs/. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: For the biologist, running bioinformatics analyses involves a time-consuming management of data and tools. Users need support to organize their work, retrieve parameters and reproduce their analyses. They also need to be able to combine their analytic tools using a safe data flow software mechanism. Finally, given that scientific tools can be difficult to install, it is particularly helpful for biologists to be able to use these tools through a web user interface. However, providing a web interface for a set of tools raises the problem that a single web portal cannot offer all the existing and possible services: it is the user, again, who has to cope with data copy among a number of different services. A framework enabling portal administrators to build a network of cooperating services would therefore clearly be beneficial. RESULTS: We have designed a system, Mobyle, to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software. While being focused on the end user, the Mobyle system also addresses the need, for the bioinfomatician, to automate remote services execution: PlayMOBY is a companion tool that automates the publication of BioMOBY web services, using Mobyle program definitions. AVAILABILITY: The Mobyle system is distributed under the terms of the GNU GPLv2 on the project web site (http://bioweb2.pasteur.fr/projects/mobyle/). It is already deployed on three servers: http://mobyle.pasteur.fr, http://mobyle.rpbs.univ-paris-diderot.fr and http://lipm-bioinfo.toulouse.inra.fr/Mobyle. The PlayMOBY companion is distributed under the terms of the CeCILL license, and is available at http://lipm-bioinfo.toulouse.inra.fr/biomoby/PlayMOBY/.

BACKGROUND: Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces. RESULTS: Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster. CONCLUSION: Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.

BACKGROUND: Virtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community. RESULTS: We introduce a new approach (LigCSRre) to the 3D ligand similarity search of drug candidates. It combines a 3D maximum common substructure search algorithm independent on atom order with a tunable description of atomic compatibilities to prune the search and increase its physico-chemical relevance. We show, on 47 experimentally validated active compounds across five protein targets having different specificities, that for single compound search, the approach is able to recover on average 52% of the co-actives in the top 1% of the ranked list which is better than gold standards of the field. Moreover, the combination of several runs on a single protein target using different query active compounds shows a remarkable improvement in enrichment. Such Results demonstrate LigCSRre as a valuable tool for ligand-based screening. CONCLUSION: LigCSRre constitutes a new efficient and generic approach to the 3D similarity screening of small compounds, whose flexible design opens the door to many enhancements. The program is freely available to the academics for non-profit research at: http://bioserv.rpbs.univ-paris-diderot.fr/LigCSRre.html.

We present a semantic map of resources for structural bioinformatics applied to proteins, i.e., various methods to predict and analyze protein structures in silico. Our map depicts resources on two levels: a logical level that provides a high-level description of the scientific concepts using a domain ontology; a physical level, that describes the actual resources implementing these connections. Scientists can use our system to express a query that captures their scientific aim, and are guided to identify the resources best meeting their needs. It is intended to provide scientists a tool to register and share knowledge about the available services in this field. Our approach addresses the problem of semantic interoperability of scientific resources publicly available on the Web

UNLABELLED: CS-PSeq-Gen is a program derived from PSeq-Gen, designed to perform simulations of the evolution of protein sequences under the constraints of a reconstructed phylogeny. It also provides a basis for the investigation of the correlated evolution of sites. AVAILABILITY: http://condor.urbb.jussieu.fr/CS-PSeq-Gen.html

UNLABELLED: PredAcc is a tool for predicting the solvent accessibility of protein residues from the sequence at different relative accessibility levels (0-55%). The prediction rate varies between 70. 7% (for 25% relative accessibility) and 85.7% (for 0% relative accessibility). Amino acids are predicted in four categories: almost certainly hidden and almost certainly exposed with a given a posteriori prediction error, probably hidden and probably exposed otherwise. AVAILABILITY: http://condor.urbb.jussieu.fr/PredAccCfg.html CONTACT: [email protected]

We have developed a new algorithm 'Complete sentences compatibility' (CSC) which uses single and double digestion fragments to rapidly determine restriction maps of circular DNA. From possible combinations of fragments of each simple digestion, which we call 'sentences of decomposition', we construct a restriction map which combines the sentences while taking into account compatibility rules. The algorithm can also deal with experimental errors of fragment weight and can suggest solutions that account for non-readable bands (fragments of zero length or multiple bands) on the gel. Because experiments using pairs of restrictive enzymes often result in multiple solutions, a complementary algorithm tries to reduce the number of proposed solutions by establishing consensus maps. The restriction map construction algorithm was tested on real cases, some containing more than fifteen fragments. Execution times range from 1-10 s on an IBM PC compatible microcomputer.