MOTIVATION: The toroidal nucleus is a novel chromosomal instability (CIN) biomarker which complements the micronucleus. Understanding the specific biological stresses leading to the formation of each CIN-associated phenotype requires the evaluation of large panels of biological images collected from different genetic backgrounds and environmental conditions. However, the quantification of toroidal nuclei is currently a manual process which is unviable on a large scale. RESULTS: Here, we present QATS (QuAntification of Toroidal nuclei in biological imageS), a tool that automates the identification of toroidal nuclei, minimizing false positives while highly agreeing with the manual quantifications. Additionally, QATS identifies micronuclei for a convenient comparison of both CIN biomarkers. QATS is an open-source ImageJ plugin with a user-friendly interface that enables a wide scientific community to easily assess the frequency of CIN biomarkers for the determination of CIN levels in cellular models. AVAILABILITY AND IMPLEMENTATION: QATS is an ImageJ plugin freely available at http://www.toroidalnucleus.org/qats. The user manual and the images used for the evaluation of QATS are included in the website. Supplementary data are available at Bioinformatics online.

UNLABELLED: pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. AVAILABILITY AND IMPLEMENTATION: The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality. AVAILABILITY AND IMPLEMENTATION: The source code is released under GNU General Public License version 2 and can be downloaded from http://mmb.pcb.ub.es/~cpons/Cell-Dock. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces. RESULTS: Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved. CONCLUSIONS: The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.

MOTIVATION: Prediction of protein-protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein-protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current 'omics' world. AVAILABILITY: http://sbg.cib.csic.es/Software/FRODOCK/

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.

BACKGROUND: It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other). The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. RESULTS: For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. CONCLUSION: Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.