SUMMARY: We present the cpPredictor webserver that implements a novel template-based method for prediction of secondary structure of RNA. The method outperforms available prediction methods as it uses RNA structures of related molecules, either predicted or experimentally identified, as structural templates. The server aims at three major tasks: i) prediction of RNA secondary structures that are difficult to predict by available methods, ii) characterization of uncharacterized RNAs as compatible or incompatible with a chosen template structure and iii) an identification of the most relevant structure among different candidate structures of a single RNA ambiguously predicted by available methods. The web server is accompanied with a comprehensive documentation. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://cppredictor.elixir-czech.cz/. The source code of the cpPredictor algorithm is freely available from the webserver under the Apache License, Version 2.0.

Ligand-based Virtual screening became a standard in-silico complement to the wet laboratory screening in small molecules discovery. It utilizes prior knowledge about molecules to rank a set of candidate molecules with yet unknown activity. Virtual screening software is often implemented as a specialized screening tool or as a part of a drug-discovery suite where ligand-based screening is one of the available tools. There exist many approaches to virtual screening, but although there exist several free-to-use screening tools, these tools usually provide only single approach to screening or do not provide user with aggregated analyses of multiple (preferably state-of-the-art) screening approaches. Such a functionality would be useful since different screening approaches yield different ranking of the candidate molecules. To the best of our knowledge there is no freely available, easy-to-use, tool that would allow to apply multiple screening approaches to a dataset and subsequently facilitate integration and interpretation of the results. Here, we present the first version of such a tool called ViSeT, available at https://github.com/skodapetr/viset under the MIT licence. ViSeT is a web-based, extensible, ligand-based virtual screening tool supporting multiple screening approaches and subsequent analysis of the results.

BACKGROUND: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. RESULTS: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. CONCLUSION: In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.