MOTIVATION: Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD. However, within an automated virtual screening workflow it is not feasible to use the GUI in between every step to change the GOLD configuration file. Thus, a python module called PyGOLD was developed, to parse, edit and write the GOLD configuration file and to automate docking based virtual screening workflows. AVAILABILITY AND IMPLEMENTATION: The latest version of PyGOLD, its documentation and example scripts are available at: http://www.ccb.tu-dortmund.de/koch or http://www.agkoch.de. PyGOLD is implemented in Python and can be imported as a standard python module without any further dependencies. CONTACT: [email protected], [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Conserved water molecules play a crucial role in protein structure, stabilization of secondary structure, protein activity, flexibility and ligand binding. Clustering of water molecules in superimposed protein structures, obtained by X-ray crystallography at high resolution, is an established method to identify consensus water molecules in all known protein structures of the same family. PyWATER is an easy-to-use PyMOL plug-in and identifies conserved water molecules in the protein structure of interest. PyWATER can be installed via the user interface of PyMOL. No programming or command-line knowledge is required for its use. AVAILABILITY AND IMPLEMENTATION: PyWATER and a tutorial are available at https://github.com/hiteshpatel379/PyWATER. PyMOL is available at http://www.pymol.org/ or http://sourceforge.net/projects/pymol/. CONTACT: [email protected].

MOTIVATION: Specific information on newly discovered proteins is often difficult to find in literature. Particularly if only sequences and no common names of proteins or genes are available, preceding sequence similarity searches can be crucial for the process of information collection. In drug research, it is important to know whether a small molecule targets only one specific protein or whether similar or homologous proteins are also influenced that may account for possible side effects. RESULTS: prolific (protein-literature investigation for interacting compounds) provides a one-step solution to investigate available information on given protein names, sequences, similar proteins or sequences on the gene level. Co-occurrences of UniProtKB/Swiss-Prot proteins and PubChem compounds in all PubMed abstracts are retrievable. Concise 'heat-maps' and tables display frequencies of co-occurrences. They provide links to processed literature with highlighted found protein and compound synonyms. Evaluation with manually curated drug-protein relationships showed that up to 69% could be discovered by automatic text-processing. Examples are presented to demonstrate the capabilities of prolific. AVAILABILITY: The web-application is available at http://prolific.pharmaceutical-bioinformatics.de and a web service at http://www.pharmaceutical-bioinformatics.de/prolific/soap/prolific.wsdl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.