LiGRO: a graphical user interface for protein-ligand molecular dynamics.

To speed up the drug-discovery process, molecular dynamics (MD) calculations performed in GROMACS can be coupled to docking simulations for the post-screening analyses of large compound libraries. This requires generating the topology of the ligands in different software, some basic knowledge of Linux command lines, and a certain familiarity in handling the output files. LiGRO-the python-based graphical interface introduced here-was designed to overcome these protein-ligand parameterization challenges by allowing the graphical (non command line-based) control of GROMACS (MD and analysis), ACPYPE (ligand topology builder) and PLIP (protein-binder interactions monitor)-programs that can be used together to fully perform and analyze the outputs of complex MD simulations (including energy minimization and NVT/NPT equilibration). By allowing the calculation of linear interaction energies in a simple and quick fashion, LiGRO can be used in the drug-discovery pipeline to select compounds with a better protein-binding interaction profile. The design of LiGRO allows researchers to freely download and modify the software, with the source code being available under the terms of a GPLv3 license from http://www.ufrgs.br/lasomfarmacia/ligro/ .

Analysis of the tryptic search space in UniProt databases.

In this article, we provide a comprehensive study of the content of the Universal Protein Resource (UniProt) protein data sets for human and mouse. The tryptic search spaces of the UniProtKB (UniProt knowledgebase) complete proteome sets were compared with other data sets from UniProtKB and with the corresponding International Protein Index, reference sequence, Ensembl, and UniRef100 (where UniRef is UniProt reference clusters) organism-specific data sets. All protein forms annotated in UniProtKB (both the canonical sequences and isoforms) were evaluated in this study. In addition, natural and disease-associated amino acid variants annotated in UniProtKB were included in the evaluation. The peptide unicity was also evaluated for each data set. Furthermore, the peptide information in the UniProtKB data sets was also compared against the available peptide-level identifications in the main MS-based proteomics repositories. Identifying the peptides observed in these repositories is an important resource of information for protein databases as they provide supporting evidence for the existence of otherwise predicted proteins. Likewise, the repositories could use the information available in UniProtKB to direct reprocessing efforts on specific sets of peptides/proteins of interest. In summary, we provide comprehensive information about the different organism-specific sequence data sets available from UniProt, together with the pros and cons for each, in terms of search space for MS-based bottom-up proteomics workflows. The aim of the analysis is to provide a clear view of the tryptic search space of UniProt and other protein databases to enable scientists to select those most appropriate for their purposes.