Background Protein–peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein–peptide databases and tools that allow the retrieval, characterization and understanding of protein–peptide recognition and consequently support peptide design. Results We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein–peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein–peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. Conclusions Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein–peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia

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The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.

Proteins are fundamental biomolecules for the metabolism of living beings and have several biotechnological uses. The computational study of this class of macromolecules allows the ex- pansion of knowledge and speed in research applications, such as catalytic processes, three- dimensional conformations, enzyme inhibition, molecular engineering, among others. In this dissertation, we present a set of papers that purposes computational strategies to study three- dimensional structures of proteins. In the first work, we combine an in-house developed machine learning strategy with docking, MM-PBSA, and metadynamics simulations to detect potential inhibitors for SARS-COV-2 main protease. Computational strategies can help to speed up the process of drug discovery, reducing the time and cost of wet-lab experiments because they will be focused on fewer molecules. Our work points out six ligands that have a good interaction with our target in its active pocket, indicating an inhibitor behavior. We highlighted the strongest interaction of our experiments, M pro -mirabegron complex, which was used as input for subse- quent in vitro assays to validate the inhibition potential suggested by in silico experiments. In the second paper, we present a literature review of several bioinformatics tools for the study of proteins. The article is a very detailed material to support the choice of students and profession- als for the most appropriate tool for a particular application. In the third work, we introduced the Propedia database for protein-peptide identification, which comprises over 19,000 high- resolution structures from the Protein Data Bank. Protein-peptide interactions can be useful for predicting, classifying, and scoring complexes or for designing new molecules. The main ad- vantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. The papers presented here provide an overview of the diversity of protein bioinformatics study and some of its applications in biological problems. Keywords: Bioinformatics. Protein-protein interaction. Peptide-protein interaction. Machine learning. Docking. Molecular dynamics. Proteins.

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