Shedding Light on the Drug-Target Prediction of the Anti-Inflammatory Peptide Tn P with Bioinformatics Tools.

Peptide-protein interactions are involved in various fundamental cellular functions, and their identification is crucial for designing efficacious peptide therapeutics. Drug-target interactions can be inferred by in silico prediction using bioinformatics and computational tools. We patented the Tn P family of synthetic cyclic peptides, which is in the preclinical stage of developmental studies for chronic inflammatory diseases such as multiple sclerosis. In an experimental autoimmune enceph-alomyelitis model, we found that Tn P controls neuroinflammation and prevents demyelination due to its capacity to cross the blood-brain barrier and to act in the central nervous system blocking the migration of inflammatory cells responsible for neuronal degeneration. Therefore, the identification of potential targets for Tn P is the objective of this research. In this study, we used bioinformatics and computational approaches, as well as bioactivity databases, to evaluate Tn P-target prediction for proteins that were not experimentally tested, specifically predicting the 3D structure of Tn P and its biochemical characteristics, Tn P-target protein binding and docking properties, and dynamics of Tn P competition for the protein/receptor complex interaction, construction of a network of con-nectivity and interactions between molecules as a result of Tn P blockade, and analysis of similarities with bioactive molecules. Based on our results, integrins were identified as important key proteins and considered responsible to regulate Tn P-governed pharmacological effects. This comprehensive in silico study will help to understand how Tn P induces its anti-inflammatory effects and will also facilitate the identification of possible side effects, as it shows its link with multiple biologically important targets in humans.