Selection of short Gadd45β-binding peptides through a synergistic computational and biophysical approach.

In this study, we explored the design of linear D-tripeptides tailored to bind specific cavities of Gadd45β, chosen as a model protein target. To identify peptides that selectively interact with predicted binding sites, we combined computational modeling with biophysical experiments. Gadd45β was selected since it has emerged as a promising therapeutic target involved in multiple disease pathways, including cancer and inflammation. Computational analysis was first employed to characterize the structural features and potential binding sites of Gadd45β. Guided by these insights, linear D-tripeptides were designed and optimized for specific interactions with the target surface. The resulting candidates were subsequently assessed through a series of biophysical assays to evaluate their binding affinity, selectivity, and potential therapeutic activity. Complementary computational simulations were employed to gain atomistic insight into the dynamics of peptide-protein recognition. This integrated computational-experimental strategy led to the identification of two D-tripeptides, RYR and VWR, that bind Gadd45β at a biologically relevant site, illustrating a general framework for early-stage peptide ligand discovery.