There is interest in peptide drug design, especially for targeting intracellular protein–protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

A systematic structure–activity and computational modeling analysis of a series of glucagon-like peptide-1 receptor (GLP-1R) agonists based upon an ultra-short GLP-1 peptide, H-His-Aib-Glu-Gly-Thr-Phe-Thr-Ser-Asp-Bip-Bip-NH2, was conducted. This highly potent 11-mer peptide led to a deeper understanding of the α-helical bias of strategic α-methylation within the linear parent template as well as optimization of GLP-1R agonist potency by 1000-fold. These data were correlated with previously reported co-structures of both full-length GLP-1 analogs and progenitor N-terminal GLP-1 fragment analogs related to such ultra-short GLP-1R agonist peptides. Furthermore, the development of a quantitative structure–activity relationship (QSAR) model to analyze these findings is described in this study.