Dual-Site Targeting by Peptide Inhibitors of the N-Terminal Domain of Hsp90: Mechanism and Design

Heat shock protein 90 (Hsp90) is a pivotal molecular chaperone crucial in the maturation of client proteins, positioning it as a significant target for cancer therapy. However, the design of effective Hsp90 inhibitors presents substantial challenges due to the complex interaction network and the requisite specificity of the inhibitors. This study tackles the task of designing peptide inhibitors capable of concurrently binding to both the ATP-binding pocket and the Cdc37-binding site within the N-terminal domain of Hsp90. In response to these challenges, we developed an advanced peptide screening protocol that merges machine learning with various molecular simulation techniques to boost the identification and optimization of potent inhibitors. Our integrated approach employs a convolutional neural network-based framework to predict peptide binding propensities. This predictive model is augmented by comprehensive molecular docking and dynamic simulations to assess the stability and interaction dynamics of Hsp90/peptide complexes. We successfully identified three heptapeptides that demonstrate the ability to interact with both binding sites, effectively obstructing the entrance to the ATP-binding pocket. This study elucidates the inhibitory mechanisms of these peptides, paves the way for the development of more efficacious therapeutic agents targeting Hsp90, and underscores the value of integrating machine learning techniques with molecular modeling in the peptide design process.