Abstract A015: Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations

Abstract The use of artificial intelligence (AI) and machine learning (ML) for exploring vast amino acid sequence spaces has recently gained traction in the discovery of antibiotics and the design of biomaterials with a wide array of algorithms. However, despite some advancements, designing peptide inhibitors to specifically modulate protein-protein interactions remains a significant challenge. In this contribution, we explore the use of a Long Short-Term Memory (LSTM) network - a type of recurrent neural network - to model peptide sequences, given its ability to process sequential data and capture long-term dependencies, critical for peptide design. Our research focuses on developing precision peptides that target the interaction between the Notch intracellular domain (NICD) and CBF1/RBPJ transcription factors, key regulators of the Notch signaling pathway implicated in breast and pancreatic cancer stemness. We inferred hydrophobic, hydrophilic, van der Waals, and salt bridge interactions from experimentally determined three-dimensional protein complex structures, which were used for feature engineering via one-hot encoding. Peptides, each 20 amino acids in length, were generated using temperature scaling in the LSTM model. These peptides were then structurally optimized and subjected to molecular dynamics (MD) simulations, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) analysis to assess their interactions and binding affinities with the Notch receptor. The MD simulations provide valuable molecular-level insights into the peptide-Notch interactions, helping to evaluate their binding strength. Further biological testing is underway to validate the efficacy of these lead peptide inhibitors and elucidate their molecular mechanisms in targeting cancer stem cells associated with breast cancer. Citation Format: Gurudeeban Selvaraj, Satyavani Kaliamurthi, Gilles H. Peslherbe. Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A015

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