ACPOpt: A deep learning approach to design structure-aware property optimized anti-cancer peptides

Peptide-based therapeutics have gained significant attention for their potential in targeting specific proteins with high specificity and minimal toxicity. In cancer therapy, anti-cancer peptides (ACPs) have emerged as promising candidates due to their ability to selectively interact with the target protein. However, the design of peptides with optimal biological activity and stability remains challenging. Recent advancements in generative artificial intelligence (AI) provide opportunities to address these challenges by leveraging existing peptide datasets to identify potent sequences with optimized therapeutic properties. This study presents a novel pipeline, ACPOpt (Optimized Anti-cancer peptides), for the generation of target-specific ACPs. The pipeline utilizes deep learning algorithms to design peptides optimized for binding to the Pim1 kinase, a key protein involved in cancer progression and treatment resistance, aiming to enhance the specificity and efficacy of anti-cancer treatments. In the current work, soluble, non-hemolytic peptides with high affinity were generated against the substrate protein binding site of the Pim1 kinase. By integrating advanced peptide generation models with property optimization, this approach offers potential pathway for the development of target-specific peptide-based therapies.