Deconstructing the Potency and Cell‐Line Selectivity of Membranolytic Anticancer Peptides**

Abstract

Current cancer treatments damage healthy cells and tissues, causing short‐term and long‐term side effects. New treatments are desired that show greater selectivity toward cancer cells and evade the common mechanisms of multidrug resistance. Membranolytic anticancer peptides (mACPs) hold promise against cancer and multidrug resistance. Amphipathicity, hydrophobicity, and net charge of mACPs participate in their respective interactions with cell membranes and their overall inhibition of cancer cells. To support the design of cell‐line selective mACPs, we investigated the relationships that amino acid composition, physicochemical properties, sequence motifs, and sequence homology could have with their potency and selectivity towards several healthy and cancer cell lines. Sequence length and net charge are known to affect the selectivity of mACPs between cancer and healthy cell lines. Our study reveals that increasing the net charge or flexibility (i. e., small and aliphatic residues) influences their selectivity between cancer cell lines with comparable lipid compositions.

Structure-aware machine learning strategies for antimicrobial peptide discovery

“…The combination of predictive and generative models minimizes the need for extensive experiments and resources while increasing our chances of achieving successful outcomes, e.g., drug hits or leads. The main applications of ML models for biofuel production, material design, or drug development include designing peptides with one or more objectives (e.g., cell-penetrating property 1 , antiviral activity 2 , antimicrobial activity 3 , 4 , anticancer activity 5 – 8 ), protein binders 9 – 11 , monoclonal antibodies 12 , 13 , protein families 14 – 16 , and enzymes 17 , 18 .…”