In silico design and optimization of selective membranolytic anticancer peptides.

ABSTRACT: Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a f

SUBMITTER: Gabernet G 

PROVIDER: S-EPMC6677754 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature