ALA-A2 Is a Novel Anticancer Peptide Inspired by Alpha-Lactalbumin: A Discovery from a Computational Peptide Library, In Silico Anticancer Peptide Screening and In Vitro Experimental Validation

Anticancer peptides (ACPs) are rising as a new strategy for Cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of Peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer-generated peptide library inspired by alpha-lactalbumin, a milk protein with known Anticancer properties. A total of 2688 distinct Peptides, 5-25 Amino acids in length, are generated from alpha-lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate Peptides ALA-A1 and ALA-A2. In vitro screening against five human Cancer cell lines supports ALA-A2 as the positive hit. ALA-A2 selectively kills A549 lung Cancer cells in a dose-dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions-proteomics and functional validation reveal that ALA-A2 induces Autophagy to mediate lung Cancer cell death. This approach to identify ALA-A2 is time and cost-effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA-A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development.

SWATH‐MS; anticancer peptides; cytotoxic screening; drug discovery; lung adenocarcinoma; machine learning; peptide library.