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. Issue 3 (12th January 2023)

• Record Type: Journal Article
• Authors: Lerksuthirat, Tassanee; On‐yam, Pasinee; Chitphuk, Sermsiri; Stitchantrakul, Wasana; Newburg, David S.; Morrow, Ardythe L.; Hongeng, Suradej; Chiangjong, Wararat; Chutipongtanate, Somchai
• Is Part Of: Global challenges. Volume 7:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-12
• Subjects: anticancer peptides -- cytotoxic screening -- drug discovery -- lung adenocarcinoma -- machine learning -- peptide library -- SWATH‐MS; Climatic changes -- Periodicals; Sustainable development -- Periodicals; Globalization -- Environmental aspects -- Periodicals; Electronic journals; Periodicals; 500
• DOI: 10.1002/gch2.202200213
• Languages: English
• ISSNs: 2056-6646

Abstract

Abstract: 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. Abstract : This study describes a novel strategy for searching for a new anticancer peptide (ACP) by integrating the computational peptide library of alpha‐lactalbumin with downstream in silico machine learning‐based screening and in vitro experimental validation. The novel ACP, so‐called ALA‐A2, specifically kills lung adenocarcinoma cells through autophagy mediated cell death.