Peptide name : ALA-A2

Source/Organism : Alpha-Lactalbumin

Linear/Cyclic : Linear

Chirality : L

Peptide length: 13

C-terminal modification: Linear

N-terminal modification : Free

Non-natural peptide information:

Assay type : MTT assay

Assay time : 24-h

Activity : ~50% cell viability

Cell line : A-549

Cancer type : Lung Cancer

Other activity : Anticancer

Amino acid composition bar chart :

Molecular mass : 1546.7925 Dalton

Aliphatic index : 0.523

Instability index : 122.992

Hydrophobicity (GRAVY) : -1.053

Isoelectric point : 10.061

Charge (pH 7) : 2.7482

Aromaticity : 7.692

Molar extinction coefficient (cysteine, cystine): (1, 0)

Hydrophobic/hydrophilic ratio : 0.625

hydrophobic moment : -0.346

Missing amino acid : A,D,E,F,G,H,I,M,N,T,Y

Most occurring amino acid : S

Most occurring amino acid frequency : 3

Least occurring amino acid : L

Least occurring amino acid frequency : 1

Secondary structure fraction (Helix, Turn, Sheet): (23., 30., 23.)

SMILES Notation: CC(C)C[C@H](NC(=O)[C@@H](N)CCCCN)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CS)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CO)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CO)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O)C(C)C

1 : Lerksuthirat T, et al. 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. Glob Chall. 2023; 7:2200213. doi: 10.1002/gch2.202200213

Paper title : 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.

Doi : https://doi.org/10.1002/gch2.202200213

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.