Statistical analysis of plant anticancer peptides using the R environment

Despite significant advances in cancer treatment, interest in the design of new drugs has increased. Some plant peptides show a wide range of cytotoxic activity against cancer cells. The purpose of this study was to investigate the recognition of plant anti-cancer peptides and also to find the most important common features among them. In this regard, a list of the antimicrobial peptides and information about each peptide was extracted. Statistical analyzes were performed using R Studio software. The results showed 55 plant anticancer peptides were taxonomically belonging to the Malpighiales. Approximately length of 44% of peptides was in the range of 25 to 30 amino acids. Histidine and methionine had the lowest abundance among peptide amino acids. Cysteine, serine and glycine were the most abundant amino acids. 91% of peptides had less than 10 acidic amino acids and 71% peptides had less than 10 basic amino acids. A pure charge of 76% peptides was between 2 and -2. 64% of peptides had a Bowman index of less than 1. The low index indicates high hydrophobicity of these peptides and increases their chances for interaction with other proteins. Also, the most important three-dimensional structure of plant anti-cancer peptides was the presence of 3 di-sulfide bridges. Therefore, pharmaceutical manufacturers and drug designers can use these features extracted from advanced statistical analyzes to synthesize or discover new effective drugs with less side effects.