ACPS: An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data

Abstract

The anti-cancer targets play a crucial role in the signaling processes of cells, and therefore, it becomes nearly impossible to engage these targets without affecting the native cellular function. Thus, an approach has been taken to develop an anti-cancer Scanner (ACPS) tool aimed toward the recognition of anti-cancer marks in the form of peptides. The proposed ACPS tool allows fast fingerprinting of the anti-cancer targets having extreme significance in the current bioinformatics research. There already exist some tools that offer these features on a single platform; however, the performance of ACPS was compared with the preexisting online tools and was observed that ACPS offers greater than 95% accuracy that is comparatively much higher. The anti-cancer marked sequences of proteins supplied by the operators are scanned against the anti-cancer target datasets via ACPS and provide precision-based anti-cancer peptides. The proposed tool has been contrived in PERL programming language, and this tool is the extended version of A-CaMP codes, which are highly scalable having an extensible application in cancer biology with robust coding architecture. The availability of tools like ACPS will greatly benefit researchers in the field of oncology and structure-based drug design.

Keywords: anti-cancer vaccines; artificial intelligence; artificial neural network; machine learning.