Reprogramming parasitic signatures into anticancer peptide candidates: in silico discovery of Leishmania...

This study aims to computationally identify and prioritize anticancer peptide (ACP) candidate sequences derived from the Leishmania major proteins KMP11 and GP63 using an integrated bioinformatics framework that incorporates peptide design, safety assessment, multi-criteria decision-making, and membrane-oriented evaluation. Amino acid sequences of KMP11 and GP63 were obtained from the NCBI database. Peptide fragments ranging from 5 to 25 residues were computationally generated and initially screened for anticancer potential using AntiCP 2.0 and MLACP prediction tools. Candidate peptides were subsequently subjected to systematic amino acid substitutions followed by iterative re-evaluation to optimize predicted anticancer properties. Toxicity, allergenicity, and antigenicity were assessed using TOXINPRED2, ALGPRED2, and VAXIJEN2, respectively. Peptides meeting safety and functional criteria were prioritized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Structural modeling, exploratory molecular docking, and membrane interaction analyses were then performed to provide comparative mechanistic insights into membrane association and potential receptor accessibility, rather than to predict specific target binding. Reference datasets of experimentally validated ACPs and non-ACPs were compiled, and motif analysis using MERCI identified sequence patterns associated with anticancer activity, with enriched motifs most frequently observed in the 12-13 residue range. Following iterative screening and safety evaluation, subsets of peptides derived from KMP11 and GP63 were identified as non-toxic and non-allergenic according to in silico prediction tools. These peptides were subsequently prioritized using the TOPSIS multi-criteria decision-making model. The top-ranked candidates were further subjected to exploratory molecular docking against selected cancer-associated receptors and coarse-grained membrane interaction analysis to provide comparative mechanistic context regarding membrane interaction propensity and potential receptor accessibility. Based on integrated computational scoring, ten peptides were prioritized as candidate sequences for further experimental validation. This study demonstrates the feasibility of computationally deriving and prioritizing anticancer peptide candidates from L. major proteins KMP11 and GP63. The proposed framework provides a structured, hypothesis-generating strategy for ACP candidate prioritization, emphasizing comparative evaluation rather than direct prediction of therapeutic efficacy or specific molecular targets. By leveraging evolutionary and physicochemical features associated with host-pathogen interactions, this approach enables systematic exploration of parasite-derived peptide sequence space. Experimental validation will be essential to determine the biological activity, selectivity, and translational relevance of the identified candidates.