A Computational Approach for Designing a Peptide-Based Acetyl-CoA Synthetase 2 Inhibitor: A New Horizon for Anticancer Development

Abstract

Acetyl-CoA Synthetase 2 (ACSS2) has emerged as a new target for anticancer development owing to its high expression in various tumours and its enhancement of malignancy. Stressing the growing interest in peptide-derived drugs featuring better selectivity and efficacy, a computational protocol was applied to design a peptide inhibitor for ACSS2. Herein, 3600 peptide sequences derived from ACSS2 nucleotide motif were generated by classifying the 20 amino acids into six physiochemical groups. De novo modeling maintained essential binding interactions, and a refined library of 16 peptides was derived using Support Vector Machine filters to ensure proper bioavailability, toxicity, and therapeutic relevance. Structural and folding predictions, along with molecular docking, identified the top candidate, Pep16, which demonstrated significantly higher binding affinity (91.1 ± 1.6 kcal/mol) compared to a known inhibitor (53.7 ± 0.7 kcal/mol). Further molecular dynamics simulations and binding free energy calculations revealed that Pep16 enhances ACSS2 conformational variability, occupies a larger binding interface, and achieved firm binding. MM/GBSA analysis highlighted key electrostatic interactions with specific ACSS2 residues, including ARG 373, ARG 526, ARG 628, ARG 631, and LYS 632. Overall, Pep16 appears to lock the ACSS2 nucleotide pocket into a compact, rigid conformation, potentially blocking ATP binding and catalytic activity, and may serve as a novel specific ACSS2 inhibitor. Though, we urge further research to confirm and compare its therapeutic potential to existing inhibitors. We also believe that this systematic methodology would represent an indispensable tool for prospective peptide-based drug discovery.

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