In-silico Identification of Cyclic Peptide Inhibitors for A42r Profilin-like Protein from Monkeypox Virus

The emergence of Monkeypox Virus (MPXV) as a major global health concern underscores an urgent unmet need for innovative antiviral therapeutics. To address this need, a novel large-scale in silico screening strategy was adopted to identify highly potent antiviral cyclic peptides that inhibit the A42R profilin-like protein of monkeypox virus.

A total of 5,115 cyclic peptides were initially screened using Tanimoto similarity analysis, followed by data clustering to ensure structural diversity. From this, 500 representative peptides were selected and subjected to molecular docking against the A42R profilin-like protein. Subsequently, density functional theory calculations were performed to evaluate electronic properties. The topranked peptides were further analyzed using 300 ns molecular dynamics simulations conducted in triplicate to ensure reproducibility. Advanced analyses, including steered molecular dynamics, umbrella sampling, and MM/GBSA binding free energy calculations, were performed to assess the stability and binding affinity of the peptide-protein complexes.

Molecular docking identified 12 cyclic peptides with binding energies ≤ -6.0 kcal/mol (-6.5 to -6.1 kcal/mol), with CycPept_5184 showing the lowest score (-6.5 kcal/mol). Molecular dynamics simulations revealed that CycPept_3468 and CycPept_4348 maintained stable binding (RMSD of 1 nm), whereas CycPept_5184 and CycPept_5604 exhibited large fluctuations (up to 10 nm), indicating dissociation after 40 ns. MM/GBSA analysis confirmed favorable binding for both stable complexes, with CycPept_4348 showing a more negative binding free energy (-31.94 ± 5.01 kcal/mol) than CycPept_3468 (-26.09 ± 3.74 kcal/mol), driven primarily by stronger electrostatic interactions.

A multi-step computational screening strategy has been successfully used to narrow the large peptide library down to only two potent cyclic peptides. The combination of techniques in this simulation, including docking simulations, DFT methods, long-timescale MD simulations, and binding free energy calculations, improves the reliability of the predicted binding interactions.

The objective of the present study was to identify cyclic peptides CycPept_3468 and CycPept_4348, which showed excellent inhibitory activity against the MPXV A42R profilin-like protein. The high binding affinity and structural stability of the peptides indicate their potential efficacy as antiviral drugs.