  Molecular docking is a structure-based computational drug design technique
for predicting the interaction between a small molecule (ligand) and a
macromolecule (receptor). Over the past three decades various docking software
programs have been developed, mostly for drug-like molecules. With the recent
interest in peptides as therapeutic molecules, several peptide docking methods
have also been developed. AutoDock CrankPep (ADCP), is a state-of-the-art
peptide docking tool that predicts the interaction of peptide with up to 20
amino acids in a user defined region of a macromolecule, i.e.focused docking.
Recent advances in deep learning (DL) approaches have shown remarkable success
in docking linear peptides composed of natural amino acids only. Unlike ADCP,
these methods provide a confidence level in their predictions. Here we explore
whether ADCP and various DL methods (AlphaFold2 Monomer, AlphaFold2 Multimer,
and OmegaFold) and their prediction confidence metric can be used to
discriminate native and non-native substrates for HIV and FIV proteases. We
found that ADCP successfully predicts the interactions of native peptides but
fails to discriminate non-native ones. This was expected as conventional
docking methods report solutions maximizing ligand receptor interactions for
any ligand. Surprisingly, DL methods underperform when docking native peptides
into these particular docking targets but achieve high success rates with
non-native peptides. While AlphaFold managed to successfully dock a few of the
native peptides, OmegaFold failed to successfully dock any of them. Overall,
none of these methods is currently able to distinguish between native and
non-native peptides, warranting further exploration of specialized
methodologies.
