Targeting Listeria virulence-regulon glutathion-cavity by evolutecas, docking and Boltz2

Computational explorations are described by screening and generating peptide and acetic-polymer evolutecas targeting one of the virulence regulons of Listeria monocitogenes (Lm). Lm are among the most important multiple-resistant food-borne infective bacteria whose antibiotic resistances raise worldwide concerns. The Lm glutathion cavity called positive regulatory factor A (prfA) virulence regulon, was computationally targeted here because of the glutathion-dependent prfA activation of many Lm virulent genes. Computationally docking screening of both tri-,tetra-, and penta-mer peptides or innovative acetic-polymers (mimicking amino acids) and evolutecas generating thousands of ligand candidates, were employed to optimize their fitting to glutathion-prfA cavities. Combining library-screenings and evoluteca generations, novel top-peptides and top-acetic-polymers were generated predicting both low-toxicities (to reduce any known undesirable side-effects) and low nanoMolar consensus affinities (to maximize their specificities). Co-evolved top-acetic-polymer smiles were also co-folded to prfA amino acid sequence (ligand-induced-fit) by deep-learning Boltz2. Comparative ADV and Boltz2 results confirmed similar targeted cavity and most top-ligand affinities, despite their sharply different algorithms. Whether some of the top affinities / conformations correspond to the most active prfA inhibitors will only depend on experimental validation. Therefore, some additional chemical synthesis and in vitro experimental validation, are strictly required to continue with the Lm prfA virulence explorations.