Prediction of the substrate for nonribosomal peptide synthetase (NRPS) adenylation domains by virtual screening

Abstract

Nonribosomal peptide synthetases (NRPSs) synthesize a diverse array of bioactive small peptides, many of which are used in medicine. There is considerable interest in predicting NRPS substrate specificity in order to facilitate investigation of the many "cryptic" NRPS genes that have not been linked to any known product. However, the current sequence similarity-based methods are unable to produce reliable predictions when there is a lack of prior specificity data, which is a particular problem for fungal NRPSs. We conducted virtual screening on the specificity-determining domain of NRPSs, the adenylation domain, and found that virtual screening using experimentally determined structures results in good enrichment of the cognate substrate. Our results indicate that the conformation of the adenylation domain and in particular the conformation of a key conserved aromatic residue is important in determining the success of the virtual screening. When homology models of NRPS adenylation domains of known specificity, rather than experimentally determined structures, were built and used for virtual screening, good enrichment of the cognate substrate was also achieved in many cases. However, the accuracy of the models was key to the reliability of the predictions and there was a large variation in the results when different models of the same domain were used. This virtual screening approach is promising and is able to produce enrichment of the cognate substrates in many cases, but improvements in building and assessing homology models are required before the approach can be reliably applied to these models.

Keywords: ANL superfamily; adenylate-forming enzyme; comparative modeling; homology modeling; ligand docking; molecular dynamics; natural product biosynthesis; secondary metabolism; structural bioinformatics; substrate binding.