PARAS: high-accuracy machine-learning of substrate specificities in nonribosomal peptide synthetases

- Researchers have developed PARAS and PARASECT, two new high-accuracy machine learning algorithms for predicting the substrate specificities of adenylation (A) domains in nonribosomal peptide synthetases (NRPSs). These tools could accelerate the discovery of novel NRPSs and their metabolic products, which have important applications in medicine and agriculture.

- The team curated a dataset of 3,254 A domains and used it to train the models. They identified a new type of A domain with unusually high L-tryptophan specificity and showed that it directs the production of tryptopeptin-related metabolites in Streptomyces species.

- PARAS and PARASECT outperform existing predictors, with a significant increase in predictive accuracy on independent holdout data. They also provide options for structure-guided feature extraction, integrating substrate features, and accommodating reliable multi-label predictions.

- The algorithms were able to accurately predict the substrate of an unusual A domain in a novel NRPS shown to assemble metabolites related to the immunomodulator tryptopeptin A. This demonstrates their potential for discovering new natural products.

- The study highlights the importance of a large and diverse training dataset for improving the accuracy of A domain substrate predictions. It also shows the value of combining computational and experimental methods for characterizing novel NRPSs and their metabolic products. 

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💻Code:📜Paper:#PARAS #MachineLearning #NRPSs #NaturalProducts #Bioinformatics