DDAP: docking domain affinity and biosynthetic pathway prediction tool for type I polyketide synthases

Abstract

SummaryDDAP is a tool for predicting the biosynthetic pathways of the products of type I modular polyketide synthase (PKS) with the focus on providing a more accurate prediction of the ordering of proteins and substrates in the pathway. In this study, the module docking domain (DD) affinity prediction performance on a hold-out testing data set reached AUC = 0.88; the MRR of pathway prediction reached 0.67. DDAP has advantages compared to previous informatics tools in several aspects: (i) it does not rely on large databases, making it a high efficiency tool, (ii) the predicted DD affinity is represented by a probability (0 to 1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. To the best of our knowledge, DDAP is so far the first machine learning based algorithm for type I PKS pathway prediction. We also established the first database of type I modular PKSs, featuring a comprehensive annotation of available docking domains information in bacterial biosynthetic pathways.\n\nAvailability and implementationThe DDAP database is available at https://tylii.github.io/ddap. The prediction algorithm DDAP is freely available on GitHub (https://github.com/tylii/ddap) and released under the MIT license.\n\nContactukarvind@umich.edu