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

ABSTRACT

SUMMARY: DDAP 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 dataset reached 0.88 as measured by the area under the receiver operating characteristic (ROC) curve (AUC); the Mean Reciprocal Ranking (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 (0-1), which is more intuitive than raw scores, (iii) its performance is competitive compared to the current popular rule-based algorithm. DDAP is so far the first machine learning based algorithm for type I PKS DD affinity and 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. AVAILABILITY AND IMPLEMENTATION The 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. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.