Dataset Information

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.

SUBMITTER: Li T

PROVIDER: S-EPMC8215927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Bacterial aromatic polyketides are a pharmacologically important group of natural products synthesized by type II polyketide synthases (type II PKSs) in actinobacteria. Isolation of novel aromatic polyketides from microbial sources is currently impeded because of the lack of knowledge about prolific taxa for polyketide synthesis and the difficulties in finding and optimizing target microorganisms. Comprehensive analysis of type II PKSs and the prediction of possible polyketide chemotypes in various actinobacterial genomes will thus enable the discovery or synthesis of novel polyketides in the most plausible microorganisms. DESCRIPTION: We performed a comprehensive computational analysis of type II PKSs and their gene clusters in actinobacterial genomes. By identifying type II PKS subclasses from the sequence analysis of 280 known type II PKSs, we developed highly accurate domain classifiers for these subclasses and derived prediction rules for aromatic polyketide chemotypes generated by different combinations of type II PKS domains. Using 319 available actinobacterial genomes, we predicted 231 type II PKSs from 40 PKS gene clusters in 25 actinobacterial genomes, and polyketide chemotypes corresponding to 22 novel PKS gene clusters in 16 genomes. These results showed that the microorganisms capable of producing aromatic polyketides are specifically distributed within a certain suborder of Actinomycetales such as Catenulisporineae, Frankineae, Micrococcineae, Micromonosporineae, Pseudonocardineae, Streptomycineae, and Streptosporangineae. CONCLUSIONS: We could identify the novel candidates of type II PKS gene clusters and their polyketide chemotypes in actinobacterial genomes by comprehensive analysis of type II PKSs and prediction of aromatic polyketides. The genome analysis results indicated that the specific suborders in actinomycetes could be used as prolific taxa for polyketide synthesis. The chemotype-prediction rules with the suggested type II PKS modules derived using this resource can be used further for microbial engineering to produce various aromatic polyketides. All these resources, together with the results of the analysis, are organized into an easy-to-use database PKMiner, which is accessible at the following URL: http://pks.kaist.ac.kr/pkminer. We believe that this web-based tool would be useful for research in the discovery of novel bacterial aromatic polyketides.