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Bioinformatics. 2017 Jul 1;33(13):1937-1943. doi: 10.1093/bioinformatics/btx112.

Discriminating the reaction types of plant type III polyketide synthases.

Author information

1
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.

Abstract

Motivation:

Functional prediction of paralogs is challenging in bioinformatics because of rapid functional diversification after gene duplication events combined with parallel acquisitions of similar functions by different paralogs. Plant type III polyketide synthases (PKSs), producing various secondary metabolites, represent a paralogous family that has undergone gene duplication and functional alteration. Currently, there is no computational method available for the functional prediction of type III PKSs.

Results:

We developed a plant type III PKS reaction predictor, pPAP, based on the recently proposed classification of type III PKSs. pPAP combines two kinds of similarity measures: one calculated by profile hidden Markov models (pHMMs) built from functionally and structurally important partial sequence regions, and the other based on mutual information between residue positions. pPAP targets PKSs acting on ring-type starter substrates, and classifies their functions into four reaction types. The pHMM approach discriminated two reaction types with high accuracy (97.5%, 39/40), but its accuracy decreased when discriminating three reaction types (87.8%, 43/49). When combined with a correlation-based approach, all 49 PKSs were correctly discriminated, and pPAP was still highly accurate (91.4%, 64/70) even after adding other reaction types. These results suggest pPAP, which is based on linear discriminant analyses of similarity measures, is effective for plant type III PKS function prediction.

Availability and Implementation:

pPAP is freely available at ftp://ftp.genome.jp/pub/tools/ppap/.

Contact:

goto@kuicr.kyoto-u.ac.jp.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28334262
PMCID:
PMC5870536
DOI:
10.1093/bioinformatics/btx112
[Indexed for MEDLINE]
Free PMC Article

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