Dataset Information

Sequence-Based Screening for Rare Enzymes: New Insights into the World of AMDases Reveal a Conserved Motif and 58 Novel Enzymes Clustering in Eight Distinct Families.

ABSTRACT: Arylmalonate Decarboxylases (AMDases, EC 4.1.1.76) are very rare and mostly underexplored enzymes. Currently only four known and biochemically characterized representatives exist. However, their ability to decarboxylate ?-disubstituted malonic acid derivatives to optically pure products without cofactors makes them attractive and promising candidates for the use as biocatalysts in industrial processes. Until now, AMDases could not be separated from other members of the aspartate/glutamate racemase superfamily based on their gene sequences. Within this work, a search algorithm was developed that enables a reliable prediction of AMDase activity for potential candidates. Based on specific sequence patterns and screening methods 58 novel AMDase candidate genes could be identified in this work. Thereby, AMDases with the conserved sequence pattern of Bordetella bronchiseptica's prototype appeared to be limited to the classes of Alpha-, Beta-, and Gamma-proteobacteria. Amino acid homologies and comparison of gene surrounding sequences enabled the classification of eight enzyme clusters. Particularly striking is the accumulation of genes coding for different transporters of the tripartite tricarboxylate transporters family, TRAP transporters and ABC transporters as well as genes coding for mandelate racemases/muconate lactonizing enzymes that might be involved in substrate uptake or degradation of AMDase products. Further, three novel AMDases were characterized which showed a high enantiomeric excess (>99%) of the (R)-enantiomer of flurbiprofen. These are the recombinant AmdA and AmdV from Variovorax sp. strains HH01 and HH02, originated from soil, and AmdP from Polymorphum gilvum found by a data base search. Altogether our findings give new insights into the class of AMDases and reveal many previously unknown enzyme candidates with high potential for bioindustrial processes.

SUBMITTER: Maimanakos J

PROVIDER: S-EPMC4996985 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Sequence-Based Screening for Rare Enzymes: New Insights into the World of AMDases Reveal a Conserved Motif and 58 Novel Enzymes Clustering in Eight Distinct Families.

Maimanakos Janine J Chow Jennifer J Gaßmeyer Sarah K SK Güllert Simon S Busch Florian F Kourist Robert R Streit Wolfgang R WR

Frontiers in microbiology 20160825

Arylmalonate Decarboxylases (AMDases, EC 4.1.1.76) are very rare and mostly underexplored enzymes. Currently only four known and biochemically characterized representatives exist. However, their ability to decarboxylate α-disubstituted malonic acid derivatives to optically pure products without cofactors makes them attractive and promising candidates for the use as biocatalysts in industrial processes. Until now, AMDases could not be separated from other members of the aspartate/glutamate racema ...[more]

PMID: 27610105

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Project description:BackgroundClustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches.ResultsUsing clusters from a manually curated dataset as reference, we examined the performance of a widely used graph-based Markov clustering algorithm (MCL) and a greedy heuristic approach (UCLUST) in delineating protein families coded by three sets of bacterial genomes of different G+C content. Both MCL and UCLUST generated clusters that are comparable to the reference sets at specific parameter settings, although UCLUST tends to under-cluster compositionally biased sequences (G+C content 33% and 66%). Using simulated data, we sought to assess the individual effects of sequence divergence, rate heterogeneity, and underlying G+C content. Performance decreased with increasing sequence divergence, decreasing among-site rate variation, and increasing G+C bias. Two MCL-based methods recovered the simulated families more accurately than did UCLUST. MCL using local alignment distances is more robust across the investigated range of sequence features than are greedy heuristics using distances based on global alignment.ConclusionsOur results demonstrate that sequence divergence, rate heterogeneity and content bias can individually and in combination affect the accuracy with which MCL and UCLUST can recover homologous protein families. For application to data that are more divergent, and exhibit higher among-site rate variation and/or content bias, MCL may often be the better choice, especially if computational resources are not limiting.

Dataset Information

Sequence-Based Screening for Rare Enzymes: New Insights into the World of AMDases Reveal a Conserved Motif and 58 Novel Enzymes Clustering in Eight Distinct Families.

Publications

Sequence-Based Screening for Rare Enzymes: New Insights into the World of AMDases Reveal a Conserved Motif and 58 Novel Enzymes Clustering in Eight Distinct Families.

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