Genome scale prediction of substrate specificity for acyl adenylate superfamily of enzymes based on active site residue profiles.

BMC Bioinformatics
Pankaj KhuranaDebasisa Mohanty

Abstract

Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference. We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probab...Continue Reading

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Citations

Dec 2, 2011·Bioinformatics·Carlos PrietoJuan Francisco Martín
Feb 13, 2014·International Journal of Molecular Sciences·Yuan YuanLuqi Huang
Mar 30, 2011·The Plant Journal : for Cell and Molecular Biology·Jay Shockey, John Browse
Feb 9, 2012·The Plant Journal : for Cell and Molecular Biology·Ana SaballosWilfred Vermerris
May 13, 2014·Nature Chemical Biology·Wolfram LorenzenHelge B Bode
Mar 9, 2018·Chembiochem : a European Journal of Chemical Biology·Franziska HemmerlingFrank Hahn
Aug 23, 2020·The Journal of Biological Chemistry·Serina L RobinsonLawrence P Wackett
Nov 10, 2020·Biochimie·Mauno Vihinen

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Datasets Mentioned

BETA
31616027
55980573
5702188
416605

Methods Mentioned

BETA
X-ray

Software Mentioned

BLAST
MSA
AUTODOCK
HMMER
MODELLER
InsightII
ACSITE
PSSM
GenTHREADER
EMBOSS

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