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Article: The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

TitleThe interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases
Authors
Issue Date2011
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/biotechadv
Citation
Biotechnology Advances, 2011, v. 29 n. 1, p. 94-110 How to Cite?
AbstractOne of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs. © 2010 Elsevier Inc.
Persistent Identifierhttp://hdl.handle.net/10722/181260
ISSN
2015 Impact Factor: 9.848
2015 SCImago Journal Rankings: 2.921
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorUdatha, DBRKGen_US
dc.contributor.authorKouskoumvekaki, Ien_US
dc.contributor.authorLsson, Len_US
dc.contributor.authorPanagiotou, Gen_US
dc.date.accessioned2013-02-21T02:03:34Z-
dc.date.available2013-02-21T02:03:34Z-
dc.date.issued2011en_US
dc.identifier.citationBiotechnology Advances, 2011, v. 29 n. 1, p. 94-110en_US
dc.identifier.issn0734-9750en_US
dc.identifier.urihttp://hdl.handle.net/10722/181260-
dc.description.abstractOne of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs. © 2010 Elsevier Inc.en_US
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/biotechadven_US
dc.relation.ispartofBiotechnology Advancesen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAmino Acid Sequenceen_US
dc.subject.meshCarboxylic Ester Hydrolases - Chemistry - Classification - Geneticsen_US
dc.subject.meshComputational Biology - Methodsen_US
dc.subject.meshDrug Designen_US
dc.subject.meshModels, Molecularen_US
dc.subject.meshMolecular Sequence Dataen_US
dc.subject.meshPhylogenyen_US
dc.subject.meshSubstrate Specificityen_US
dc.titleThe interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterasesen_US
dc.typeArticleen_US
dc.identifier.emailPanagiotou, G: gipa@hku.hken_US
dc.identifier.authorityPanagiotou, G=rp01725en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.biotechadv.2010.09.003en_US
dc.identifier.pmid20851174-
dc.identifier.scopuseid_2-s2.0-78649906497en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78649906497&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume29en_US
dc.identifier.issue1en_US
dc.identifier.spage94en_US
dc.identifier.epage110en_US
dc.identifier.isiWOS:000286543400010-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridUdatha, DBRKG=36704182100en_US
dc.identifier.scopusauthoridKouskoumvekaki, I=6602787035en_US
dc.identifier.scopusauthoridlsson, L=36664788500en_US
dc.identifier.scopusauthoridPanagiotou, G=8566179700en_US

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