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@article{cf45172ef0eb460d8d89dc1cb093def9,
title = "The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases",
keywords = "k-means clustering, Support vector machine, Phylogenetic analysis, Ligand binding site, Catalytic triad, Self organizing maps",
publisher = "Elsevier Inc.",
author = "Udatha, {D.B.R.K. Gupta} and Irene Kouskoumvekaki and Lisbeth Olsson and Gianni Panagiotou",
year = "2011",
doi = "10.1016/j.biotechadv.2010.09.003",
volume = "29",
number = "1",
pages = "94--110",
journal = "Biotechnology Advances",
issn = "0734-9750",

}

RIS

TY - JOUR

T1 - The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

A1 - Udatha,D.B.R.K. Gupta

A1 - Kouskoumvekaki,Irene

A1 - Olsson,Lisbeth

A1 - Panagiotou,Gianni

AU - Udatha,D.B.R.K. Gupta

AU - Kouskoumvekaki,Irene

AU - Olsson,Lisbeth

AU - Panagiotou,Gianni

PB - Elsevier Inc.

PY - 2011

Y1 - 2011

N2 - One 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.

AB - One 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.

KW - k-means clustering

KW - Support vector machine

KW - Phylogenetic analysis

KW - Ligand binding site

KW - Catalytic triad

KW - Self organizing maps

U2 - 10.1016/j.biotechadv.2010.09.003

DO - 10.1016/j.biotechadv.2010.09.003

JO - Biotechnology Advances

JF - Biotechnology Advances

SN - 0734-9750

IS - 1

VL - 29

SP - 94

EP - 110

ER -