Descriptor-based computational analysis reveals a novel classification scheme for feruloyl esterase enzyme families
Conference poster, 2011
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.
Our study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs. 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. Support Vector Machine model was constructed for the classification of 365 FAEs into the pre-assigned clusters and 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.
The development of pharmacophore models for specific FAE sub-families will have a huge impact on the application of members of the particular group to completely novel and unexpected substrates. Virtual screening with the developed pharmacophores of chemical and natural compound databases could reveal unique opportunities for FAEs-based-biocatalytic modifications to synthesize compounds with altered or improved medicinal properties. We are confident that the classification and characterization of this expanding super family of enzymes will provide researchers and industries with the toolbox from which to select FAEs for suitable reactions and applications; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family and understanding their structure-function relationships.
We welcome interested researchers to submit putative FAE sequences to us for sub-grouping as per the new classification system. Sequences can be submitted at http://faeclassification.webs.com/