| 1. |
- Kouskoumvekaki, Irene, et al.
(författare)
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Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification
- 2008
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Ingår i: BMC Bioinformatics. - 1471-2105. ; 9:59
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Tidskriftsartikel (refereegranskat)abstract
- Background: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6-methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotypes. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains. Conclusion: Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.
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| 2. |
- Udatha, Gupta, 1984, et al.
(författare)
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Descriptor-based computational analysis and molecular simulation studies reveals a novel classification scheme and structure-function relationship elements for feruloyl esterases
- 2011
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Ingår i: Enzyme Engineering XXI, Vail, Colorado, USA. ; 18th -22nd September, 2011
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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. 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 expressed three enzymes representing different novel FEF families in Pichia pastoris and probed their specificity profiles. Structural and functional features of expressed FEF members were probed using Circular dichroism (CD) spectroscopy and Molecular simulations were done to understand the structural-function relationships of the FAEs for engineering studies. The active site residues were detected for the three expressed FAEs based on titration curves of amino acid residues as a function of pH obtained through simulation systems. 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.
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| 3. |
- Udatha, Gupta, 1984, et al.
(författare)
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Descriptor-based computational analysis reveals a new classification scheme for feruloyl esterase
- 2010
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Ingår i: 10th Swedish Bioinformatics Workshop, Gothenburg, Sweden. ; March 4-5, 2010
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- One of the most important groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine, food and nutrition industries due to their capability of acting on a large range of substrates for cleaving or/and synthesizing ester bonds. Despite the extensive studies in the past two decades on the production and partial characterization of FAEs there is poor knowledge on the control mechanisms of substrate recognition.The present work centers on the descriptor-based characterization and structural analysis of FAEs to propose a complete classification system of this industrially important enzyme. We collected 365 sequences from plants, fungi and bacteria and clustered them based on 91 descriptors derived from the amino acid sequence. A Support Vector Machine (SVM) model was subsequently trained for the classification of the sequences, which led to the identification of ten different sub families. The SVM model successfully recognized 98.30% of the sequences correctly that belong to respective clusters and all the sequences of the blind test set. Another important aspect of the present work involved the structural analysis of previously characterized FAEs based on the pharmacophoric features of the known substrates. In this work we apply an array of computational tools and we succeed to develop a new classification scheme for FAEs which opens new vistas in the application of this intriguing group of enzymes in biocatalytic transformations. The present work is not restricted to FAEs but represents a framework for the functional characterization and identification of substrate specificity for any poorly characterized enzyme group. In addition we demonstrated that sequence information can be used for constructing models that reveal the underlying structural reasons determining substrate specificities.
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| 4. |
- Udatha, Gupta, 1984, et al.
(författare)
-
Descriptor-based computational analysis reveals a novel classification scheme for feruloyl esterase enzyme families
- 2011
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Ingår i: International Conference on Bioinformatics, Kuala Lumpur, Malaysia. ; 20th November – 2nd December, 2011
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundOne 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.ResultsOur 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. ConclusionsThe 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/
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| 5. |
- Udatha, Gupta, 1984, et al.
(författare)
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The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases
- 2011
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Ingår i: Biotechnology Advances. - : Elsevier BV. - 0734-9750. ; 29:1, s. 94-110
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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