Visualization and classification of protein secondary structures using Self-Organizing Maps

In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure prediction is ultimately possible in wet lab, determining a correlation with the functionality is a hard task which can be facilitated by a...

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Published in:2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) pp. 129 - 136
Main Authors: Grevisse, Christian, Muller, Ian, Jimenez Laredo, Juan Luis, Ostaszewski, Marek, Danoy, Gregoire, Bouvry, Pascal
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Correlation
Data visualization
Histograms
Kernel
Protein engineering
Proteins
Smoothing methods
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Abstract In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure prediction is ultimately possible in wet lab, determining a correlation with the functionality is a hard task which can be facilitated by a computational model. In that context, this paper presents an automated algorithm for the visualization and classification of enzymatic proteins with the aim of examining whether the functionality is correlated to the secondary structure. To that end, up-to-date protein data was acquired from publicly accessible databases in order to construct their secondary structures. The resulting data were injected into a tailored version of a Kohonen Self-Organizing Map (SOM). Part of the work was to determine a proper way of reducing large secondary structures to a common length in order to be able to cope with the constant dimensionality requirement of SOMs. The final contribution consisted in the labeling of the trained nodes. Eventually, we were able to get a visual intuition and some quantified assessment on the nature of this correlation.
AbstractList In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure prediction is ultimately possible in wet lab, determining a correlation with the functionality is a hard task which can be facilitated by a computational model. In that context, this paper presents an automated algorithm for the visualization and classification of enzymatic proteins with the aim of examining whether the functionality is correlated to the secondary structure. To that end, up-to-date protein data was acquired from publicly accessible databases in order to construct their secondary structures. The resulting data were injected into a tailored version of a Kohonen Self-Organizing Map (SOM). Part of the work was to determine a proper way of reducing large secondary structures to a common length in order to be able to cope with the constant dimensionality requirement of SOMs. The final contribution consisted in the labeling of the trained nodes. Eventually, we were able to get a visual intuition and some quantified assessment on the nature of this correlation.
Author Jimenez Laredo, Juan Luis
Ostaszewski, Marek
Grevisse, Christian
Danoy, Gregoire
Bouvry, Pascal
Muller, Ian
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  givenname: Christian
  surname: Grevisse
  fullname: Grevisse, Christian
  email: christian.grevisse001@student.uni.lu
  organization: Computer Science and Communications Research Unit, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg
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  givenname: Ian
  surname: Muller
  fullname: Muller, Ian
  email: ian.muller001@student.uni.lu
  organization: Computer Science and Communications Research Unit, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg
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  givenname: Juan Luis
  surname: Jimenez Laredo
  fullname: Jimenez Laredo, Juan Luis
  email: juan.jimenez@uni.lu
  organization: Computer Science and Communications Research Unit, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg
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  givenname: Marek
  surname: Ostaszewski
  fullname: Ostaszewski, Marek
  email: marek.ostaszewski@uni.lu
  organization: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Avenue des Hauts Fourneaux L-4362 Esch-sur-Alzette, Luxembourg
– sequence: 5
  givenname: Gregoire
  surname: Danoy
  fullname: Danoy, Gregoire
  email: gregoire.danoy@uni.lu
  organization: Computer Science and Communications Research Unit, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg
– sequence: 6
  givenname: Pascal
  surname: Bouvry
  fullname: Bouvry, Pascal
  email: pascal.bouvry@uni.lu
  organization: Computer Science and Communications Research Unit, University of Luxembourg, 6, rue Richard Coudenhove-Kalergi L-1359 Luxembourg
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Snippet In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure...
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StartPage 129
SubjectTerms Correlation
Data visualization
Histograms
Kernel
Protein engineering
Proteins
Smoothing methods
Title Visualization and classification of protein secondary structures using Self-Organizing Maps
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