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Type: Journal article
Title: Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: A new vista in engineering enzymes
Author: Ebrahimi, M.
Lakizadeh, A.
Agha-Golzadeh, P.
Ebrahimie, E.
Ebrahimi, M.
Citation: PLoS One, 2011; 6(8):e23146-1-e23146-11
Publisher: Public Library of Science
Issue Date: 2011
ISSN: 1932-6203
Editor: Sarkar, I.N.
Statement of
Mansour Ebrahimi, Amir Lakizadeh, Parisa Agha-Golzadeh, Esmaeil Ebrahimie, Mahdi Ebrahimi
Abstract: The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.
Keywords: Amino Acids
Cluster Analysis
Reproducibility of Results
Protein Engineering
Enzyme Stability
Decision Trees
Models, Biological
Databases, Protein
Neural Networks, Computer
Rights: © 2011 Ebrahimi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pone.0023146
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