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Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition

Paper ID Volume ID Publish Year Pages File Format Full-Text
35167 45079 2009 7 PDF Available
Title
Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition
Abstract

Understating the adaptation mechanism of enzymes to pH extremes and discriminating them is a challenging task and would help to design stable enzymes. In this work, we have systematically analyzed the secondary structure amino acid compositions of 105 acidic and 111 alkaline enzymes, respectively. We found that the propensity of the individual residues to participate in different secondary structures might be a general stability mechanism for their adaptation to pH extremes. Based on it, we present a secondary structure amino acid composition method for extracting useful features from sequence, and a novel ensemble classifier named random forest was used. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.7%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5% to 21.2%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 3.2% to 19.9%.

Keywords
Acidic enzyme; Alkaline enzyme; Adaptation mechanism; Secondary structure amino acid composition; Feature extraction; Random forests
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Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition
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Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 44, Issue 6, June 2009, Pages 654–660
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us