標題: | Knowledge acquisition and development of accurate rules for predicting protein stability changes |
作者: | Huang, Liang-Tsung Gromiha, M. Michael Hwang, Shiow-Fen Ho, Shinn-Ying 生物科技學系 生物資訊及系統生物研究所 Department of Biological Science and Technology Institude of Bioinformatics and Systems Biology |
關鍵字: | protein stability;prediction;data mining;decision trees;bioinformatics |
公開日期: | 1-十二月-2006 |
摘要: | Knowing the mechanisms by which protein stability change is one of the most important and valuable tasks in molecular biology. The conventional methods of predicting protein stability changes mainly focus on improving prediction accuracy. However, it is desirable to extract domain knowledge from large databases that is beneficial to accurate prediction of the protein stability change. This paper presents an interpretable prediction tree method (named iPTREE) that produces explanatory rules to explore hidden knowledge accompanied with high prediction accuracy and consequently analyzes the factors influencing the protein stability changes. To evaluate iPTREE and the knowledge upon protein stability changes, a thermodynamic dataset consisting of 1615 mutants led by single point mutation from ProTherm is adopted. Being as a predictor for protein stability changes, the rule-based approach can achieve a prediction accuracy of 87%, which is better than other methods based on artificial neural networks (ANN) and support vector machines (SVM). Besides, these methods lack the ability in biological knowledge discovery. The human-interpretable rules produced by iPTREE reveal that temperature is a factor of concern in predicting protein stability changes. For example, one of interpretable rules with high support is as follows: if the introduced residue type is Alanine and temperature is between 4 degrees C and 40 degrees C, then the stability change will be negative (destabilizing). The present study demonstrates that iPTREE can easily be used in the application of protein stability changes where one requires more understandable knowledge. (c) 2006 Elsevier Ltd. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.compbiolchem.2006.06.004 http://hdl.handle.net/11536/11480 |
ISSN: | 1476-9271 |
DOI: | 10.1016/j.compbiolchem.2006.06.004 |
期刊: | COMPUTATIONAL BIOLOGY AND CHEMISTRY |
Volume: | 30 |
Issue: | 6 |
起始頁: | 408 |
結束頁: | 415 |
顯示於類別: | 期刊論文 |