The γ-turns play important roles in protein folding and molecular recognition. The prediction and analysis of γ-turn types are important for both protein structure predictions and better understanding the characteristics of different γ-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict γ-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding γ-turn types.
|Number of pages||5|
|Journal||World Academy of Science, Engineering and Technology|
|State||Published - 1 May 2010|
- Classification and regression tree (CART)
- Gamma-turn, Physicochemical properties
- Protein secondary structure