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Welcome to CPred

Circular permutation (CP) has been an important technique for protein bioengineering and for researches of protein folding, stability and function. However, not all positions in a protein structure are permissive for creating viable, i.e. foldable and adequately stable, permutants. CPred predict viable CP sites by integating four machine learning methods, inclusive of artificial neural network (ANN), support vector machine (SVM), random forest and hiearchical feature integration. It features giving a probability estimate for each residue of the query protein. Residues possessing higher estimates are more feasibile for creating viable circular permutants.





Wei-Cheng Lo, Yen-Yi Liu, Li-Fen Wang, Tian Dai, Jenn-Kang Hwang, and Ping-Chiang Lyu
Maintained at Institute of Bioinformatics and Systems Biology, NCTU, Taiwan