Prediction of protein structural classes for low-homology sequences based on predicted secondary structure

Background: Prediction of protein structural classes (a, b, a + b and a/b) from amino acid sequences is of great importance, as it is beneficial to study protein function, regulation and interactions. Many methods have been developed for high-homology protein sequences, and the prediction accuracies can achieve up to 90%. However, for low-homology sequences whose average pairwise sequence identity lies between 20% and 40%, they perform relatively poorly, yielding the prediction accuracy often below 60%. Results: We propose a new method to predict protein structural classes on the basis of features extracted from the predicted secondary structures of proteins rather than directly from their amino acid sequences. It first uses PSIPRED to predict the secondary structure for each protein sequence. Then, the chaos game representation is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using recurrence quan...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jian-Yi Yang, Zhen-Ling Peng, Xin Chen
• Keywords: Amino Acid Sequences, BMCBI 2010, Protein Structural Classes, Secondary Structure