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

<p>Abstract</p> <p>Background</p> <p>Prediction of protein structural classes (<it>α</it>, <it>β</it>, <it>α </it>+ <it>β </it>and <it>α</it>/<it>β</it>) 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%.</p> <p>Results</p> <p>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 <it>chaos game representation </it>is employed to represent the predicted secondary structure as two time series, from which we generate a comprehensive set of 24 features using <it>recurrence quantification analysis</it>, <it>K-string based information entropy </it>and <it>segment-based analysis</it>. The resulting feature vectors are finally fed into a simple yet powerful Fisher's discriminant algorithm for the prediction of protein structural classes. We tested the proposed method on three benchmark datasets in low homology and achieved the overall prediction accuracies of 82.9%, 83.1% and 81.3%, respectively. Comparisons with ten existing methods showed that our method consistently performs better for all the tested datasets and the overall accuracy improvements range from 2.3% to 27.5%. A web server that implements the proposed method is freely available at <url>http://www1.spms.ntu.edu.sg/~chenxin/RKS_PPSC/</url>.</p> <p>Conclusion</p> <p>The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the predicted secondary structure sequences, which is capable of characterizing the sequence order information, local interactions of the secondary structural elements, and spacial arrangements of <it>α </it>helices and <it>β </it>strands. Thus, it is a valuable method to predict protein structural classes particularly for low-homology amino acid sequences.</p

A New Approach to Analyzing Convergence and Synchronicity in Growth and Business Cycles: Cross Recurrence Plots and Quantification Analysis

Convergence and synchronisation of business and growth cycles are important issues in the efficient formulation of euro area economic policies, and in particular European Central Bank (ECB) monetary policy. Although several studies in the economics literature address the issue of synchronicity of growth within the euro area, this is the first to address the issue using cross recurrence analysis. The main findings are that member state growth rates had largely converged before the introduction of the euro, but there is a wide degree of different synchronisation behaviours which appear to be non-linear in nature. Many of the euro area member states display what is termed here intermittency in synchronization, although this is not consistent across countries or members of the euro area. These differences in synchronization behaviors could introduce further challenges in managing the country-specific effects of the common monetary policy in the euro area