Tree-Based Consensus Model for Proline Cis-Trans Isomerization Prediction

Proline cis-trans isomerization plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline cis-trans isomerization is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline cis-trans isomerization predictor with a biophysically-motivated consensus model through the use of evolutionary information only. The current computational predictors of proline cis-trans isomerization achieve about 70-73% accuracies through the use of evolutionary information as well as predicted protein secondary structure information. However, our methods that utilize support vector machine (SVM) and tree-based consensus model have achieved 76.72% and 81.5% accuracies, respectively, on the same proline dataset.