Prediction of the Number of Helices for the Twilight Zone Proteins

Protein structure prediction is one of the core research areas in bioinformatics. This paper addresses the protein secondary structure prediction problem for the twilight zone proteins, which are characterized by low, about 25% homology to the sets of known sequences. The commonly used sequence alignment based algorithms fail to provide accurate prediction for sequences of such low homology, and thus alternative solutions should be sought. We propose a novel method that aims at the prediction of the number of helical structures based on the twilight zone protein sequences. The method is based on a custom designed and compact feature based sequences representation and applies a decision tree prediction algorithm. The performed experimental study shows superiority of the proposed method over three other prediction algorithms and the results provided by YASPIN algorithm, which is a state-of-the-art alignment based secondary structure prediction method designed using low homology sequences