Protein Secondary Structure Prediction using DeterministicSequential Sampling

Kuo-ching Liang and Xiaodon

Abstract

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. While many of the existing algorithms utilize the similarity and homology to proteins with known secondary structures in the Protein Data Bank, other proteins with low similarity measures require a single sequence approach to the discovery of their secondary structure. In this paper we propose an algorithm based on the deterministic sequential sampling method and hidden Markov model for the single-sequence protein secondary structure prediction. The predictions are made based on windowed observations and by the weighted average over possible conformations within the observation window. The proposed algorithm is shown to achieve better performance on real dataset compared to the existing single-sequence algorithm.