Alpha helix prediction based on Metropolis-Hastings sampling

Abstract

Accurate identification of protein secondary structures is beneficial to the structural interpretation of low-resolution X-ray and EM electron density maps. Existing alpha helix identification methods mainly focus on locally voxelwise classification and then link the helix-voxels based on post-processing processes. In this paper, a novel alpha helix identification approach, named as SSEPredictor, based on Metropolis-Hastings sampling is proposed, which can provide both locally and globally optimized prediction for alpha helical structures in low-resolution electron density maps. The approach has been tested on X-ray crystallographic electron density maps at 8Å resolution. The experimental results show that the identification accuracy is promising.