Similarity/dissimilarity analysis of protein structures based on Markov random fields

Protein Structure Similarity plays an important role in study on functional properties of proteins and evolutionary study. Many efficient methods have been proposed to advance protein structural comparison, but there are still some challenges in the contact strength definitions and similarity measures. In this work, we schemed out a new method to analyze the similarity/dissimilarity of the protein structures based on Markov random fields. We evaluated the proposed method with two experiments and compared it with the competing methods The results indicate that the proposed method exhibits a strong ability to detect the similarities/dissimilarities among the conformation of different cyclic peptides and protein structures. We also found that the alpha-C, oxygen O and N allow us to extract more conserved structures of the proteins, and Markov random fields with 2-point cliques (V) and orders 3 and 1 are more efficient in detecting the similarities/dissimilarities among different protein structures. This understanding can be used to design more powerful methods for similarities/dissimilarities analysis of different protein structures. (C) 2018 Published by Elsevier Ltd.