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.

MTGIpick allows robust identification of genomic islands from a single genome.

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.

Prediction of high-risk types of human papillomaviruses using statistical model of protein "sequence space".

Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein "sequence space" to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein "sequence space" could potentially be used to improve prediction of high-risk types of HPVs.