Name
Title | ||
|---|---|---|
Protein remote homology detection and fold recognition based on Sequence-Order Frequency Matrix |
Abstract | ||
|---|---|---|
Protein remote homology detection and fold recognition are two critical tasks for the studies of protein structures and functions. Currently, the profile-based methods achieve the state-of-the-art performance in these fields. However, the widely used sequence profiles, like Position-Specific Frequency Matrix (PSFM) and Position-Specific Scoring Matrix (PSSM), ignore the sequence-order effects along protein sequence. In this study, we have proposed a novel profile, called Sequence-Order Frequency Matrix (SOFM), to extract the sequence-order information of neighboring residues from Multiple Sequence Alignment (MSA). Combined with two profile feature extraction approaches: Top-n-grams and Smith-Waterman algorithm, the SOFMs are applied to protein remote homology detection and fold recognition, and two predictors called SOFM-Top and SOFM-SW are proposed. Experimental results show that SOFM contains more information content than other profiles, and these two predictors outperform other state-of-the-art methods. It is anticipated that SOFM will become a very useful profile in the studies of protein structures and functions. IEEE |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2017.2765331 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | DocType | Volume |
Amino acids,Benchmark testing,Data mining,Feature extraction,Hidden Markov models,Kernel,protein fold recognition,protein remote homology detection,Proteins,Sequence-Order Frequency Matrix,Smith-Waterman Local Alignment algorithm,Top-n-gram | Journal | 16 |
Issue | ISSN | Citations |
1 | 15455963 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Liu | 1 | 419 | 33.30 |
| Junjie Chen | 2 | 76 | 3.24 |
| Guo M. | 3 | 6 | 1.10 |
| Xiaolong Wang | 4 | 1208 | 115.39 |