Abstract
Sequence-based protein homology detection has been extensively studied, but it still remains very challenging for remote homologs with divergent sequences. So far the most sensitive method for homology detection is based upon comparison of protein sequence profiles, which are usually derived from multiple sequence alignment (MSA) of sequence homologs in a protein family and represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model). HMM is more sensitive than PSSM because the former contains position-specific gap information and also takes into account correlation among sequentially adjacent residues. The main issue with HMM lies in that it makes use of only position-specific amino acid mutation patterns and very short-range residue correlation, but not long-range residue interaction. However, remote homologs may have very divergent sequences and are only similar at the level of (long-range) residue interaction pattern, which is not encoded in current popular PSSM or HMM models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, S., Ma, J., Peng, J., Xu, J.: Protein structure alignment beyond spatial proximity. Scientific Reports 3 (2013)
Zhao, F., Xu, J.: A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study. Structure 20(6), 1118–1126 (2012)
Wang, Z., Xu, J.: Predicting protein contact map using evolutionary and physical constraints by integer programming. Bioinformatics 29(13), i266–i273 (2013)
Ma, J., Wang, S., Zhao, F., Xu, J.: Protein threading using context-specific alignment potential. Bioinformatics 29(13), i257–i265 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ma, J., Wang, S., Wang, Z., Xu, J. (2014). MRFalign: Protein Homology Detection through Alignment of Markov Random Fields. In: Sharan, R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science(), vol 8394. Springer, Cham. https://doi.org/10.1007/978-3-319-05269-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-05269-4_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05268-7
Online ISBN: 978-3-319-05269-4
eBook Packages: Computer ScienceComputer Science (R0)