MRFalign: Protein Homology Detection through Alignment of Markov Random Fields View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Jianzhu Ma , Sheng Wang , Zhiyong Wang , Jinbo Xu

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. More... »

PAGES

173-174

References to SciGraph publications

Book

TITLE

Research in Computational Molecular Biology

ISBN

978-3-319-05268-7
978-3-319-05269-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-05269-4_13

DOI

http://dx.doi.org/10.1007/978-3-319-05269-4_13

DIMENSIONS

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