Bayesian alignment of proteins via Delaunay tetrahedralization

An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the bes...

Full description

Saved in:
Bibliographic Details
Published inJournal of applied statistics Vol. 42; no. 5; pp. 1064 - 1079
Main Authors Najibi, S.M., Faghihi, M.R., Golalizadeh, M., Arab, S.S.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 04.05.2015
Taylor & Francis Ltd
Subjects
Alignment
Applied statistics
Bayesian analysis
Bioinformatics
Biomolecules
Convergence
Mathematical models
MCMC
primary structure
protein alignment
Proteins
shape analysis
Similarity
size-and-shape distance
Statistical methods
structural alignment
Studies
Online AccessGet full text

Cover

More Information
Summary:An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the best alignment by maximizing the number of matched features. In this paper, after reviewing statistical models for matching the structural biomolecule, it is shown that local alignment based on the Delaunay tetrahedralization (DT) can be used for Bayesian alignment of proteins. In this method, we use DT to add a priori structural information of protein in the Bayesian methodology. We demonstrate that this method shows advantages over competing methods in achieving a global alignment of proteins, accelerating the convergence rate and improving the parameter estimates.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2014.995605