ProDomAs, protein domain assignment algorithm using center-based clustering and independent dominating set

ABSTRACT Decomposition of structural domains is an essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods bec...

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Veröffentlicht in:Proteins, structure, function, and bioinformatics Jg. 82; H. 9; S. 1937 - 1946
Hauptverfasser: Ansari, Elnaz Saberi, Eslahchi, Changiz, Pezeshk, Hamid, Sadeghi, Mehdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.09.2014
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Amino Acid Sequence
Benchmarks
bottom-up algorithm
Cluster Analysis
compactness principle of structural domains
Computational Biology
Decomposition
discontinuous domains
domain boundary detection
graph theoretical approach
Performance assessment
Protein Structure, Tertiary
Proteins - chemistry
Proteins - classification
Proteomics
Sequence Analysis, Protein
discontinuous domains
compactness principle of structural domains
domain boundary detection
bottom-up algorithm
graph theoretical approach
ISSN:0887-3585, 1097-0134, 1097-0134
Online-Zugang:Volltext
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Zusammenfassung:ABSTRACT Decomposition of structural domains is an essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods becomes more essential. In this article, we introduce a bottom‐up algorithm for assigning protein domains using a graph theoretical approach. This algorithm is based on a center‐based clustering approach. For constructing initial clusters, members of an independent dominating set for the graph representation of a protein are considered as the centers. A distance matrix is then defined for these clusters. To obtain final domains, these clusters are merged using the compactness principle of domains and a method similar to the neighbor‐joining algorithm considering some thresholds. The thresholds are computed using a training set consisting of 50 protein chains. The algorithm is implemented using C++ language and is named ProDomAs. To assess the performance of ProDomAs, its results are compared with seven automatic methods, against five publicly available benchmarks. The results show that ProDomAs outperforms other methods applied on the mentioned benchmarks. The performance of ProDomAs is also evaluated against 6342 chains obtained from ASTRAL SCOP 1.71. ProDomAs is freely available at http://www.bioinf.cs.ipm.ir/software/prodomas. Proteins 2014; 82:1937–1946. © 2014 Wiley Periodicals, Inc.
Bibliographie:istex:7598EC3298E4E2B60F2EF3A12CF0616B89E8DEB3
ArticleID:PROT24547
ark:/67375/WNG-PLJ7CWT0-C
School of Biological Science, Institute for Research in Fundamental Science (IPM)
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ISSN:0887-3585
1097-0134
1097-0134
DOI:10.1002/prot.24547