Structural relatedness via flow networks in protein sequence space

Zakharia M. Frenkel; Zeev M. Frenkel; Edward N. Trifonov; Sagi Snir

doi:10.1016/j.jtbi.2009.07.004

Structural relatedness via flow networks in protein sequence space

Zakharia M. Frenkel, Zeev M. Frenkel, Edward N. Trifonov, Sagi Snir

Department of Evolutionary and Environmental Biology

Research output: Contribution to journal › Article › peer-review

Abstract

A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.

Original language	English
Pages (from-to)	438-444
Number of pages	7
Journal	Journal of Theoretical Biology
Volume	260
Issue number	3
DOIs	https://doi.org/10.1016/j.jtbi.2009.07.004
State	Published - 7 Oct 2009

Bibliographical note

Funding Information:
The work has been supported by the Center for Complexity Science Grant GR2006-018.

Keywords

Network analysis
Protein sequence analysis
Protein structure prediction
Sequence annotation

ASJC Scopus subject areas

Statistics and Probability
Modeling and Simulation
General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology
General Agricultural and Biological Sciences
Applied Mathematics

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10.1016/j.jtbi.2009.07.004

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title = "Structural relatedness via flow networks in protein sequence space",

abstract = "A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.",

keywords = "Network analysis, Protein sequence analysis, Protein structure prediction, Sequence annotation",

author = "Frenkel, {Zakharia M.} and Frenkel, {Zeev M.} and Trifonov, {Edward N.} and Sagi Snir",

note = "Funding Information: The work has been supported by the Center for Complexity Science Grant GR2006-018. ",

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AU - Frenkel, Zakharia M.

AU - Frenkel, Zeev M.

AU - Trifonov, Edward N.

AU - Snir, Sagi

N1 - Funding Information: The work has been supported by the Center for Complexity Science Grant GR2006-018.

PY - 2009/10/7

Y1 - 2009/10/7

N2 - A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.

AB - A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.

KW - Network analysis

KW - Protein sequence analysis

KW - Protein structure prediction

KW - Sequence annotation

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SN - 0022-5193

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SP - 438

EP - 444

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

IS - 3

ER -

Structural relatedness via flow networks in protein sequence space

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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