### Abstract

The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.

Original language | English |
---|---|

Pages (from-to) | 28-38 |

Number of pages | 11 |

Journal | Current Protein and Peptide Science |

Volume | 9 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2008 |

### Fingerprint

### Keywords

- Computational biology
- Molecular dynamics
- Protein folding
- Recurrence quantification analysis
- Systems biology

### ASJC Scopus subject areas

- Biochemistry
- Cell Biology
- Molecular Biology

### Cite this

*Current Protein and Peptide Science*,

*9*(1), 28-38. https://doi.org/10.2174/138920308783565705

**Proteins as networks : Usefulness of graph theory in protein science.** / Krishnan, Arun; Zbilut, Joseph P.; Tomita, Masaru; Giuliani, Alessandro.

Research output: Contribution to journal › Article

*Current Protein and Peptide Science*, vol. 9, no. 1, pp. 28-38. https://doi.org/10.2174/138920308783565705

}

TY - JOUR

T1 - Proteins as networks

T2 - Usefulness of graph theory in protein science

AU - Krishnan, Arun

AU - Zbilut, Joseph P.

AU - Tomita, Masaru

AU - Giuliani, Alessandro

PY - 2008

Y1 - 2008

N2 - The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.

AB - The network paradigm is based on the derivation of emerging properties of studied systems by their representation as oriented graphs: any system is traced back to a set of nodes (its constituent elements) linked by edges (arcs) correspondent to the relations existing between the nodes. This allows for a straightforward quantitative formalization of systems by means of the computation of mathematical descriptors of such graphs (graph theory). The network paradigm is particularly useful when it is clear which elements of the modelled system must play the role of nodes and arcs respectively, and when topological constraints have a major role with respect to kinetic ones. In this review we demonstrate how nodes and arcs of protein topology are characterized at different levels of definition: 1. Recurrence matrix of hydrophobicity patterns along the sequence 2. Contact matrix of alpha carbons of 3D structures 3. Correlation matrix of motions of different portion of the molecule in molecular dynamics. These three conditions represent different but potentially correlated reticular systems that can be profitably analysed by means of network analysis tools.

KW - Computational biology

KW - Molecular dynamics

KW - Protein folding

KW - Recurrence quantification analysis

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=41949141017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=41949141017&partnerID=8YFLogxK

U2 - 10.2174/138920308783565705

DO - 10.2174/138920308783565705

M3 - Article

C2 - 18336321

AN - SCOPUS:41949141017

VL - 9

SP - 28

EP - 38

JO - Current Protein and Peptide Science

JF - Current Protein and Peptide Science

SN - 1389-2037

IS - 1

ER -