Fatgraph models of proteins

R. C. Penner; Michael Knudsen; Carsten Wiuf; Jørgen Ellegaard Andersen

doi:10.1002/cpa.20340

Fatgraph models of proteins

R. C. Penner^*, Michael Knudsen, Carsten Wiuf, Jørgen Ellegaard Andersen

^*Corresponding author af dette arbejde

33 Citationer (Scopus)

Abstract

We introduce a new model of proteins that extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore amenable to fast computer implementation. Applications include the refinement of structural protein classifications and the prediction of geometric and other properties of proteins from their chemical structures.

Originalsprog	Engelsk
Tidsskrift	Communications on Pure and Applied Mathematics
Vol/bind	63
Udgave nummer	10
Sider (fra-til)	1249-1297
Antal sider	49
ISSN	0010-3640
DOI	https://doi.org/10.1002/cpa.20340
Status	Udgivet - 1 okt. 2010
Udgivet eksternt	Ja

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10.1002/cpa.20340

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AU - Penner, R. C.

AU - Knudsen, Michael

AU - Wiuf, Carsten

AU - Andersen, Jørgen Ellegaard

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Y1 - 2010/10/1

N2 - We introduce a new model of proteins that extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore amenable to fast computer implementation. Applications include the refinement of structural protein classifications and the prediction of geometric and other properties of proteins from their chemical structures.

AB - We introduce a new model of proteins that extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore amenable to fast computer implementation. Applications include the refinement of structural protein classifications and the prediction of geometric and other properties of proteins from their chemical structures.

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DO - 10.1002/cpa.20340

M3 - Journal article

AN - SCOPUS:77956460775

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Fatgraph models of proteins

Abstract

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