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BioPhysConnectoR : Connecting sequence information and biophysical models

Hoffgaard, Franziska and Weil, P. and Hamacher, Kay (2010):
BioPhysConnectoR : Connecting sequence information and biophysical models.
In: BMC Bioinformatics, p. 199, 11, (1), [Online-Edition: http://www.biomedcentral.com/1471-2105/11/199/abstract],
[Article]

Abstract

BACKGROUND:One of the most challenging aspects of biomolecular systems
 is the understanding of the coevolution in and among the molecule(s).
 A complete, theoretical picture of the selective advantage, and thus
 a functional annotation, of (co-)mutations is still lacking. Using
 sequence-based and information theoretical inspired methods we can
 identify coevolving residues in proteins without understanding the
 underlying biophysical properties giving rise to such coevolutionary
 dynamics. Detailed (atomistic) simulations are prohibitively expensive.
 At the same time reduced molecular models are an efficient way to
 determine the reduced dynamics around the native state. The combination
 of sequence based approaches with such reduced models is therefore
 a promising approach to annotate evolutionary sequence changes.RESULTS:With
 the R package BioPhysConnectoR we provide a framework to connect
 the information theoretical domain of biomolecular sequences to biophysical
 properties of the encoded molecules - derived from reduced molecular
 models. To this end we have integrated several fragmented ideas into
 one single package ready to be used in connection with additional
 statistical routines in R. Additionally, the package leverages the
 power of modern multi-core architectures to reduce turn-around times
 in evolutionary and biomolecular design studies. Our package is a
 first step to achieve the above mentioned annotation of coevolution
 by reduced dynamics around the native state of proteins.CONCLUSIONS:BioPhysConnectoR
 is implemented as an R package and distributed under GPL 2 license.
 It allows for efficient and perfectly parallelized functional annotation
 of coevolution found at the sequence level.

Item Type: Article
Erschienen: 2010
Creators: Hoffgaard, Franziska and Weil, P. and Hamacher, Kay
Title: BioPhysConnectoR : Connecting sequence information and biophysical models
Language: English
Abstract:

BACKGROUND:One of the most challenging aspects of biomolecular systems
 is the understanding of the coevolution in and among the molecule(s).
 A complete, theoretical picture of the selective advantage, and thus
 a functional annotation, of (co-)mutations is still lacking. Using
 sequence-based and information theoretical inspired methods we can
 identify coevolving residues in proteins without understanding the
 underlying biophysical properties giving rise to such coevolutionary
 dynamics. Detailed (atomistic) simulations are prohibitively expensive.
 At the same time reduced molecular models are an efficient way to
 determine the reduced dynamics around the native state. The combination
 of sequence based approaches with such reduced models is therefore
 a promising approach to annotate evolutionary sequence changes.RESULTS:With
 the R package BioPhysConnectoR we provide a framework to connect
 the information theoretical domain of biomolecular sequences to biophysical
 properties of the encoded molecules - derived from reduced molecular
 models. To this end we have integrated several fragmented ideas into
 one single package ready to be used in connection with additional
 statistical routines in R. Additionally, the package leverages the
 power of modern multi-core architectures to reduce turn-around times
 in evolutionary and biomolecular design studies. Our package is a
 first step to achieve the above mentioned annotation of coevolution
 by reduced dynamics around the native state of proteins.CONCLUSIONS:BioPhysConnectoR
 is implemented as an R package and distributed under GPL 2 license.
 It allows for efficient and perfectly parallelized functional annotation
 of coevolution found at the sequence level.

Journal or Publication Title: BMC Bioinformatics
Volume: 11
Number: 1
Uncontrolled Keywords: Bioinformatik
Divisions: 10 Department of Biology
?? fb10_mikrobiologie ??
10 Department of Biology > Computational Biology and Simulation
20 Department of Computer Science
Date Deposited: 22 Jun 2010 12:29
Official URL: http://www.biomedcentral.com/1471-2105/11/199/abstract
Identification Number: doi:10.1186/1471-2105-11-199
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