Neural network model of the genetic code

[Soren Brunak]

Neural network model of the genetic code is strongly correlated to  the
GES scale of amino acid transfer free energies 

N. Tolstrup, J. Toftgaard, J. Engelbrecht and S. Brunak
Centre for Biological Sequence Analysis
Department of Physical Chemistry
The Technical University of Denmark
DK-2800 Lyngby, Denmark

Journal of Molecular Biology, to appear.

Abstract 

A neural network trained to classify the 61 nucleotide triplets of  the
genetic code into twenty amino acid categories develops in its internal
representation a pattern matching the  relative  cost  of  transferring
amino acids with satisfied backbone hydrogen bonds  from  water  to  an
environment of dielectric constant of roughly  2.0.  Such  environments
are typically found in lipid membranes or in the interior of  proteins.
In learning the mapping between the  codons  and  the  categories,  the
network groups the amino acids according to the scale of transfer  free
energies developed by  Engelman,  Goldman  and  Steitz.  Several  other
scales based on internal preference statistics  also  agree  reasonably
well with the network grouping. The  network  is  able  to  relate  the
structure  of  the  genetic  code  to  quantifications  of  amino  acid
hydrophobicity-philicity more systematicly than the  numerous  attempts
made earlier. Due to its inherent non-linearity, the code is also shown
to impose decisive constraints on algorithmic analysis of  the  protein
coding potential of DNA.  


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unix>         ftp 129.142.74.40  (or ftp virus.fki.dth.dk)
Name:         anonymous
Password:     (your email address, please) 
ftp>          binary
ftp>          cd pub
ftp>          get gcode.ps.gz
ftp>          bye
unix>         gunzip gcode.ps.gz
unix>         lpr gcode.ps 

URL  ftp://virus.fki.dth.dk/pub/gcode.ps.gz