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A MODIFIED STACK DECODER FOR PROTEIN SECONDARY STRUCTURE PREDICTION Zafer Aydin, Toygar Akgun, Yucel Altunbasak
 

Summary: A MODIFIED STACK DECODER FOR PROTEIN SECONDARY STRUCTURE PREDICTION
Zafer Aydin, Toygar Akgun, Yucel Altunbasak
Center for Signal and Image Processing
Georgia Institute of Technology
Atlanta, Georgia, 30332-0250
Email: {aydinz,takgun,yucel}@ece.gatech.edu
ABSTRACT
Secondary structure prediction is an important step in deter-
mining the structure and function of the proteins. A funda-
mental assumption of the current Bayesian secondary struc-
ture prediction methods is the conditional independence of
residues which occur in distinct segments [1]. This assump-
tion enables the exact calculation of posterior probabilities
by using pre-determined probabilistic models. However,
this assumption is clearly violated in the case of protein
sequences due to the existence of structural motifs which
rely on sequentially distant segments interacting in three-
dimensional space, including -sheets. It has been sug-
gested that the inability to capture such nonlocal interac-
tions may be the main reason for the low accuracy typically

  

Source: Altunbasak, Yucel - School of Electrical and Computer Engineering, Georgia Institute of Technology
Aydin, Zafer - Department of Genome Sciences, University of Washington at Seattle

 

Collections: Biology and Medicine; Engineering