Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility.
BACKGROUND
Fold recognition programs align a probe protein sequence onto protein three-dimensional (3D) structure templates. The alignment between the probe sequence and the most suitable template can be used to predict the 3D structure and often biological function of the probe. Here we present a new threading scoring function of protein sequence-structure compatibility. An artificial neural network model is trained to predict compatibility of amino acid side-chains with structural environments. Log-odds scores of predicted probabilities from this model can then be used to construct protein sequence-structure alignments.


RESULTS
Our model is tested on discrimination of native and decoy protein 3D structures. With a residue level structural description, its performance is comparable to those of pseudo-energy functions with atom level structural descriptions, better than the two functions with residue level structural descriptions.


BACKGROUND
The C++ source code of our neural network model is available at http://mathbio.nimr.mrc.ac.uk/~kxlin.
Lin
Kuang
K
Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill NW7 1AA, UK.
May
Alex C W
AC
Taylor
William R
WR
D000595:Amino Acid Sequence; D030562:Databases, Protein; D008957:Models, Genetic; D008958:Models, Molecular; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software; D013269:Stochastic Processes
D011506:Proteins