Prediction of protein folding class using global description of amino acid sequence.
We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.
Dubchak
I
I
Department of Chemistry, University of California, Berkeley 94720, USA.
Muchnik
I
I
Holbrook
S R
SR
Kim
S H
SH
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D003198:Computer Simulation; D016208:Databases, Factual; D008956:Models, Chemical; D016571:Neural Networks, Computer; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000145:classification; D015203:Reproducibility of Results; D012997:Solvents
D000596:Amino Acids; D011506:Proteins; D012997:Solvents