Data-driven model for the prediction of protein transmembrane regions.
We present a novel approach combining mathematical methods and artificial neural networks to predict the transmembrane regions of transmembrane proteins, considering protein sequence information alone. We have focused on developing a data-driven model based on a non-linear modelling method, the counter-propagation artificial neural network, and on mathematical descriptors defining the sequence information of transmembrane proteins with known three-dimensional structures. The developed model has proven to be promising in predicting protein transmembrane regions, with an error below 10% for the external validation set. In combination with available experimental data the model can give us a better understanding of transmembrane proteins.
Roy Choudhury
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Laboratory of Chemometrics, National Institute of Chemistry, SI-1001 Ljubljana, Slovenia.
Novic
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D000595:Amino Acid Sequence; D003198:Computer Simulation; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary
D008565:Membrane Proteins