Neural networks for protein classification.
This paper describes a biomolecular classification methodology based on multilayer perceptron neural networks. The system developed is used to classify enzymes found in the Protein Data Bank. The primary goal of classification, here, is to infer the function of an (unknown) enzyme by analysing its structural similarity to a given family of enzymes. A new codification scheme was devised to convert the primary structure of enzymes into a real-valued vector. The system was tested with a different number of neural networks, training set sizes and training epochs. For all experiments, the proposed system achieved a higher accuracy rate when compared with profile hidden Markov models. Results demonstrated the robustness of this approach and the possibility of implementing fast and efficient biomolecular classification using neural networks.
Weinert
Wagner Rodrigo
WR
Laboratório de Bioinformática/CPGEI, Centro Federal de Educação Tecnológica do Paraná (CEFET-PR), Curitiba (PR), Brazil.
Lopes
Heitor Silvério
HS
D000465:Algorithms; D000595:Amino Acid Sequence; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins