A probabilistic peptide machine for predicting hepatitis C virus protease cleavage sites.
Although various machine learning approaches have been used for predicting protease cleavage sites, constructing a probabilistic model for these tasks is still challenging. This paper proposes a novel algorithm termed as a probabilistic peptide machine where estimating probability density functions and constructing a classifier for predicting protease cleavage sites are combined into one process. The simulation based on experimentally determined Hepatitis C virus (HCV) protease cleavage data has demonstrated the success of this new algorithm.
Yang
Zheng Rong
ZR
University of Exeter, Exeter EX4 5DE, UK.
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D004789:Enzyme Activation; D008956:Models, Chemical; D010455:Peptides / Q000737:chemistry*; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*; D017361:Viral Nonstructural Proteins / Q000737:chemistry*; D012697:Serine Endopeptidases; D043583:Nucleoside-Triphosphatase; D053487:DEAD-box RNA Helicases; D000085242:Viral Proteases
D010455:Peptides; D017361:Viral Nonstructural Proteins; C084422:NS3 protein, hepatitis C virus; D012697:Serine Endopeptidases; D043583:Nucleoside-Triphosphatase; D053487:DEAD-box RNA Helicases; D000085242:Viral Proteases