Variable site-occupancy classification of N-linked glycosylation using artificial neural networks.
A novel neural-network-based model has been developed for the prediction of N-linked glycosylation characteristics related to glycosylation site-occupancy. Intracellular oligosaccharide transfer to a polypeptide is known to be either robust or dependent upon culture conditions during pharmaceutical production. This glycan attachment is classified by the model as robust or variable and is based on an input of the polypeptide primary sequence around the site of glycosylation. The glycosylation model utilizes multiple recurrent neural networks followed by a perceptron classifier. The input length of the polypeptide chain around the site of glycosylation (glycosylation window) was optimized through multiple independent training sessions. Incorporation of five residues prior (n - 5) to the site of glycosylation (n) and four residues beyond (n + 4) the glycan attachment site led to optimal network performance. The size of the glycosylation window for site-occupancy determination is much larger than has been previously reported. This model was developed to evaluate the effects of theoretical polypeptide mutations on glycosylation site-occupancy characteristics. Following correct prediction of the model testing data set, 20 independent networks were used to predict site-occupancy characteristics of wild-type and mutants of the rabies virus glycoprotein (rgp). Simulation results strongly correlated with previously published experimental results (Kasturi, L.; Hegang, C.; Shakin-Eshleman, S. H. Regulation of N-linked core glycosylation: use of a site-directed mutagenesis approach to identify Asn-Xaa-Ser/Thr sequons that are poor oligosacchride acceptors. Biochem. J. 1997, 323, 415-419. Mellquist, J. L.; Kasturi, L.; Spitalnik, S. L.; Shakin-Eshleman, S. H. The amino acid following an Asn-X-Ser/Thr sequon is an important determinant of N-linked core glycosylation efficiency. Biochemistry 1998, 37, 6833-6837). Further simulations on purely theoretical sequences suggested that influences of charged residues were a subset of multiple mechanisms in the determination of glycosylation site-occupancy.
Senger
Ryan S
RS
Department of Chemical Engineering, Texas Tech University, MS 43121, Lubbock, 79409-3121, USA.
Karim
M Nazmul
MN
D001665:Binding Sites; D001709:Biotechnology; D019295:Computational Biology; D003199:Computer Systems; D006023:Glycoproteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D006031:Glycosylation; D009154:Mutation; D016571:Neural Networks, Computer; D009844:Oligosaccharides / Q000737:chemistry / Q000378:metabolism; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D011499:Protein Processing, Post-Translational; D011820:Rabies virus / Q000235:genetics / Q000378:metabolism; D014764:Viral Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism
D006023:Glycoproteins; D009844:Oligosaccharides; D010455:Peptides; D014764:Viral Proteins