DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Molecules

Doina Caragea¹  Dukka B. KC²  Kiyoko F. Aoki-Kinoshita³  Subash C. Pakhrin⁴ 

[1] Department of Computer Science, Kansas State University, Manhattan, KS 66506, USA;Department of Computer Science, Michigan Technological University, Houghton, MI 49931, USA;Glycan and Life Systems Integration Center (GaLSIC), Soka University, Tokyo 192-8577, Japan;School of Computing, Wichita State University, 1845 Fairmount St., Wichita, KS 67260, USA;

关键词: post-translation modification;    sequon;    deep neural network;    N-linked glycosylation;   

DOI  :  10.3390/molecules26237314

来源: DOAJ

【 摘 要 】

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

【 授权许可】

Unknown