DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction.
DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction.
复制标题
DeepNGlyPred:一种基于深度神经网络的人类N-连接糖基化位点预测方法。
DOI:
10.3390/molecules26237314
复制
发表时间:
2021-12-02
期刊:
影响因子:
--
通讯作者:
Kc DB
中科院分区:
文献类型:
--
作者:
Pakhrin SC;Aoki-Kinoshita KF;Caragea D;Kc DB
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.
登录
查看更多内容
影响因子:
7.5
作者:
Boscher, Cecile;Dennis, James W.;Nabi, Ivan R.
通讯作者:
Nabi, Ivan R.
影响因子:
3
作者:
Caragea C;Sinapov J;Silvescu A;Dobbs D;Honavar V
通讯作者:
Honavar V
影响因子:
2.9
作者:
Klausen, Michael Schantz;Jespersen, Martin Closter;Marcatili, Paolo
通讯作者:
Marcatili, Paolo
影响因子:
5.8
作者:
Hanson, Jack;Yang, Yuedong;Zhou, Yaoqi
通讯作者:
Zhou, Yaoqi
影响因子:
5.8
作者:
Li, Fuyi;Li, Chen;Song, Jiangning
通讯作者:
Song, Jiangning