SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties
Abstract Motivation Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glyc...
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| Published in | Bioinformatics Vol. 35; no. 20; pp. 4140 - 4146 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.10.2019
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| Online Access | Get full text |
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| Abstract | Abstract
Motivation
Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.
Results
The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features.
Availability and implementation
http://sparks-lab.org/server/SPRINT-Gly/
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation, and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.
The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features.
http://sparks-lab.org/server/SPRINT-Gly/.
Supplementary data are available at Bioinformatics online. Abstract Motivation Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively. Results The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features. Availability and implementation http://sparks-lab.org/server/SPRINT-Gly/ Supplementary information Supplementary data are available at Bioinformatics online. Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.MOTIVATIONProtein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.The method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features.RESULTSThe method, called SPRINT-Gly, achieved consistent results between ten-fold cross validation and independent test for predicting human and mouse glycosylation sites. For N-glycosylation, a mouse-trained model performs equally well in human glycoproteins and vice versa, however, due to significant differences in O-linked sites separate models were generated. Overall, SPRINT-Gly is 18% and 50% higher in Matthews correlation coefficient than the next best method compared in N-linked and O-linked sites, respectively. This improved performance is due to the inclusion of novel structure and sequence-based features.http://sparks-lab.org/server/SPRINT-Gly/.AVAILABILITY AND IMPLEMENTATIONhttp://sparks-lab.org/server/SPRINT-Gly/.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
| Author | Golchin, Maryam Campbell, Matthew P Zhou, Yaoqi Taherzadeh, Ghazaleh Dehzangi, Abdollah |
| Author_xml | – sequence: 1 givenname: Ghazaleh surname: Taherzadeh fullname: Taherzadeh, Ghazaleh organization: School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia – sequence: 2 givenname: Abdollah surname: Dehzangi fullname: Dehzangi, Abdollah organization: Department of Computer Science, Morgan State University, Baltimore, MD, USA – sequence: 3 givenname: Maryam surname: Golchin fullname: Golchin, Maryam organization: School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia – sequence: 4 givenname: Yaoqi orcidid: 0000-0002-9958-5699 surname: Zhou fullname: Zhou, Yaoqi email: yaoqi.zhou@griffith.edu.au organization: School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia – sequence: 5 givenname: Matthew P orcidid: 0000-0002-9525-792X surname: Campbell fullname: Campbell, Matthew P email: m.campbell2@griffith.edu.au organization: Institute for Glycomics, Griffith University, Parklands Drive, Gold Coast, QLD, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30903686$$D View this record in MEDLINE/PubMed |
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Motivation
Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses,... Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling,... |
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| Title | SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties |
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