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dc.contributor.authorYuan, Qianmu
dc.contributor.authorChen, Jianwen
dc.contributor.authorZhao, Huiying
dc.contributor.authorZhou, Yaoqi
dc.contributor.authorYang, Yuedong
dc.date.accessioned2021-10-06T02:12:06Z
dc.date.available2021-10-06T02:12:06Z
dc.date.issued2021
dc.identifier.issn1367-4803
dc.identifier.doi10.1093/bioinformatics/btab643
dc.identifier.urihttp://hdl.handle.net/10072/408619
dc.description.abstractMOTIVATION: Protein-protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. RESULTS: We propose a deep graph-based framework GraphPPIS (deep Graph convolutional network for Protein-Protein Interacting Site prediction) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to 8 layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. AVAILABILITY: The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofjournalBioinformatics
dc.subject.fieldofresearchBioinformatics and computational biology
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode3102
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode31
dc.subject.fieldofresearchcode46
dc.titleStructure-aware protein-protein interaction site prediction using deep graph convolutional network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationYuan, Q; Chen, J; Zhao, H; Zhou, Y; Yang, Y, Structure-aware protein-protein interaction site prediction using deep graph convolutional network, Bioinformatics, 2021
dcterms.dateAccepted2021-09-03
dc.date.updated2021-10-01T02:00:52Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.
gro.rights.copyright© 2021 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Structure-aware protein-protein interaction site prediction using deep graph convolutional network, Bioinformatics, 2021 is available online at: https://doi.org/10.1093/bioinformatics/btab643.
gro.hasfulltextFull Text
gro.griffith.authorYang, Yuedong
gro.griffith.authorZhou, Yaoqi


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