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dc.contributor.authorHanson, Jack
dc.contributor.authorPeliwal, Kuldip
dc.contributor.authorLitfin, Thomas
dc.contributor.authorYang, Yuedong
dc.contributor.authorZhou, Yaoqi
dc.date.accessioned2019-07-04T12:36:37Z
dc.date.available2019-07-04T12:36:37Z
dc.date.issued2018
dc.identifier.issn1367-4803
dc.identifier.doi10.1093/bioinformatics/bty481
dc.identifier.urihttp://hdl.handle.net/10072/383639
dc.description.abstractMotivation: Accurate prediction of a protein contact map depends greatly on capturing as much contextual information as possible from surrounding residues for a target residue pair. Recently, ultra-deep residual convolutional networks were found to be state-of-the-art in the latest Critical Assessment of Structure Prediction techniques (CASP12) for protein contact map prediction by attempting to provide a protein-wide context at each residue pair. Recurrent neural networks have seen great success in recent protein residue classification problems due to their ability to propagate information through long protein sequences, especially Long Short-Term Memory (LSTM) cells. Here, we propose a novel protein contact map prediction method by stacking residual convolutional networks with two-dimensional residual bidirectional recurrent LSTM networks, and using both one-dimensional sequence-based and two-dimensional evolutionary coupling-based information. Results: We show that the proposed method achieves a robust performance over validation and independent test sets with the Area Under the receiver operating characteristic Curve (AUC) > 0.95 in all tests. When compared to several state-of-the-art methods for independent testing of 228 proteins, the method yields an AUC value of 0.958, whereas the next-best method obtains an AUC of 0.909. More importantly, the improvement is over contacts at all sequence-position separations. Specifically, a 8.95%, 5.65% and 2.84% increase in precision were observed for the top L∕10 predictions over the next best for short, medium and long-range contacts, respectively. This confirms the usefulness of ResNets to congregate the short-range relations and 2D-BRLSTM to propagate the long-range dependencies throughout the entire protein contact map ‘image’.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherOXFORD UNIV PRESS
dc.relation.ispartofpagefrom4039
dc.relation.ispartofpageto4045
dc.relation.ispartofissue23
dc.relation.ispartofjournalBIOINFORMATICS
dc.relation.ispartofvolume34
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.titleAccurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Yaoqi
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorYang, Yuedong
gro.griffith.authorLitfin, Tom
gro.griffith.authorHanson, Jack S.


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