Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning

Xu Ying, André Leier, Tatiana Marquez-Lago, Jue Xie, Antonio Jose Jimeno Yepes, James Whisstock, Campbell Wilson, Jiangning Song

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins and regions (IDP/IDR). Different from protein secondary structure (SS) prediction, the SSP prediction assumes a dynamic assignment of secondary structures that seem correlate with disordered states. In this study, we designed a single-task deep learning framework to predict IDP/IDR and SSP respectively; and multitask deep learning frameworks to allow explainable predictions of IDP/IDR using the simultaneously predicted SSP. According to independent test results, single-task deep learning models improve the prediction performance of shallow models for SSP
and IDP/IDR. Also, the prediction performance was further improved for IDP/IDR prediction when SSP prediction was simultaneously predicted in multitask models. With p53 as a use case, we demonstrate how predicted SSP is used to explain the IDP/IDR predictions for each functional region.
Original languageEnglish
Title of host publicationAMIA Annual Symposium Proceedings Volume 2020
EditorsEneida Mendonca, Bradley Malin, Karen Monsen, Theresa Walunas, Adam Wilcox
Place of PublicationUSA
PublisherAMIA Annual Symposium Proceedings Archive
Number of pages10
Publication statusPublished - 2020
EventAmerican Medical Informatics Association Symposium 2020 - Online, United States of America
Duration: 14 Nov 202018 Nov 2020 (Website) (Proceedings)


ConferenceAmerican Medical Informatics Association Symposium 2020
Abbreviated titleAMIA 2020
Country/TerritoryUnited States of America
Internet address

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