Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning.
Xu Ying, Andre Leier, Tatiana T Marquez-Lago, Jue Xie, Antonio Jose Jimeno Yepes, James C Whisstock, Campbell Wilson, Jiangning Song
AMIA Annual Symposium Proceedings | Published : 2020
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 quantitative predictions of IDP/IDR evidenced by the simultaneously predicted SSP. According to ..View full abstract