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Multi-task learning to leverage partially annotated data for PPI interface prediction
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DOI：10.1038/s41598-022-13951-2.png)
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摘要
En 中文
Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations-with data extension-reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein's functional properties of interest are only partially annotated.
Keyword:
PROTEIN-PROTEIN INTERACTIONS
SECONDARY STRUCTURE
INTERACTION SITES
RESIDUES
ROC
CONSERVATION
GENERATION
ANGLES
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期刊
IF:
3.9
论文数:
27.9W
被引数:
83.5W
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引用论文
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE受试者工作特征 (ROC) 曲线下面积的含义和用途.png)
RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏分享收藏Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learningSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
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