Journal
BIOINFORMATICS
Volume 36, Issue 10, Pages 3018-3027Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa110
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Funding
- National Key Research and Development Program of China [2018YFC0910500]
- National Natural Science Foundation of China [61725302, 61671288, 61903248]
- Science and Technology Commission of Shanghai Municipality [17JC1403500]
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Motivation: Knowledge of protein-ligand binding residues is important for understanding the functions of proteins and their interaction mechanisms. From experimentally solved protein structures, how to accurately identify its potential binding sites of a specific ligand on the protein is still a challenging problem. Compared with structure-alignment-based methods, machine learning algorithms provide an alternative flexible solution which is less dependent on annotated homogeneous protein structures. Several factors are important for an efficient protein-ligand prediction model, e.g. discriminative feature representation and effective learning architecture to deal with both the large-scale and severely imbalanced data. Results: In this study, we propose a novel deep-learning-based method called DELIA for protein-ligand binding residue prediction. In DELIA, a hybrid deep neural network is designed to integrate 1D sequence-based features with 2D structure-based amino acid distance matrices. To overcome the problem of severe data imbalance between the binding and nonbinding residues, strategies of oversampling in mini-batch, random undersampling and stacking ensemble are designed to enhance the model. Experimental results on five benchmark datasets demonstrate the effectiveness of proposed DELIA pipeline.
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