Protein–ligand binding residue prediction enhancement through hybrid deep heterogeneous learning of sequence and structure data

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. Availability and implementation The web server of DELIA is available at www.csbio.sjtu.edu.cn/bioinf/delia/. Supplementary information Supplementary data are available at Bioinformatics online.

Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information

The identification of microRNA (miRNA)-binding protein residues significantly impacts several research areas, including gene regulation and expression. We propose a method, PmiRBR, which combines a novel hybrid feature with the Laplacian support vector machine (LapSVM) algorithm to predict miRNA-binding residues in protein sequences. The hybrid feature is constituted by secondary structure, conservation scores, and a novel feature, which includes evolutionary information combined with the physicochemical properties of amino acids. Performance comparisons of the various features indicate that our novel feature contributes the most to prediction improvement. Our results demonstrate that PmiRBR can achieve 85.96% overall accuracy, with 43.89% sensitivity and 90.56% specificity. PmiRBR significantly outperforms other approaches at miRNA-binding residue prediction.