PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine

• Published in: BMC bioinformatics Vol. 19; no. Suppl 19; p. 522
• Main Authors: Deng, Lei, Pan, Juan, Xu, Xiaojie, Yang, Wenyi, Liu, Chuyao, Liu, Hui
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.12.2018; BioMed Central; BMC
• Subjects: Algorithms; Amino acids; Artificial intelligence; Binding; Binding sites; Bioinformatics; Classification; Computer applications; Datasets; Deoxyribonucleic acid; DNA; DNA binding; DNA-binding residue; Feature extraction; Incremental feature selection; Learning algorithms; Light gradient boosting; Machine learning; Methods; Nucleotide sequence; Performance evaluation; Physiological aspects; Prediction models; Proteins; Random forest; Residues; State of the art; Support vector machines; China; Incremental feature selection; Random forest; DNA-binding residue; Light gradient boosting
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2527-1

Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability. Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches. PDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.