Abstract
BackgroundIdentifying 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.ResultsHere, 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.ConclusionsPDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.
Highlights
Th protein-DNA interaction is one of the central issues in molecular biology and widely exists in various biological activities in living organisms, such as DNA replication, repair, and modification processes
A number of computational approaches have been focused on applying machine learning algorithms to build prediction models based on sequence and structural information
Our experiments show that PDRLGB significantly outperforms other state-of-the-art DNA-binding residue prediction approaches
Summary
Th protein-DNA interaction is one of the central issues in molecular biology and widely exists in various biological activities in living organisms, such as DNA replication, repair, and modification processes. To understand the recognition mechanism of protein-DNA complexes, researchers often focus on protein-DNA binding sites especially the interface residues that bind DNA Experimental approach such as electrophoretic mobility shift assays (EMSAs) [1, 2], conventional chromatin immunoprecipitation (ChIP) [3], X-ray crystallography [4], PNA (peptide nucleic acid)-assisted identification of RNA binding proteins (PAIR) [5], and NMR spectroscopy [6] have been applied to expose the DNA binding amino acids. These laboratory methods are expensive and time-consuming. 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
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