Abstract
The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).
Highlights
Interactions between proteins and DNA play essential roles for controlling of several biological processes such as transcription, translation, DNA replication, and gene regulation [1,2,3]
We introduce new sequence-based features using Jensen–Shannon divergence (JSD) to improve the performance of previous machine learning approaches in identification of DNA-binding residues in proteins
Using JSD, we calculate the divergences between observed amino acid distributions in multiple sequence alignments (MSAs) of proteins under study and the background distribution which is calculated according to amino acid counts at non-binding residue positions in MSAs
Summary
Interactions between proteins and DNA play essential roles for controlling of several biological processes such as transcription, translation, DNA replication, and gene regulation [1,2,3]. An important step to understand the underlying molecular mechanisms of these interactions is the identification of DNA-binding residues in proteins. These residues can provide a great insight into the protein function which leads to gene expression and could facilitate the generation of new drugs [4,5]. To overcome the difficulty of experimental approaches, it is highly desired to develop fast and reliable computational methods for the prediction of DNA-binding residues. For this purpose, several state-of-the-art prediction methods have been developed for the automated identification of those residues. Such methods can be assigned into two main categories: (i) based on the information observed from structure and sequence in a collective manner; (ii) based on the features derived directly
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