A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

Abstract

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).

On Inactivation of the Coronavirus Main Protease

“…The JSD, a metric quantifying differences between two probability distributions, − was utilized to conduct pairwise comparisons of the probability densities associated with the torsion angles of all residues (across both chains) across the five states of the most dominant pathway. We computed backbone ϕ and ψ and side chain χ1 and χ2 torsion angles of each residue were calculated using PyEMMA .…”

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins

“…( 31 ), DISIS ( 32 ), BindN-RF ( 33 ), DBindR ( 34 ), DBD-Threader ( 35 ), ProteDNA ( 36 ), BindN+ ( 37 ), NAPS ( 38 ), MetaDBSite ( 39 ), DNABR ( 40 ), TargetS ( 41 ), SNBRFinder ( 42 ), DisoRDPbind ( 43 , 44 ), DQPred-DBR ( 45 ), Dang et al. ( 46 ), TargetDNA ( 47 ), PRODNA ( 48 ), DRNApred ( 49 ), PDRLGB ( 50 ), ENSEMBLE-CNN ( 51 ), method by Zhang et al. ( 52 ), NucBind ( 53 ), hybridNAP ( 11 ), DNAPred ( 54 ), ProNA2020 ( 55 ), NCBRPred ( 56 ), MTDsite ( 57 ), DNAgenie ( 58 ) and DeepDISOBind ( 59 ).…”

Information Entropy for Evaluation of Wastewater Composition

“…With the development of thermodynamics in the 19th century, L. Boltzmann defined entropy as a simple function of all possible ordered states W as S = klnW, where k is the Boltzmann constant, which means that entropy increases with higher disorder of a system. J. W. Gibbs (1839-1903) substituted the number of possible states with n states with probabilities p i and derived the relationship S = −kn n i=1 p i ln p i which inspired C. E. Shannon's (1916Shannon's ( -2001 concept of information entropy [1,2].…”

“…Unlike a majority of statistical methods based on normal distribution, entropy-based statistics is applicable to any distribution and even in cases when distributions are a priori unknown Maruyama, et al [3]. It has been commonly used in physics [4,5], chemistry [6,7], informatics [8] and bioinformatics [2,9,10], image processing [11,12], for the evaluation of business organisations [13], economics and finance processes [14] and company systems performance [15,16]. The concept has been used also for the analysis of urban ecosystems [17], environmental analysis [3,[18][19][20], medical records [21][22][23] and in scientometrics [24,25].…”