Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters

Abstract

We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (Ncons) and their spatial clustering (ρe), hydrogen bond donor capability (Dp) and residue propensity (Rp). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. Rp shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, Rp can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins.

DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches

“…Additionally, binding residues are more conserved than nonbinding residues, and putative hotspots tend to occur as clusters of conserved residues . Besides the aforementioned well‐known features that can be used to characterize DNA‐binding residues, other structural properties such as packing density, surface curvature, B‐factor, residue fluctuations, and hydrogen bond donors, have also been successfully exploited. These structure‐based analyses can provide important clues to discriminate DNA‐binding residues from the protein surface.…”

SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes

“…These are evolutionary sequence conservation, positively charged residues, H-bond donor/acceptor and hydrophobic residues (Table 1). Conservation of residues was determined from multiple sequence analysis [10]. From the MSA result it was evident that in five columns (out of 25) the conservation score was very high.…”

“…Side chain groups of positively charged residues as arginine, histidine and lysine within the interaction site were assumed to be capable of getting involved in H-bonding during the interaction [10, 13]. Asparagine, arginine, glutamine, cysteine, histidine, serine, threonine, tryptophan and tyrosine were calculated as the H-bond donor, while aspartic acid and glutamic acid were calculated as the H-bond acceptor.…”

“…It has been gradually applied to protein secondary structure prediction [8], tumor cell prediction and DNA binding residues prediction [9] and many more pattern classification problems in biology related areas. Recently [10] SVM has been used as a new and promising technique for detecting DNA binding site prediction for TF proteins.…”

PiDNA: predicting protein–DNA interactions with structural models

“…Many studies have developed structure-based computational methods for predicting protein–DNA interactions (3–5). Most of the studies focus on predicting protein functions or DNA-binding residues based on structure models (4–8), and the prediction of binding residues has achieved a high degree of accuracy (6,8). Many potential functions, including physics-based and knowledge-based (7,9–11), have been developed for improving protein–DNA docking (11–13).…”