SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides

Abstract

BackgroundHeme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed.ResultsThis work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO:0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible.ConclusionsSCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

“…Compared to earlier SCM-based methods [10, 13, 14], SCMBYK is more strictly formulated for the purpose of characterization of BY-kinases, as long as it relies on a carefully selected dataset of 26 different bacterial phyla. With the advent of next-generation sequencing, the rate at which protein databases grow is very fast.…”

“…The original SCM algorithm was first proposed by Huang et al [10] and was consequently applied to discriminate and analyze proteins with various functions [8–10, 13, 14] based on their sequence information. To train the classifier, two FASTA files are expected as the input: one for the positive training data and one for the negative training data.…”

“…SCM-PCP is a PCP mining method used to identify the important physicochemical properties (PCPs) based on the propensity scores of 20 amino acids [13]. To find a set of PCPs possibly correlated with a considered protein function, we examined the 544 indices representing different PCPs available from the AA-index database.…”

HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs

“…Derived from a scoring card method (SCM), the latest prediction method for heme binding to proteins "SCMHBP" benefits from an evaluation of heme-binding tendencies of 400 dipeptides and 20 amino acids, which is transferred onto protein sequences. Consequently, two non-redundant training datasets were designed with 747 heme-binding proteins and 747 non-heme-binding proteins, and two already existing datasets were taken into account for testing the SCMHBP, resulting in a mean accuracy of 85.9% [9,10,14]. In another approach, Zhang et al clustered 4003 X-ray structures of heme-binding proteins via Blastclust [15] with a sequence identity of less than 30% and selected 260 representatives for testing [16].…”

Heterogeneous Asymmetric Oxidation Catalysis Using Hemophore HasApf. Application in the Chemoenzymatic Deracemization of sec-Alcohols with Sodium Borohydride

“…However, the use of a HasA hemophore incorporating an iron electron-transfer system for the asymmetric oxidation (with oxygen) of secondary alcohols in organic synthesis has not yet been examined as a heterogeneous enzyme catalysis reaction [3]. However, it is generally accepted that the system of hemophore HasA secreted by host ABC transporters [4] enables heme uptake across the cell outer membrane [5] and spontaneously transforms it into the HasR receptor at the heme-binding site [6].…”