title: SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.
abstract: BACKGROUND
  Heme 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.
  
  
  RESULTS
  This 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.
  
  
  CONCLUSIONS
  SCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.
authors.forename: Yi-Fan
authors.initials: YF
authors.lastname: Liou
authors.forename: Phasit
authors.initials: P
authors.lastname: Charoenkwan
authors.forename: Yerukala
authors.initials: Y
authors.lastname: Srinivasulu
authors.forename: Tamara
authors.initials: T
authors.lastname: Vasylenko
authors.forename: Shih-Chung
authors.initials: SC
authors.lastname: Lai
authors.forename: Hua-Chin
authors.initials: HC
authors.lastname: Lee
authors.forename: Yi-Hsiung
authors.initials: YH
authors.lastname: Chen
authors.forename: Hui-Ling
authors.initials: HL
authors.lastname: Huang
authors.forename: Shinn-Ying
authors.initials: SY
authors.lastname: Ho
mesh_terms: D001499:Bayes Theorem; D001665:Binding Sites; D002352:Carrier Proteins / Q000737:chemistry / Q000378:metabolism*; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry / Q000378:metabolism*; D006418:Heme / Q000737:chemistry / Q000378:metabolism*; D000081062:Heme-Binding Proteins; D006420:Hemeproteins / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D008024:Ligands; D057216:Propensity Score; D011487:Protein Conformation; D012984:Software; D060388:Support Vector Machine
chemical_list: D002352:Carrier Proteins; D004151:Dipeptides; D000081062:Heme-Binding Proteins; D006420:Hemeproteins; D008024:Ligands; D006418:Heme