ArticleBMC bioinformatics2014
SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 36 citations in OpenAlex.
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.Protein science : a publication of the Protein Society · 2025Article
- Review
- The mycoremediation potential of the armillarioids: a comparative genomics analysis.Frontiers in bioengineering and biotechnology · 2023Article
- iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides.ACS omega · 2022Article
- SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides.ACS omega · 2022Article
- A Chemical Proteomic Map of Heme-Protein Interactions.Journal of the American Chemical Society · 2022Article
- SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.Pharmaceutics · 2022Article
- Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy.Frontiers in cell and developmental biology · 2021Article
- Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.Journal of computer-aided molecular design · 2020Article
- Bioinformatics of Metalloproteins and Metalloproteomes.Molecules (Basel, Switzerland) · 2020Review
- HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs.BMC bioinformatics · 2020Article
- PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method.Cells · 2020Article
- NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence.Protein science : a publication of the Protein Society · 2020Article
- Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.International journal of molecular sciences · 2019Article
- DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network.PLoS computational biology · 2019Article
- SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.BMC bioinformatics · 2016Article
- Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information.Advances in bioinformatics · 2015Article
- SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides.BMC genomics · 2015Article
- InCoB2014: bioinformatics to tackle the data to knowledge challenge. Introduction.BMC bioinformatics · 2014Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.