ArticleProteome science2012
Prediction of heme binding residues from protein sequences with integrative sequence profiles.
Article in Proteome science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 39 citations in OpenAlex.
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs.BMC bioinformatics · 2020Article
- Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion.Frontiers in genetics · 2020Article
- RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites.Frontiers in bioengineering and biotechnology · 2020Article
- PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.Frontiers in bioengineering and biotechnology · 2020Article
- T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.Frontiers in microbiology · 2020Article
- STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.Frontiers in bioengineering and biotechnology · 2019Article
- Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm.International journal of molecular sciences · 2018Article
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
- PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method.Frontiers in microbiology · 2018Article
- SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.BMC bioinformatics · 2014Article
- PPIcons: identification of protein-protein interaction sites in selected organisms.Journal of molecular modeling · 2013Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe heme-protein interactions are essential for various biological processes such as electron transfer, catalysis, signal transduction and the control of gene expression. The knowledge of heme binding residues can provide crucial clues to understand these activities and aid in functional annotation, however, insufficient work has been done on the research of heme binding residues from protein sequence information.
methodsWe propose a sequence-based approach for accurate prediction of heme binding residues by a novel integrative sequence profile coupling position specific scoring matrices with heme specific physicochemical properties. In order to select the informative physicochemical properties, we design an intuitive feature selection scheme by combining a greedy strategy with correlation analysis.
resultsOur integrative sequence profile approach for prediction of heme binding residues outperforms the conventional methods using amino acid and evolutionary information on the 5-fold cross validation and the independent tests.
conclusionsThe novel feature of an integrative sequence profile achieves good performance using a reduced set of feature vector elements.
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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.