ArticlePloS one2014
Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Protein Structural Model Selection Informed by Comparison of Predicted Ligand Binding Poses.Journal of chemical information and modeling · 2025Article
- Estimation of model accuracy by a unique set of features and tree-based regressor.Scientific reports · 2022Article
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- Decoy selection for protein structure prediction via extreme gradient boosting and ranking.BMC bioinformatics · 2020Article
- Article
- MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.BMC bioinformatics · 2020Review
- QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.Bioinformatics (Oxford, England) · 2020Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors.ACS omega · 2020Article
- Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites.Current genomics · 2020Review
- A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.Current genomics · 2020Review
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
- Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.International journal of molecular sciences · 2019Article
- Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
- Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2019Article
- Selecting Near-Native Protein Structures from Predicted Decoy Sets Using Ordered Graphlet Degree Similarity.Genes · 2019Article
- RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold.PloS one · 2019Article
- 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
- Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.PloS one · 2019Article
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3 authors.
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Abstract
Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TM-score of the model selected by RFMQA (TMRF) and the best server model (TMbest) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TMRF and TMbest over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from http://lee.kias.re.kr/RFMQA/RFMQA_eval.tar.gz.
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