ArticleBMC bioinformatics2016
PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility.
Article in BMC bioinformatics, 2016. 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.
- Sequence-Based Prediction for Protein Solvent Accessibility.International journal of molecular sciences · 2025Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.Protein science : a publication of the Protein Society · 2023Article
- Interpretable machine learning prediction of all-cause mortality.Communications medicine · 2022Article
- Article
- Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences.Frontiers in molecular biosciences · 2020Article
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.BMC bioinformatics · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.BMC bioinformatics · 2018Article
- M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning.Molecular therapy. Nucleic acids · 2018Article
- Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.Biomolecules · 2018Article
- SDADB: a functional annotation database of protein structural domains.Database : the journal of biological databases and curation · 2018Article
- A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.BMC bioinformatics · 2017Article
- A boosting approach for prediction of protein-RNA binding residues.BMC bioinformatics · 2017Article
- Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.Molecules (Basel, Switzerland) · 2017Article
- Accurate prediction of protein relative solvent accessibility using a balanced model.BioData mining · 2017Article
- Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.PloS one · 2017Article
- A hydrophobic spine stabilizes a surface-exposed α-helix according to analysis of the solvent-accessible surface area.BMC bioinformatics · 2016Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA.
resultsExperimental results obtained from 5-fold cross-validation based on the Manesh-215 dataset show that the mean absolute error (MAE) and the Pearson correlation coefficient (PCC) of PredRSA are 9.0 % and 0.75, respectively, which are better than that of the existing methods. Moreover, we evaluate the performance of PredRSA using an independent test set of 68 proteins. Compared with the state-of-the-art approaches (SPINE-X and ASAquick), PredRSA achieves a significant improvement on the prediction quality.
conclusionsOur experimental results show that the Gradient Boosted Regression Trees algorithm and the novel feature combination are quite effective in relative solvent accessibility prediction. The proposed PredRSA method could be useful in assisting the prediction of protein structures by applying the predicted RSA as useful restraints.
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