ArticleBMC bioinformatics2010
Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Article
- Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.International journal of molecular sciences · 2020Article
- Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.BMC bioinformatics · 2019Article
- SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences.Bioinformatics (Oxford, England) · 2019Article
- Special Protein Molecules Computational Identification.International journal of molecular sciences · 2018Article
- Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.International journal of molecular sciences · 2017Article
- DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction.BioMed research international · 2017Article
- Progress and challenges in predicting protein interfaces.Briefings in bioinformatics · 2016Review
- Scoring docking conformations using predicted protein interfaces.BMC bioinformatics · 2014Article
- LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.BMC bioinformatics · 2014Article
- eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.Journal of computer-aided molecular design · 2013Article
- Prediction of heme binding residues from protein sequences with integrative sequence profiles.Proteome science · 2012Article
- HomPPI: a class of sequence homology based protein-protein interface prediction methods.BMC bioinformatics · 2011Article
- Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.PloS one · 2011Article
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2 authors.
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Abstract
backgroundProtein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.
resultsWe propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.
conclusionsThe integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance. AVAILABILITY: Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.
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