ArticleBMC bioinformatics2009
Prediction of protein binding sites in protein structures using hidden Markov support vector machine.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
- IDP⁻CRF: Intrinsically Disordered Protein/Region Identification Based on Conditional Random Fields.International journal of molecular sciences · 2018Article
- Protein Remote Homology Detection Based on an Ensemble Learning Approach.BioMed research international · 2016Article
- Progress and challenges in predicting protein interfaces.Briefings in bioinformatics · 2016Review
- Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.Molecular genetics and genomics : MGG · 2015Article
- Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.BMC systems biology · 2015Article
- Survey of Natural Language Processing Techniques in Bioinformatics.Computational and mathematical methods in medicine · 2015Review
- Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico.BioMed research international · 2015Review
- Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.International journal of molecular sciences · 2014Article
- Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.Bioinformatics (Oxford, England) · 2014Article
- Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions.TheScientificWorldJournal · 2014Article
- Using distances between Top-n-gram and residue pairs for protein remote homology detection.BMC bioinformatics · 2014Article
- Predicting the types of J-proteins using clustered amino acids.BioMed research international · 2014Article
- enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.BioMed research international · 2014Article
- Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.TheScientificWorldJournal · 2014Article
- Predictions of Protein-Protein Interfaces within Membrane Protein Complexes.Avicenna journal of medical biotechnology · 2013Article
- Predicting protein-protein interface residues using local surface structural similarity.BMC bioinformatics · 2012Article
- Machine learning on normalized protein sequences.BMC research notes · 2011Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.
resultsIn this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.
conclusionThe improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.
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