ArticleBMC bioinformatics2007
Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.International journal of molecular sciences · 2020Article
- Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features.Molecules (Basel, Switzerland) · 2018Article
- Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology.Scientific reports · 2017Article
- Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.BMC systems biology · 2016Article
- Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.BMC bioinformatics · 2016Article
- Progress and challenges in predicting protein interfaces.Briefings in bioinformatics · 2016Review
- Algorithmic approaches to protein-protein interaction site prediction.Algorithms for molecular biology : AMB · 2015Article
- Principal components analysis of protein sequence clusters.Journal of structural and functional genomics · 2014Article
- Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles.Journal of chemical theory and computation · 2013Article
- SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes.Bioinformation · 2013Article
- Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.PloS one · 2011Article
- Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information.BMC bioinformatics · 2010Article
- Prediction of protein binding sites in protein structures using hidden Markov support vector machine.BMC bioinformatics · 2009Article
- Using support vector machine combined with post-processing procedure to improve prediction of interface residues in transient complexes.The protein journal · 2009Article
- Improved prediction of protein binding sites from sequences using genetic algorithm.The protein journal · 2009Article
- A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.BMC bioinformatics · 2008Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRecognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.
resultIn this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.
conclusionAlthough there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.
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