ArticleBMC bioinformatics2012
Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.International journal of molecular sciences · 2026Article
- APIPred: An XGBoost-Based Method for Predicting Aptamer-Protein Interactions.Journal of chemical information and modeling · 2024Article
- S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE.Frontiers in genetics · 2024Article
- Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.International journal of molecular sciences · 2021Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- PPAI: a web server for predicting protein-aptamer interactions.BMC bioinformatics · 2020Article
- Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.BMC bioinformatics · 2019Article
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- A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.BioMed research international · 2018Article
- Global discovery of protein kinases and other nucleotide-binding proteins by mass spectrometry.Mass spectrometry reviews · 2016Review
- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
- iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Molecular genetics and genomics : MGG · 2016Article
- An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.International journal of molecular sciences · 2015Article
- JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.BioMed research international · 2015Article
- Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble.BMC bioinformatics · 2014Article
- Simplified sequence-based method for ATP-binding prediction using contextual local evolutionary conservation.Algorithms for molecular biology : AMB · 2014Article
- Application of Hybrid Functional Groups to Predict ATP Binding Proteins.ISRN computational biology · 2014Article
- Isotope-coded ATP probe for quantitative affinity profiling of ATP-binding proteins.Analytical chemistry · 2013Article
- Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.BMC bioinformatics · 2013Article
- Prediction of drugs target groups based on ChEBI ontology.BioMed research international · 2013Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAdenosine-5'-triphosphate (ATP) is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex.
resultsIn this paper, we propose a novel framework for predicting the proteins' functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target's knowledge and multiple sources of descriptors can be complementary.
conclusionsPrediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.
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