ArticleJournal of chemical information and modeling2018
ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons.
Article in Journal of chemical information and modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A grass-specific structural myosin VIII regulates protoxylem development and hydraulic conductance in sorghum.Plant physiology · 2026Article
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- Nature Counts to Three: Universal Mg-Pinch Motif Polarizes the Cleaved Bond in NTP-Processing Enzymes.Journal of the American Chemical Society · 2026Article
- A Novel Weighted Ensemble Framework of Transformer and Deep Q-Network for ATP-Binding Site Prediction Using Protein Language Model Features.International journal of molecular sciences · 2026Article
- Levels of extracellular ATP in growth zones of Arabidopsis primary roots are changed by altered expression of apyrase enzymes.Plant signaling & behavior · 2025Article
- Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network.Scientific reports · 2025Article
- Predicting the binding residues of four small molecule ligands by utilizing ensemble algorithms with additional correlation features.Scientific reports · 2025Article
- RTK_RAG: Leveraging Retrieval Augmented Generation with Multi-Window Convolutional Neural Networks for Superior ATP Binding Site Prediction in Receptor Tyrosine Kinases.Journal of chemical information and modeling · 2025Article
- AScience advances · 2025Article
- Improving Identification of Drug-Target Binding Sites Based on Structures of Targets Using Residual Graph Transformer Network.Biomolecules · 2025Article
- Unveiling the biosynthesis mechanism of novel lantibiotic homicorcin: an in silico analysis.Scientific reports · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
- S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE.Frontiers in genetics · 2024Article
- DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model.Bioinformatics (Oxford, England) · 2023Article
- CombinedACS omega · 2023Article
- GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning.Nucleic acids research · 2023Article
- Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.Biomolecules · 2023Article
- BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.Protein science : a publication of the Protein Society · 2022Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
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4 authors.
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Abstract
Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.
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Registered trials
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