ArticlePloS one2014
Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 43 citations in OpenAlex.
- A novel prediction method for protein-DNA binding sites based on protein language model fusion features with SE-connection pyramidal network and ensemble learning.BMC genomics · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform.Computational intelligence and neuroscience · 2022Article
- DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.Journal of computer-aided molecular design · 2019Article
- Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.Nucleic acids research · 2018Article
- HOMCOS: an updated server to search and model complex 3D structures.Journal of structural and functional genomics · 2016Article
- Template-based prediction of protein function.Current opinion in structural biology · 2015Review
- SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.PloS one · 2015Article
- Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.Avicenna journal of medical biotechnologyArticle
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.
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Registered trials
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