ENSEMBLE-CNN: Predicting DNA Binding Sites in Protein Sequences by an Ensemble Deep Learning Method

2018 ◽  
pp. 301-306 ◽  
Author(s):  
Yongqing Zhang ◽  
Shaojie Qiao ◽  
Shengjie Ji ◽  
Jiliu Zhou
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Sites ◽  
Protein Sequences ◽  
Learning Method ◽  
Dna Binding Sites

scholarly journals Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs

RNA Biology ◽  
2018 ◽  
Vol 15 (12) ◽  
pp. 1468-1476 ◽  
Author(s):  
Fan Wang ◽  
Pranik Chainani ◽  
Tommy White ◽  
Jin Yang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Sites ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Dna Binding Sites ◽  
Genome Wide

scholarly journals A novel ensemble learning method for de novo computational identification of DNA binding sites

2007 ◽  
Vol 8 (1) ◽  
pp. 249 ◽  
Author(s):  
Arijit Chakravarty ◽  
Jonathan M Carlson ◽  
Radhika S Khetani ◽  
Robert H Gross
Keyword(s):  
Dna Binding ◽  
Ensemble Learning ◽  
Binding Sites ◽  
De Novo ◽  
Learning Method ◽  
Dna Binding Sites ◽  
Computational Identification

scholarly journals DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method

2021 ◽  
Vol 22 (11) ◽  
pp. 5510
Author(s):  
Samuel Godfrey Hendrix ◽  
Kuan Y. Chang ◽  
Zeezoo Ryu ◽  
Zhong-Ru Xie
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Structures ◽  
Learning Model ◽  
Atom Type ◽  
Dna Binding Sites ◽  
Dna Binding Site ◽  
Deep Learning Model

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

scholarly journals Analysis of two distinct single-stranded DNA binding sites on the recA nucleoprotein filament.

1993 ◽  
Vol 268 (30) ◽  
pp. 22525-22530
Author(s):  
A Zlotnick ◽  
R.S. Mitchell ◽  
R.K. Steed ◽  
S.L. Brenner
Keyword(s):  
Dna Binding ◽  
Binding Sites ◽  
Single Stranded Dna ◽  
Dna Binding Sites

scholarly journals Immobilized oligodeoxynucleotides as probes of the DNA-binding sites of mouse steroid holoreceptors.

1982 ◽  
Vol 257 (9) ◽  
pp. 4738-4745
Author(s):  
S C Gross ◽  
S A Kumar ◽  
H W Dickerman
Keyword(s):  
Dna Binding ◽  
Binding Sites ◽  
Dna Binding Sites
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