Back to Search
Start Over
PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity
- Source :
- Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020), Scientific Reports
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group, 2020.
-
Abstract
- The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.
- Subjects :
- 0301 basic medicine
Sequence analysis
lcsh:Medicine
Computational biology
Article
03 medical and health sciences
symbols.namesake
chemistry.chemical_compound
Sequence Analysis, Protein
Transcription (biology)
Machine learning
Computational models
lcsh:Science
Computational model
Multidisciplinary
030102 biochemistry & molecular biology
Ensemble forecasting
lcsh:R
DNA replication
DNA
Pearson product-moment correlation coefficient
DNA-Binding Proteins
030104 developmental biology
Models, Chemical
chemistry
RNA splicing
symbols
lcsh:Q
Software
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 10
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....48c348147df40c7c01dd89f7a8184005