DNA–protein interaction: identification, prediction and data analysis

• Published in: Molecular biology reports 2019-06, Vol.46 (3), p.3571-3596
• Main Authors: Emamjomeh, Abbasali, Choobineh, Darush, Hajieghrari, Behzad, MahdiNezhad, Nafiseh, Khodavirdipour, Amir
• Format: Article
• Language: English
• Subjects: Animal Anatomy; Animal Biochemistry; Animals; Binding Sites - genetics; Binding Sites - physiology; Bioinformatics; Biomedical and Life Sciences; Computational Biology - methods; Data Analysis; Data processing; Deoxyribonucleic acid; DNA; DNA - genetics; DNA - metabolism; DNA-Binding Proteins - genetics; DNA-Binding Proteins - metabolism; Forecasting - methods; Genomes; Histology; Humans; Life Sciences; Models, Molecular; Morphology; Protein interaction; Proteins; Review; Sequence Analysis, DNA - methods; Sequence Analysis, Protein - methods; Statistical analysis
• ISSN: 0301-4851; 1573-4978
• DOI: 10.1007/s11033-019-04763-1

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA–protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA–protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.