Protein-induced DNA linking number change by sequence-specific DNA binding proteins and its biological effects

In silico simulations of occurrence of transcription factor binding sites in bacterial genomes

Background

Interactions between transcription factors and their specific binding sites are a key component of regulation of gene expression. Until recently, it was generally assumed that most bacterial transcription factor binding sites are located at or near promoters. However, several recent works utilizing high-throughput technology to detect transcription factor binding sites in bacterial genomes found a large number of binding sites in unexpected locations, particularly inside genes, as opposed to known or expected promoter regions. While some of these intragenic binding sites likely have regulatory functions, an alternative scenario is that many of these binding sites arise by chance in the absence of selective constraints. The latter possibility was supported by in silico simulations for σ⁵⁴ binding sites in Salmonella.

Results

In this work, we extend these simulations to more than forty transcription factors from E. coli and other bacteria. The results suggest that binding sites for all analyzed transcription factors are likely to arise throughout the genome by random genetic drift and many transcription factor binding sites found in genomes may not have specific regulatory functions. In addition, when comparing observed and expected patterns of occurrence of binding sites in genomes, we observed distinct differences among different transcription factors.

Conclusions

We speculate that transcription factor binding sites randomly occurring throughout the genome could be beneficial in promoting emergence of new regulatory interactions and thus facilitating evolution of gene regulatory networks.

Electronic supplementary material

The online version of this article (10.1186/s12862-019-1381-8) contains supplementary material, which is available to authorized users.

Chromosomal organization of transcription: in a nutshell

Early studies of transcriptional regulation focused on individual gene promoters defined specific transcription factors as central agents of genetic control. However, recent genome-wide data propelled a different view by linking spatially organized gene expression patterns to chromosomal dynamics. Therefore, the major problem in contemporary molecular genetics concerned with transcriptional gene regulation is to establish a unifying model that reconciles these two views. This problem, situated at the interface of polymer physics and network theory, requires development of an integrative methodology. In this review, we discuss recent achievements in classical model organism E. coli and provide some novel insights gained from studies of a bacterial plant pathogen, D. dadantii. We consider DNA topology and the basal transcription machinery as key actors of regulation, in which activation of functionally relevant genes is coupled to and coordinated with the establishment of extended chromosomal domains of coherent transcription. We argue that the spatial organization of genome plays a fundamental role in its own regulation.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.