Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

ABSTRACT: Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial TFs-DNA interactions remains an intricate challenge. A deeper understanding of DNA secondary structures could greatly enhance our knowledge of how TFs recognize and interact with DNA, thereby elucidating their biological function. In this study, we employed machine learning algorithms to predict transcription factor binding sites (TFBS) and classify them as directed-repeat (DR) or inverted-repeat (IR). To accomplish this, we divided the set of TFBS nucleotide sequences by size, ranging from 8 to 20 base pairs, and converted them into thermodyna

SUBMITTER: Borges Farias A 

PROVIDER: S-EPMC11562833 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature