Unraveling the mysteries of gene transcription activation with bioinformatics
Year: 2023
Type: other
Abstract: The mechanism of gene expression initiation by transcription activation domains (TADs) remains a mystery: TADs are short and intrinsically disordered, lacking a specific sequence or structure and exhibiting “fuzzy” interactions with a variety of targets. We have identified features corresponding to TADs using bioinformatics and machine learning methods. First, two published data sets containing thousands of TADs selected from random sequences were re-analyzed and compared. Greater than 1% of random sequences proved to be functional as TADs, and regression analysis demonstrated that greater abundance of acidic and aromatic amino acids conferred functionality, while basic residues were detrimental. The long short-term memory (LSTM) neural network was used to understand functional sequences in greater detail, improving prediction accuracy (AUC 0.98) and identifying rules for functionality, such as the need for both aromatic and acidic residues in a functional TAD, as well as location preferences for certain types of residues. We conducted additional experiments to confirm and expand on these rules: surprisingly, TADs with only one aromatic and one acidic residue proved to be functional. Additionally, these experiments demonstrated that breaking an amphipathic alpha-helix with a proline increases the likelihood of TAD functionality. These findings strongly contradict the traditional recruitment model, emphasizing the need for additional work, thought, and open-mindedness in the area of gene transcription.
Institution: Butler University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4416506484
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