Transcription factor prediction using protein 3D secondary structures.

ABSTRACT:

Motivation

Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs.

Results

We propose a deep learning-based TF prediction method (StrucTFactor),

SUBMITTER: Liebold J 

PROVIDER: S-EPMC11769678 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature