Predicting transcriptional activation domain function using Graph Neural Networks
Year: 2024
Type: preprint
Abstract: Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.
Institutions: Purdue University West Lafayette, Butler University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Collaborative Research: RoL: Revealing a new mechanism of action for eukaryotic transcriptional activation domains, G1885015333, Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images, IIBR Informatics: Development of Multimodal approaches for protein function prediction, G5601722728, Collaborative Research: Integrated Moment-Based Descriptors and Deep Neural Network for Screening Three-Dimensional Biological Data, G7000278813, Building protein structure models for intermediate resolution cryo-electron microscopy maps, Collaborative Research: Identification and Structural Modeling of Intrinsically Disordered Protein-Protein and Protein-Nucleic Acids Interactions
OpenAlex ID: W4396869815