Biology subjects

Erkine, A. M.

Publications and source records attributed to Erkine, A. M..

3 recordsLinked to original sources

bioRxiv2024

Predicting transcriptional activation domain function using Graph Neural Networks

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.

bioinformatics↗

bioRxiv2023

Sequence features of transcriptional activation domains are consistent with the surfactant mechanism of gene activation

Transcriptional activation domains (ADs) of gene activators remain enigmatic for decades as they are short, extremely variable in sequence, structurally disordered, and interact fuzzily to a spectrum of targets. We showed that the single required characteristic of the most common acidic ADs is an amphiphilic aromatic-acidic surfactant-like property which is the key for the local gene-promoter chromatin phase transition and the formation of "transcription factory" condensates. We demonstrate that the presence of tryptophan and aspartic acid residues in the AD sequence is sufficient for in vivo functionality, even when present only as a single pair of residues within a 20-amino-acid sequence containing only 18 additional glycine residues. We demonstrate that breaking the amphipathic -helix in AD by prolines increases AD functionality. The proposed mechanism is paradigm-shifting for gene activation area and generally for biochemistry as it relies on near-stochastic allosteric interactions critical for the key biological function.

molecular biology↗

bioRxiv2018

Genetic and Epigenetic Determinants Establish a Continuum of Hsf1 Occupancy and Activity Across the Yeast Genome

Heat Shock Factor 1 (Hsf1) is the master transcriptional regulator of molecular chaperones and binds to the same cis-acting element - Heat Shock Element (HSE) - across the eukaryotic lineage. In budding yeast, Hsf1 drives transcription of ~20 genes essential to maintain proteostasis under basal conditions, yet its specific targets and extent of inducible binding during heat shock remain unclear. Here we combine Hsf1 ChIP-seq, nascent RNA-seq and Hsf1 nuclear depletion to quantify Hsf1 binding and transcription across the yeast genome. Hsf1 binds 74 loci during acute heat shock, 46 of which are linked to genes with strong Hsf1-dependent transcription. Most of these targets show detectable Hsf1 binding under basal conditions, but basal occupancy and heat shock-inducible binding both vary over two orders of magnitude. Notably, Hsf1s induced DNA binding leads to a disproportionate (up to 50-fold) increase in nascent transcription. While variation in basal Hsf1 occupancy poorly correlates with the strength of the HSE, promoters with high basal Hsf1 occupancy have nucleosome-depleted regions due to the presence of pioneer factors. Such accessible chromatin may be critical for Hsf1 occupancy of its genomic sites as the activator is incapable of binding HSEs embedded within a stable nucleosome in vitro. In response to heat shock, however, Hsf1 is able to gain access to nucleosomal sites and promotes chromatin remodeling with the RSC complex playing a key role. We propose that the interplay between nucleosome occupancy, HSE strength and active Hsf1 levels allows cells to precisely tune expression of the proteostasis network.

genomics↗