Biology subjects

Staller, M. V.

Publications and source records attributed to Staller, M. V..

11 recordsLinked to original sources

bioRxiv2026

Machine learning guided design of strong transcriptional activation domains

Artificial intelligence has accelerated the design of structured proteins, but it remains difficult to design functional intrinsically disordered proteins because of weak structural constraints and poorly defined sequence-to-function relationships1. Activation domains (ADs), transcription factor regions that recruit coactivators, are an excellent test case for the de novo design of intrinsically disordered sequences. ADs are one of the oldest groups of functional intrinsically disordered sequences2 and designed ADs have applications in synthetic biology, biotechnology, and agriculture. We computationally generated 500,000 candidate sequences that resembled known ADs and predicted AD function with extant machine learning models, including three neural networks. From high-scoring sequences, we selected 18,750 for experimental validation in yeast. Surprisingly, >90% of the 16,770 well-measured sequences function as ADs. Sequences predicted to be active by multiple models were stronger, motivating development of a fast and accurate consensus AD predictor, MADRIGAL. A number of our designed ADs retain functionality across fungi, plants, and human cells. This work illustrates a generalizable paradigm for incorporating sequence-to-function models into the design loop to efficiently generate functional intrinsically disordered sequences.

synthetic biology↗

bioRxiv2025

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

Deep neural networks have improved the accuracy of many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn molecular mechanisms. Here, we address the interpretability challenges associated with predicting transcriptional activation domains from protein sequence. Activation domains, regions within transcription factors that drive gene expression, were traditionally difficult to predict due to their sequence diversity and poor conservation. Multiple deep neural networks can now accurately predict activation domains, but these predictors are difficult to interpret. With the goal of interpretability, we designed simple neural networks that incorporated biophysical models of activation domains. The simplicity of these neural networks allowed us to visualize their parameters and directly interpret what the networks learned. The biophysical neural networks revealed two new ways that arrangement (i.e. the sequence grammar) of activation domain controlled function: 1) hydrophobic residues both increase activation domain strength and decrease protein abundance, and 2) acidic residues control both activation domain strength and protein abundance. Notably, the biophysical neural networks helped us to recognize the same signatures in complex interpreters of the deeper neural networks. We demonstrate how combining biophysical and deep neural networks maximizes both prediction accuracy and interpretability to yield insights into biological mechanisms.

systems biology↗

bioRxiv2025

Active learning enables discovery of transcriptional activators across fungal evolutionary space

Biological discovery and design are increasingly being guided by predictive models in place of costly experimentation. However, existing datasets are often biased by overrepresentation from model organisms, leading to failures in evolutionary studies of non-model species. We present a hybrid framework that leverages high-throughput molecular assays and active learning to quantify biological properties across evolutionary space. We focus on transcriptional activators, which contain activation domains (ADs) that promote gene expression. ADs are intrinsically disordered and poorly conserved, which limits their study using comparative genomics. Here, we developed ADhunter, a high-capacity regression model that outperforms state-of-theart algorithms in identifying and quantifying the strength of transcriptional activators. Model uncertainty was used to guide evolutionary sampling across 7.8 million proteins from 2,400 fungal genomes. We functionally characterized 9,836 ADs from 1,071 fungal genomes, providing a 15.5-fold expansion in genome representation compared to existing datasets. Comprehensive sampling from non-model genomes improved model generalizability and provides the first functional annotation for 3,416 proteins from 670 non-model fungi. Model interpretability analysis aligns with the biophysical model of AD function and reveals novel, underrepresented protein codes, highlighting the importance of sampling from non-model organisms to build evolutionarily robust models for predicting biological properties.

genomics↗

bioRxiv2024

Short activation domains control chromatin association of transcription factors

Transcription factors regulate gene expression with DNA-binding domains (DBDs) and activation domains. Despite mounting evidence to the contrary, it is frequently assumed that DBDs are solely responsible for interacting with DNA and chromatin. Here, we used single-molecule tracking of transcription factors in living cells to show that short activation domains can control the fraction of molecules bound to chromatin. Stronger activation domains have higher bound fractions and longer residence times on chromatin. Furthermore, mutations that increase activation domain strength also increase chromatin binding. This trend was consistent in four different activation domains and their mutants. This effect further held for activation domains appended to three different structural classes of DBDs. Stronger activation domains with high chromatin-bound fractions also exhibited increased binding to the p300 coactivator in proximity-assisted photoactivation experiments. Taken together, these results suggest that activation domains play a major role in tethering transcription factors to chromatin, challenging the traditional view that the DBD is the sole driver of genome binding.

biophysics↗

bioRxiv2024

Conservation of function without conservation of amino acid sequence in intrinsically disordered transcriptional activation domains

Protein function is canonically believed to be more conserved than amino acid sequence, but this idea is only well supported in folded domains, where highly diverged sequences can fold into equivalent 3D structures with identical function. Intrinsically disordered protein regions (IDRs) often experience rapid amino acid sequence divergence, but because they do not fold into stable 3D structures, it remains unknown when and how function is conserved. As a model system for studying the evolution of IDRs, we examined transcriptional activation domains, the regions of transcription factors that bind to coactivator complexes. We systematically identified activation domains on 502 homologs of the transcriptional activator Gcn4 spanning 600 MY of fungal evolution in the Ascomycota. We find that the central activation domain shows strong conservation of function without conservation of sequence. We identify the molecular mechanism for this conservation of function without conservation of sequence: evolutionary turnover (gain and loss) of acidic and aromatic residues that are important for function. We further see turnover of complete N-terminal activation domains. This turnover at two length scales confounds multiple sequence alignments, explaining why traditional comparative genomics cannot detect functional conservation of activation domains. Evolutionary turnover of key residues is likely a general mechanism for conservation of function without conservation of sequence in IDRs.

systems biology↗

bioRxiv2024

Correlating Disordered Activation Domain Ensembles with Gene Expression Levels

Transcription factor proteins bind to specific DNA promoter sequences and initiate gene transcription. In eukaryotes, most transcription factors contain intrinsically disordered activation domains (ADs) that regulate their transcriptional activity. Like other disordered protein regions, ADs do not have a fixed three-dimensional structure and instead exist in an ensemble of conformations. Disordered ensembles contain sequence-encoded structural preferences which are often linked to their function. We hypothesize this link exists between the structural preferences of disordered AD ensembles and their ability to induce gene expression. To test this, we used FRET microscopy to measure the ensemble dimensions of two activation domains, HIF-1 and CITED2, in live cells, and correlate this structural information with transcriptional activity. We find that point mutations that expanded the HIF-1 ensemble increased transcriptional activity, while those that compacted it reduced activity. Conversely, CITED2 showed no correlation between ensemble dimensions and activity. Our results reveal a sequence-dependent relationship between AD ensemble dimensions and their transcriptional activity. WHY IT MATTERSTranscription factors have activation domains (ADs) that bind to coactivator complexes to initiate gene transcription. Despite their key role, a comprehensive understanding of what drives their transcriptional activity has remained elusive. Efforts to understand AD activity have largely focused on their amino acid composition. In recent years, it is increasingly realized that the structural ensembles of disordered proteins contain biases that dictate their structural properties. For ADs, ensemble structures remain poorly explored, especially in relation to their activity. Here we report a mutational study of two ADs, HIF-1 and CITED2, that examines how ensemble dimensions correlate with activity. Our findings suggest that ensemble dimensions may drive activity in some ADs, and that AD ensemble dimensions can be modulated not only through mutations, but also through changes in the cellular environment.

biophysics↗

bioRxiv2023

Systematic identification of transcriptional activator domains from non-transcription factor proteins in plants and yeast

Transcription factors promote gene expression via trans-regulatory activation domains. Although whole genome scale screens in model organisms (e.g. human, yeast, fly) have helped identify activation domains from transcription factors, such screens have been less extensively used to explore the occurrence of activation domains in non-transcription factor proteins, such as transcriptional coactivators, chromatin regulators and some cytosolic proteins, leaving a blind spot on what role activation domains in these proteins could play in regulating transcription. We utilized the activation domain predictor PADDLE to mine the entire proteomes of two model eukaryotes, Arabidopsis thaliana and Saccharomyces cerevisiae (1). We characterized 18,000 fragments covering predicted activation domains from >800 non-transcription factor genes in both species, and experimentally validated that 89% of proteins contained fragments capable of activating transcription in yeast. Peptides with similar sequence composition show a broad range of activities, which is explained by the arrangement of key amino acids. We also annotated hundreds of nuclear proteins with activation domains as putative coactivators; many of which have never been ascribed any function in plants. Furthermore, our library contains >250 non-nuclear proteins containing peptides with activation domain function across both eukaryotic lineages, suggesting that there are unknown biological roles of these peptides beyond transcription. Finally, we identify and validate short, universal eukaryotic activation domains that activate transcription in both yeast and plants with comparable or stronger performance to state-of-the-art activation domains. Overall, our dual host screen provides a blueprint on how to systematically discover novel genetic parts for synthetic biology that function across a wide diversity of eukaryotes. Significance StatementActivation domains promote transcription and play a critical role in regulating gene expression. Although the mapping of activation domains from transcription factors has been carried out in previous genome-wide screens, their occurrence in non-transcription factors has been less explored. We utilize an activation domain predictor to mine the entire proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae for new activation domains on non-transcription factor proteins. We validate peptides derived from >750 non-transcription factor proteins capable of activating transcription, discovering many potentially new coactivators in plants. Importantly, we identify novel genetic parts that can function across both species, representing unique synthetic biology tools.

genetics↗

bioRxiv2023

The balance of acidic and hydrophobic residues predicts acidic transcriptional activation domains from protein sequence

Transcription factors activate gene expression in development, homeostasis, and stress with DNA binding domains and activation domains. Although there exist excellent computational models for predicting DNA binding domains from protein sequence (Stormo, 2013), models for predicting activation domains from protein sequence have lagged behind (Erijman et al., 2020; Ravarani et al., 2018; Sanborn et al., 2021), particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors (Staller et al., 2022). Here, we show how the accuracy of this human predictor arises from the balance between hydrophobic and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of neural network models trained in yeast, the intersection is more predictive than individual models, emphasizing that each approach carries orthogonal information. We synthesize these findings into a new set of activation domain predictions on human transcription factors.

systems biology↗

bioRxiv2020

Human transcriptional activation domains require hydrophobic and acidic residues

Acidic activation domains are intrinsically disordered regions of transcription factors that bind coactivators. The intrinsic disorder and low conservation of activation domains have made it difficult to identify general rules governing their function. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a new high-throughput assay in human cell culture. From these data we identify three design principles of acidic activation domains: hydrophobic motifs must be balanced by acidic residues; acidic residues make large contributions to activity when they are adjacent to motifs; and motif composition reflects the structural constraints of coactivator interactions. These design principles motivated a simple predictor of activation domains in the human proteome: scanning for clusters of aromatic and leucine residues embedded in regions of high acidity identifies known activation domains and accurately predicts new ones. Our results support a flexible model in which acidic residues solubilize hydrophobic motifs so that they can interact with coactivators.

systems biology↗

bioRxiv2017

A deep mutational scan of an acidic activation domain

Transcriptional activation domains are intrinsically disordered peptides with little primary sequence conservation. These properties have made it difficult to identify the sequence features that define activation domains. For example, although acidic activation domains were discovered 30 years ago, we still do not know what role, if any, acidic residues play in these peptides. To address this question we designed a rational mutagenesis scheme to independently test four sequence features theorized to control the strength of activation domains: acidity (negative charge), hydrophobicity, intrinsic disorder, and short linear motifs. To test enough mutants to deconvolve these four features we developed a method to quantify the activities of thousands of activation domain variants in parallel. Our results with Gcn4, a classic acidic activation domain, suggest that acidic residues in particular regions keep two hydrophobic motifs exposed to solvent. We also found that the specific activity of the Gcn4 activation domain increases during amino acid starvation. Our results suggest that Gcn4 may have evolved to have low activity but high inducibility. Our results also demonstrate that high-throughput rational mutation scans will be powerful tools for unraveling the properties that control how intrinsically disordered proteins function.

systems biology↗

bioRxiv2017

Caudal counter-represses Hunchback to regulate even-skipped stripe 2 expression in Drosophila embryos

Hunchback is a bifunctional transcription factor that can activate and repress gene expression in Drosophila development. We investigated the regulatory DNA sequence features that control Hunchback function by perturbing enhancers for one of its target genes, even-skipped. While Hunchback directly represses the eve stripe 3+7 enhancer, we found that in the eve stripe 2+7 enhancer, Hunchback repression is prevented by Caudal binding--this relationship is called counter-repression. We found evidence that this relationship is conserved by comparing predicted binding sites for Hunchback and Caudal across orthologous eve stripe 2 enhancers. These results alter the textbook view of eve stripe 2 regulation wherein Hb is depicted as a direct activator. Instead, to generate stripe 2, Hunchback repression must be counteracted by Caudal binding. We discuss the implications of this interaction for eve stripe 2 regulation and evolution.

genetics↗