A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning

Hierarchy in regulator interactions with distant transcriptional activation domains empowers rheostatic regulation

Transcription factors carry long intrinsically disordered regions often containing multiple activation domains. Despite numerous recent high-throughput identifications and characterizations of activation domains, the interplay between sequence motifs, activation domains, and regulator binding in intrinsically disordered transcription factor regions remains unresolved. Here, we map sequence motifs and activation domains in an Arabidopsis thaliana NAC transcription factor clade, revealing that although sequence motifs and activation domains often coincide, no systematic overlap exists. Biophysical analyses using NMR spectroscopy show that the long intrinsically disordered region of senescence-associated transcription factor ANAC046 is devoid of residual structure. We identify two activation domain/sequence motif regions, one at each end that both bind a panel of six positive and negative regulator domains from biologically relevant regulators promiscuously. Binding affinities measured using isothermal titration calorimetry reveal a hierarchy for regulator binding of the two ANAC046 activation domain/sequence motif regions defining these as regulatory hotspots. Despite extensive dynamic intramolecular contacts along the disordered chain revealed using paramagnetic relaxation enhancement experiments and simulations, the regions remain uncoupled in binding. Together, the results imply rheostatic regulation by ANAC046 through concentration-dependent regulator competition, a mechanism likely mirrored in other transcription factors with distantly located activation domains.

Exploring the central dogma through the lens of gene expression noise

Over the past two decades, cell-to-cell heterogeneity has garnered increasing attention due to its critical role in both developmental and pathological processes. This growing interest has been driven, in part, by the advancements in live-cell and single-molecule imaging techniques. These techniques have provided mechanistic insights into how processes, transcription in particular, contribute to gene expression noise and, ultimately, cell-to-cell heterogeneity. More recently, however, research has expanded to explore how downstream steps in the central dogma influence gene expression noise. In this review, we mostly examine the impact of transcriptional processes on the generation of gene expression noise but also discuss how post-transcriptional mechanisms modulate noise and its propagation to the protein level. This evaluation emphasizes the need for further investigation into how processes beyond transcription shape gene expression noise, highlighting unanswered questions that remain in the field.

Identification of microproteins with transactivation activity by polyalanine motif selection

Microproteins are an emerging class of proteins that are encoded by small open reading frames (smORFs) less than or equal to 100 amino acids. The functions of several microproteins have been illuminated through phenotypic screening or protein-protein interaction studies, but thousands of microproteins remain uncharacterized. The functional characterization of microproteins is challenging due to a lack of sequence homology. Here, we demonstrate a strategy to enrich microproteins that contain specific motifs as a means to more rapidly characterize microproteins. Specifically, we used the fact that polyalanine motifs are associated with nuclear proteins to select 58 candidate microproteins to screen for transactivation function. We identified three microproteins with transactivation activity when tested as GAL4-fusions in a cell-based luciferase assay. The results support the continued use of the motif selection strategy for the discovery of microprotein function.

The mechanism of YAP/TAZ transactivation and dual targeting for cancer therapy

Transcriptional coactivators Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) play key roles in cancers through transcriptional outputs. However, their transactivation mechanisms remain unclear, and effective targeting strategies are lacking. Here, we show that YAP/TAZ possess a hydrophobic transactivation domain (TAD). TAD knockout prevents tumor establishment due to growth defects and enhances immune attack. Mechanistically, TADs facilitate preinitiation complex (PIC) assembly by recruiting the TATA-binding protein-associated factor 4 (TAF4)-dependent TFIID complex and enhance RNA polymerase II (Pol II) elongation through mediator complex subunit 15 (MED15)-dependent mediator recruitment for the expressions of oncogenic/immune-suppressive programs. The synthesized peptide TJ-M11 selectively disrupts TAD interactions with MED15 and TAF4, suppressing tumor growth and sensitizing tumors to immunotherapy. Our findings demonstrate that YAP/TAZ TADs exhibit dual functions in PIC assembly and Pol II elongation via hydrophobic interactions, which represent actionable targets for cancer therapy and combination immunotherapy.

Deciphering disordered regions controlling mRNA decay in high-throughput

Intrinsically disordered regions within proteins drive specific molecular functions despite lacking a defined structure1,2. Although disordered regions are integral to controlling mRNA stability and translation, the mechanisms underlying these regulatory effects remain unclear3. Here we reveal the molecular determinants of this activity using high-throughput functional profiling. Systematic mutagenesis across hundreds of regulatory disordered elements, combined with machine learning, reveals a complex pattern of molecular features important for their activity. The presence and arrangement of aromatic residues strongly predicts the ability of seemingly diverse protein sequences to influence mRNA stability and translation. We further show how many of these regulatory elements exert their effects by engaging core mRNA decay machinery. Our results define molecular features and biochemical pathways that explain how disordered regions control mRNA expression and shed light on broader principles within functional, unstructured proteins.

Low overlap of transcription factor DNA binding and regulatory targets

DNA sequence-specific transcription factors (TFs) modulate transcription and chromatin architecture, acting from regulatory sites in enhancers and promoters of eukaryotic genes1,2. How multiple TFs cooperate to regulate individual genes is still unclear. In yeast, most TFs are thought to regulate transcription via binding to upstream activating sequences, which are situated within a few hundred base pairs upstream of the regulated gene3. Although this model has been validated for individual TFs and specific genes, it has not been tested in a systematic way. Here we integrated information on the binding and expression targets for the near-complete set of yeast TFs and show that, contrary to expectations, there are few TFs with dedicated activator or repressor roles, and that most TFs have a dual function. Although nearly all protein-coding genes are regulated by one or more TFs, our analysis revealed limited overlap between TF binding and gene regulation. Rapid depletion of many TFs also revealed many regulatory targets that were distant from detectable TF binding sites, suggesting unexpected regulatory mechanisms. Our study provides a comprehensive survey of TF functions and offers insights into interactions between the set of TFs expressed in a single cell type and how they contribute to the complex programme of gene regulation.

Q-rich activation domains: flexible 'rulers' for transcription start site selection?

Recent findings broadened the function of RNA polymerase II (Pol II) proximal promoter motifs from quantitative regulators of transcription to important determinants of transcription start site (TSS) position. These motifs are recognized by transcription factors (TFs) that we propose to term 'ruler' TFs (rTFs), such as NRF1, NF-Y, YY1, ZNF143, BANP, and members of the SP, ETS, and CRE families, sharing as a common feature a glutamine-rich (Q-rich) effector domain also enriched in valine, isoleucine, and threonine (QVIT-rich). We propose that rTFs guide TSS location by constraining the position of the pre-initiation complex (PIC) during its promoter recognition phase through a specialized, and still enigmatic, class of activation domains.

A quantitative intracellular peptide-binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as short linear motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called systematic intracellular motif-binding analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at noncore positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

Intrinsically disordered regions as facilitators of the transcription factor target search

Transcription factors (TFs) contribute to organismal development and function by regulating gene expression. Despite decades of research, the factors determining the specificity and speed at which eukaryotic TFs detect their target binding sites remain poorly understood. Recent studies have pointed to intrinsically disordered regions (IDRs) within TFs as key regulators of the process by which TFs find their target sites on DNA (the TF target search). However, IDRs are challenging to study because they can confer specificity despite low sequence complexity and can be functionally conserved despite rapid sequence divergence. Nevertheless, emerging computational and experimental approaches are beginning to elucidate the sequence-function relationship within the IDRs of TFs. Additional insights are informing potential mechanisms underlying the IDR-directed search for the DNA targets of TFs, including incorporation into biomolecular condensates, facilitating TF co-localization, and the hypothesis that IDRs recognize and directly interact with specific genomic regions.

Boosting transcriptional activities by employing repeated activation domains in transcription factors

Enhancing the transcriptional activation activity of transcription factors (TFs) has multiple applications in organism improvement, metabolic engineering, and other aspects of plant science, but the approaches remain unclear. Here, we used gene activation assays and genetic transformation to investigate the transcriptional activities of two MYB TFs, PRODUCTION OF ANTHOCYANIN PIGMENT 1 (AtPAP1) from Arabidopsis (Arabidopsis thaliana) and EsMYBA1 from Epimedium (Epimedium sagittatum), and their synthetic variants in a range of plant species from several families. Using anthocyanin biosynthesis as a convenient readout, we discovered that homologous naturally occurring TFs showed differences in the transcriptional activation ability and that similar TFs induced large changes in the genetic program when heterologously expressed in different species. In some cases, shuffling the DNA-binding domains and transcriptional activation domains (ADs) between homologous TFs led to synthetic TFs that had stronger activation potency than the original TFs. More importantly, synthetic TFs derived from MYB, NAC, bHLH, and ethylene-insensitive3-like (EIL) family members containing tandemly repeated ADs had greatly enhanced activity compared to their natural counterparts. These findings enhance our understanding of TF activity and demonstrate that employing tandemly repeated ADs from natural TFs is a simple and widely applicable strategy to enhance the activation potency of synthetic TFs.

Allovalent scavenging of activation domains in the transcription factor ANAC013 gears transcriptional regulation

Transcriptional regulation involves interactions between transcription factors, coregulators, and DNA. Intrinsic disorder is a major player in this regulation, but mechanisms driven by disorder remain elusive. Here, we address molecular communication within the stress-regulating Arabidopsis thaliana transcription factor ANAC013. Through high-throughput screening of ANAC013 for transcriptional activation activity, we identify three activation domains within its C-terminal intrinsically disordered region. Two of these overlap with acidic islands and form dynamic interactions with the DNA-binding domain and are released, not only upon binding of target promoter DNA, but also by nonspecific DNA. We show that independently of DNA binding, the RST (RCD--SRO--TAF4) domain of the negative regulator RCD1 (Radical-induced Cell Death1) scavenges the two acidic activation domains positioned vis-à-vis through allovalent binding, leading to dynamic occupation at enhanced affinity. We propose an allovalency model for transcriptional regulation, where sequentially close activation domains in both DNA-bound and DNA-free states allow for efficient regulation. The model is likely relevant for many transcription factor systems, explaining the functional advantage of carrying sequentially close activation domains.

Accurate phenotype-to-genotype mapping of high-diversity yeast libraries by heat-shock-electroporation (HEEL)

High-throughput DNA transformation techniques are invaluable when generating high-diversity mutant libraries, a cornerstone of successful protein engineering. However, transformation efficiencies have a direct correlation with the probability of introducing multiple DNA molecules into each cell, although reliable library screenings require cells that contain a single unique genotype. Thus, transformation methods that yield a high multiplicity of transformations are unsuitable for high-diversity library screenings. Here, we describe an innovative yeast library transformation method that is both simple and highly efficient. Our dual heat-shock and electroporation approach (HEEL) creates high-quality DNA libraries by increasing the fraction of mono-transformed yeast cells from 20% to over 70% of all transformed cells, thus allowing for near-perfect phenotype-to-genotype associations. HEEL also allows more than 107 yeast cells per reaction to be transformed with a circular plasmid molecule, which corresponds to an almost 100-fold improvement compared with current yeast transformation methods. To further refine our library screening approach, we integrated an automated yeast genotyping workflow with a dual-barcode design that employs both a single nucleotide polymorphism and a high-diversity region. This design allows for robust identification and quantification of unique genotypes within a heterogeneous population using standard Sanger sequencing. Our findings demonstrate that the longstanding trade-off between the size and quality of transformed yeast libraries can be overcome. By employing the HEEL method, large DNA libraries can be transformed into yeast with high-efficiency, while maintaining high library quality, essential for successful mutant screenings. This advancement holds significant promise for the fields of molecular biology and protein engineering.IMPORTANCEWith the recent expansion of artificial intelligence in the field of synthetic biology, there has never been a greater need for high-quality data and reliable measurements of phenotype-to-genotype relationships. However, one major obstacle to creating accurate computer-based models is the current abundance of low-quality phenotypic measurements originating from numerous high-throughput but low-resolution assays. Rather than increasing the quantity of measurements, new studies should aim to generate as accurate measurements as possible. The HEEL methodology presented here aims to address this issue by minimizing the problem of multi-plasmid uptake during high-throughput yeast DNA transformations, which leads to the creation of heterogeneous cellular genotypes. HEEL should enable highly accurate phenotype-to-genotype measurements going forward, which could be used to construct better computer-based models.

Versatile roles of disordered transcription factor effector domains in transcriptional regulation

Transcription, a crucial step in the regulation of gene expression, is tightly controlled and involves several essential processes, such as chromatin organization, recognition of the specific genomic sequences, DNA binding, and ultimately recruiting the transcriptional machinery to facilitate transcript synthesis. At the center of this regulation are transcription factors (TFs), which comprise at least one DNA-binding domain (DBD) and an effector domain (ED). Although the structure and function of DBDs have been well studied, our knowledge of the structure and function of effector domains is limited. EDs are of particular importance in generating distinct transcriptional responses between protein members of the same TF family that have similar DBDs and specificities. The study of transcriptional activity conferred by effector domains has traditionally been conducted through examining protein-protein interactions. However, recent research has uncovered alternative mechanisms by which EDs regulate gene expression, such as the formation of condensates that increase the local concentration of transcription factors, cofactors, and coregulated genes, as well as DNA binding. Here, we provide a comprehensive overview of the known roles of transcription factor EDs, with a specific focus on disordered regions. Additionally, we emphasize the significance of intrinsically disordered regions (IDRs) during transcriptional regulation. We examine the mechanisms underlying the establishment and maintenance of transcriptional specificity through the structural properties of predominantly disordered EDs. We then provide a comprehensive overview of the current understanding of these domains, including their physical and chemical characteristics, as well as their functional roles.

Transcriptional activation and coactivator binding by yeast Ino2 and human proto-oncoprotein c-Myc

Basic helix-loop-helix domains in yeast regulatory proteins Ino2 and Ino4 mediate formation of a heterodimer which binds to and activates expression of phospholipid biosynthetic genes. The human proto-oncoprotein c-Myc (Myc) and its binding partner Max activate genes important for cellular proliferation and contain functional domains structure and position of which strongly resembles Ino2 and Ino4. Since Ino2-Myc and Ino4-Max may be considered as orthologs we performed functional comparisons in yeast. We demonstrate that Myc and Max could be stably synthesized in S. cerevisiae and together significantly activated a target gene of Ino2/Ino4 but nevertheless were unable to functionally complement an ino2 ino4 double mutant. We also map two efficient transcriptional activation domains in the N-terminus of Myc (TAD1: aa 1-41 and TAD2: aa 91-140), corresponding to TAD positions in Ino2. We finally show that coactivators such as TFIID subunits Taf1, Taf4, Taf6, Taf10 and Taf12 as well as ATPase subunits of chromatin remodelling complexes Swi2, Sth1 and Ino80 previously shown to interact with TADs of Ino2 were also able to bind TADs of Myc, supporting the view that heterodimers Ino2/Ino4 and Myc/Max are evolutionary related but have undergone transcriptional rewiring of target genes.

The transcription factor FOXQ1 in cancer

FOXQ1 is a member of the large forkhead box (FOX) family of transcription factors that is involved in all aspects of mammalian development, physiology, and pathobiology. FOXQ1 has emerged as a major regulator of epithelial-to-mesenchymal transition and tumour metastasis in cancers, especially carcinomas of the digestive tract. Accordingly, FOXQ1 induction is recognised as an independent prognostic factor for worse overall survival in several types of cancer, including gastric and colorectal cancer. In this review article, I summarise new evidence on the role of FOXQ1 in cancer, with a focus on molecular mechanisms that control FOXQ1 levels and the regulation of FOXQ1 target genes. Unravelling the functions of FOXQ1 has the potential to facilitate the development of targeted treatments for metastatic cancers.

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. In contrast, intrinsically disordered protein regions (IDRs) do not fold into a stable 3D structure, thus it remains unknown when and how function is conserved for IDRs that experience rapid amino acid sequence divergence. 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 orthologs of the transcriptional activator Gcn4 spanning 600 MY of fungal evolution. We find that the central activation domain shows strong conservation of function without conservation of sequence. This conservation of function without conservation of sequence is facilitated by evolutionary turnover (gain and loss) of key acidic and aromatic residues, the positions most important for function. This high sequence flexibility of functional orthologs mirrors the physical flexibility of the activation domain coactivator interaction interface, suggesting that physical flexibility enables evolutionary plasticity. We propose that turnover of short functional elements, sometimes individual amino acids, is a general mechanism for conservation of function without conservation of sequence during IDR evolution.

Grammar rules and exceptions for the language of transcriptional activation domains

Transcriptional activation domains (ADs) of gene activators have remained enigmatic for decades as short, extremely variable, and structurally disordered sequences. Using a rational design and high throughput in vivo experimentation, we determine the grammar rules and exceptions for the language of ADs. According to identified rules, billions of highly active ADs can be composed of balanced amounts of acidic/aromatic amino acids, with either mixed composition of aromatic residues, or using only one aromatic residue mixed with acidic residues. However, equally active sequences can be composed of only aliphatic leucine and aspartic acid residues. The much rarer LD exceptions have a higher ratio of hydrophobic/acidic balance and display a specific LDL(L/D)DLL motif. For aromatic/acidic Ads, the intermixing of proline residues in context of amphipathic α-helix structures significantly increases the AD activity. The identified grammar rules and exceptions are interpreted in application to the biochemistry of AD function and eukaryotic gene expression.

The Enigma of Transcriptional Activation Domains

Activation domains (ADs) of eukaryotic gene activators remain enigmatic for decades as short, extremely variable sequences which often are intrinsically disordered in structure and interact with an uncertain number of targets. The general absence of specificity increasingly complicates the utilization of the widely accepted mechanism of AD function by recruitment of coactivators. The long-standing enigma at the heart of molecular biology demands a fundamental rethinking of established concepts. Here, we review the experimental evidence supporting a novel mechanistic model of gene activation, based on ADs functioning via surfactant-like near-stochastic interactions with gene promoter nucleosomes. This new model is consistent with recent information-rich experimental data obtained using high-throughput synthetic biology and bioinformatics analysis methods, including machine learning. We clarify why the conventional biochemical principle of specificity for sequence, structures, and interactions fails to explain activation domain function. This perspective provides connections to the liquid-liquid phase separation model, signifies near-stochastic interactions as fundamental for the biochemical function, and can be generalized to other cellular functions.

Revisiting the model for coactivator recruitment: Med15 can select its target sites independent of promoter-bound transcription factors

Activation domains (ADs) within transcription factors (TFs) induce gene expression by recruiting coactivators such as the Mediator complex. Coactivators lack DNA binding domains (DBDs) and are assumed to passively follow their recruiting TFs. This is supported by direct AD-coactivator interactions seen in vitro but has not yet been tested in living cells. To examine that, we targeted two Med15-recruiting ADs to a range of budding yeast promoters through fusion with different DBDs. The DBD-AD fusions localized to hundreds of genomic sites but recruited Med15 and induced transcription in only a subset of bound promoters, characterized by a fuzzy-nucleosome architecture. Direct DBD-Med15 fusions shifted DBD localization towards fuzzy-nucleosome promoters, including promoters devoid of the endogenous Mediator. We propose that Med15, and perhaps other coactivators, possess inherent promoter preference and thus actively contribute to the selection of TF-induced genes.

A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as Short Linear Motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called Systematic Intracellular Motif Binding Analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants, and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method, and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at non-core positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

The C. elegans Myc-family of transcription factors coordinate a dynamic adaptive response to dietary restriction

Dietary restriction (DR), the process of decreasing overall food consumption over an extended period of time, has been shown to increase longevity across evolutionarily diverse species and delay the onset of age-associated diseases in humans. In Caenorhabditis elegans, the Myc-family transcription factors (TFs) MXL-2 (Mlx) and MML-1 (MondoA/ChREBP), which function as obligate heterodimers, and PHA-4 (orthologous to FOXA) are both necessary for the full physiological benefits of DR. However, the adaptive transcriptional response to DR and the role of MML-1::MXL-2 and PHA-4 remains elusive. We identified the transcriptional signature of C. elegans DR, using the eat-2 genetic model, and demonstrate broad changes in metabolic gene expression in eat-2 DR animals, which requires both mxl-2 and pha-4. While the requirement for these factors in DR gene expression overlaps, we found many of the DR genes exhibit an opposing change in relative gene expression in eat-2;mxl-2 animals compared to wild-type, which was not observed in eat-2 animals with pha-4 loss. Surprisingly, we discovered more than 2000 genes synthetically dysregulated in eat-2;mxl-2, out of which the promoters of down-regulated genes were substantially enriched for PQM-1 and ELT-1/3 GATA TF binding motifs. We further show functional deficiencies of the mxl-2 loss in DR outside of lifespan, as eat-2;mxl-2 animals exhibit substantially smaller brood sizes and lay a proportion of dead eggs, indicating that MML-1::MXL-2 has a role in maintaining the balance between resource allocation to the soma and to reproduction under conditions of chronic food scarcity. While eat-2 animals do not show a significantly different metabolic rate compared to wild-type, we also find that loss of mxl-2 in DR does not affect the rate of oxygen consumption in young animals. The gene expression signature of eat-2 mutant animals is consistent with optimization of energy utilization and resource allocation, rather than induction of canonical gene expression changes associated with acute metabolic stress, such as induction of autophagy after TORC1 inhibition. Consistently, eat-2 animals are not substantially resistant to stress, providing further support to the idea that chronic DR may benefit healthspan and lifespan through efficient use of limited resources rather than broad upregulation of stress responses, and also indicates that MML-1::MXL-2 and PHA-4 may have distinct roles in promotion of benefits in response to different pro-longevity stimuli.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Systematic discovery of DNA-binding tandem repeat proteins

Tandem repeat proteins (TRPs) are widely distributed and bind to a wide variety of ligands. DNA-binding TRPs such as zinc finger (ZNF) and transcription activator-like effector (TALE) play important roles in biology and biotechnology. In this study, we first conducted an extensive analysis of TRPs in public databases, and found that the enormous diversity of TRPs is largely unexplored. We then focused our efforts on identifying novel TRPs possessing DNA-binding capabilities. We established a protein language model for DNA-binding protein prediction (PLM-DBPPred), and predicted a large number of DNA-binding TRPs. A subset was then selected for experimental screening, leading to the identification of 11 novel DNA-binding TRPs, with six showing sequence specificity. Notably, members of the STAR (Short TALE-like Repeat proteins) family can be programmed to target specific 9 bp DNA sequences with high affinity. Leveraging this property, we generated artificial transcription factors using reprogrammed STAR proteins and achieved targeted activation of endogenous gene sets. Furthermore, the members of novel families such as MOON (Marine Organism-Originated DNA binding protein) and pTERF (prokaryotic mTERF-like protein) exhibit unique features and distinct DNA-binding characteristics, revealing interesting biological clues. Our study expands the diversity of DNA-binding TRPs, and demonstrates that a systematic approach greatly enhances the discovery of new biological insights and tools.

Transcriptional activation domains interact with ATPase subunits of yeast chromatin remodelling complexes SWI/SNF, RSC and INO80

Chromatin remodelling complexes (CRC) are ATP-dependent molecular machines important for the dynamic organization of nucleosomes along eukaryotic DNA. CRCs SWI/SNF, RSC and INO80 can move positioned nucleosomes in promoter DNA, leading to nucleosome-depleted regions which facilitate access of general transcription factors. This function is strongly supported by transcriptional activators being able to interact with subunits of various CRCs. In this work we show that SWI/SNF subunits Swi1, Swi2, Snf5 and Snf6 can bind to activation domains of Ino2 required for expression of phospholipid biosynthetic genes in yeast. We identify an activator binding domain (ABD) of ATPase Swi2 and show that this ABD is functionally dispensable, presumably because ABDs of other SWI/SNF subunits can compensate for the loss. In contrast, mutational characterization of the ABD of the Swi2-related ATPase Sth1 revealed that some conserved basic and hydrophobic amino acids within this domain are essential for the function of Sth1. While ABDs of Swi2 and Sth1 define separate functional protein domains, mapping of an ABD within ATPase Ino80 showed co-localization with its HSA domain also required for binding actin-related proteins. Comparative interaction studies finally demonstrated that several unrelated activators each exhibit a specific binding pattern with ABDs of Swi2, Sth1 and Ino80.

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 K ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

Identification of plant transcriptional activation domains

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

An activity-specificity trade-off encoded in human transcription factors

Transcription factors (TFs) control specificity and activity of gene transcription, but whether a relationship between these two features exists is unclear. Here we provide evidence for an evolutionary trade-off between the activity and specificity in human TFs encoded as submaximal dispersion of aromatic residues in their intrinsically disordered protein regions. We identified approximately 500 human TFs that encode short periodic blocks of aromatic residues in their intrinsically disordered regions, resembling imperfect prion-like sequences. Mutation of periodic aromatic residues reduced transcriptional activity, whereas increasing the aromatic dispersion of multiple human TFs enhanced transcriptional activity and reprogramming efficiency, promoted liquid-liquid phase separation in vitro and more promiscuous DNA binding in cells. Together with recent work on enhancer elements, these results suggest an important evolutionary role of suboptimal features in transcriptional control. We propose that rational engineering of amino acid features that alter phase separation may be a strategy to optimize TF-dependent processes, including cellular reprogramming.

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

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

Direct prediction of intermolecular interactions driven by disordered regions

Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.

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.

Intrinsically disordered regions (IDRs): A vague and confusing concept for protein function

The term "intrinsically disordered region" (IDR) in proteins has been used in numerous publications. However, most proteins contain IDRs, the term refers to very different types of structures and functions, and many IDRs become structured upon interaction with other biomolecules. Thus, IDR is an unnecessary, vague, and ultimately confusing concept.

Evolutionary analyses of intrinsically disordered regions reveal widespread signals of conservation

Intrinsically disordered regions (IDRs) are segments of proteins without stable three-dimensional structures. As this flexibility allows them to interact with diverse binding partners, IDRs play key roles in cell signaling and gene expression. Despite the prevalence and importance of IDRs in eukaryotic proteomes and various biological processes, associating them with specific molecular functions remains a significant challenge due to their high rates of sequence evolution. However, by comparing the observed values of various IDR-associated properties against those generated under a simulated model of evolution, a recent study found most IDRs across the entire yeast proteome contain conserved features. Furthermore, it showed clusters of IDRs with common "evolutionary signatures," i.e. patterns of conserved features, were associated with specific biological functions. To determine if similar patterns of conservation are found in the IDRs of other systems, in this work we applied a series of phylogenetic models to over 7,500 orthologous IDRs identified in the Drosophila genome to dissect the forces driving their evolution. By comparing models of constrained and unconstrained continuous trait evolution using the Brownian motion and Ornstein-Uhlenbeck models, respectively, we identified signals of widespread constraint, indicating conservation of distributed features is mechanism of IDR evolution common to multiple biological systems. In contrast to the previous study in yeast, however, we observed limited evidence of IDR clusters with specific biological functions, which suggests a more complex relationship between evolutionary constraints and function in the IDRs of multicellular organisms.

Assessment of machine-learning predictions for the Mediator complex subunit MED25 ACID domain interactions with transactivation domains

The human Mediator complex subunit MED25 binds transactivation domains (TADs) present in various cellular and viral proteins using two binding interfaces, named H1 and H2, which are found on opposite sides of its ACID domain. Here, we use and compare deep learning methods to characterize human MED25-TAD interfaces and assess the predicted models to published experimental data. For the H1 interface, AlphaFold produces predictions with high-reliability scores that agree well with experimental data, while the H2 interface predictions appear inconsistent, preventing reliable binding modes. Despite these limitations, we experimentally assess the validity of MED25 interface predictions with the viral transcriptional activators Lana-1 and IE62. AlphaFold predictions also suggest the existence of a unique hydrophobic pocket for the Arabidopsis MED25 ACID domain.

Intrinsically disordered regions of the Msn2 transcription factor encode multiple functions using interwoven sequence grammars

Intrinsically disordered regions (IDRs) are abundant in eukaryotic proteins, but their sequence-function relationship remains poorly understood. IDRs of transcription factors (TFs) can direct promoter selection and recruit coactivators, as shown for the budding yeast TF Msn2. To examine how IDRs encode both these functions, we compared genomic binding specificity, coactivator recruitment, and gene induction amongst a large set of designed Msn2-IDR mutants. We find that both functions depend on multiple regions across the > 600AA IDR. Yet, transcription activity was readily disrupted by mutations that showed no effect on the Msn2 binding specificity. Our data attribute this differential sensitivity to the integration of a relaxed, composition-based code directing binding specificity with a more stringent, motif-based code controlling the recruitment of coactivators and transcription activity. Therefore, Msn2 utilizes interwoven sequence grammars for encoding multiple functions, suggesting a new IDR design paradigm of potentially general use.

Coactivator condensation drives cardiovascular cell lineage specification

During development, cells make switch-like decisions to activate new gene programs specifying cell lineage. The mechanisms underlying these decisive choices remain unclear. Here, we show that the cardiovascular transcriptional coactivator myocardin (MYOCD) activates cell identity genes by concentration-dependent and switch-like formation of transcriptional condensates. MYOCD forms such condensates and activates cell identity genes at critical concentration thresholds achieved during smooth muscle cell and cardiomyocyte differentiation. The carboxyl-terminal disordered region of MYOCD is necessary and sufficient for condensate formation. Disrupting this region's ability to form condensates disrupts gene activation and smooth muscle cell reprogramming. Rescuing condensate formation by replacing this region with disordered regions from functionally unrelated proteins rescues gene activation and smooth muscle cell reprogramming. Our findings demonstrate that MYOCD condensate formation is required for gene activation during cardiovascular differentiation. We propose that the formation of transcriptional condensates at critical concentrations of cell type-specific regulators provides a molecular switch underlying the activation of key cell identity genes during development.

Defining the mechanisms and properties of post-transcriptional regulatory disordered regions by high-throughput functional profiling

Disordered regions within RNA binding proteins are required to control mRNA decay and protein synthesis. To understand how these disordered regions modulate gene expression, we surveyed regulatory activity across the entire disordered proteome using a high-throughput functional assay. We identified hundreds of regulatory sequences within intrinsically disordered regions and demonstrate how these elements cooperate with core mRNA decay machinery to promote transcript turnover. Coupling high-throughput functional profiling with mutational scanning revealed diverse molecular features, ranging from defined motifs to overall sequence composition, underlying the regulatory effects of disordered peptides. Machine learning analysis implicated aromatic residues in particular contexts as critical determinants of repressor activity, consistent with their roles in forming protein-protein interactions with downstream effectors. Our results define the molecular principles and biochemical mechanisms that govern post-transcriptional gene regulation by disordered regions and exemplify the encoding of diverse yet specific functions in the absence of well-defined structure.

Commonly asked questions about transcriptional activation domains

Eukaryotic transcription factors activate gene expression with their DNA-binding domains and activation domains. DNA-binding domains bind the genome by recognizing structurally related DNA sequences; they are structured, conserved, and predictable from protein sequences. Activation domains recruit chromatin modifiers, coactivator complexes, or basal transcriptional machinery via structurally diverse protein-protein interactions. Activation domains and DNA-binding domains have been called independent, modular units, but there are many departures from modularity, including interactions between these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly understood because they are poorly conserved, intrinsically disordered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, describes recent progress that the field has made in understanding the sequence features that control activation domains and predicting them from sequence.

The intrinsically disordered transcriptional activation domain of CIITA is functionally tuneable by single substitutions: An exception or a new paradigm?

During protein evolution, some amino acid substitutions modulate protein function ("tuneability"). In most proteins, the tuneable range is wide and can be sampled by a set of protein variants that each contains multiple amino acid substitutions. In other proteins, the full tuneable range can be accessed by a set of variants that each contains a single substitution. Indeed, in some globular proteins, the full tuneable range can be accessed by the set of site-saturating substitutions at an individual "rheostat" position. However, in proteins with intrinsically disordered regions (IDRs), most functional studies-which would also detect tuneability-used multiple substitutions or small deletions. In disordered transcriptional activation domains (ADs), studies with multiple substitutions led to the "acidic exposure" model, which does not anticipate the existence of rheostat positions. In the few studies that did assess effects of single substitutions on AD function, results were mixed: the ADs of two full-length transcription factors did not show tuneability, whereas a fragment of a third AD was tuneable by single substitutions. In this study, we tested tuneability in the AD of full-length human class II transactivator (CIITA). Sequence analyses and experiments showed that CIITA's AD is an IDR. Functional assays of singly-substituted AD variants showed that CIITA's function was highly tuneable, with outcomes not predicted by the acidic exposure model. Four tested positions showed rheostat behavior for transcriptional activation. Thus, tuneability of different IDRs can vary widely. Future studies are needed to illuminate the biophysical features that govern whether an IDR is tuneable by single substitutions.

Using High-Throughput Measurements to Identify Principles of Transcriptional and Epigenetic Regulators

To achieve exquisite control over the epigenome, we need a better predictive understanding of how transcription factors, chromatin regulators, and their individual domain's function, both as modular parts and as full proteins. Transcriptional effector domains are one class of protein domains that regulate transcription and chromatin. These effector domains either repress or activate gene expression by interacting with chromatin-modifying enzymes, transcriptional cofactors, and/or general transcriptional machinery. Here, we discuss important design considerations for high-throughput investigations of effector domains, recent advances in discovering new domains in human cells and testing how domain function depends on amino acid sequence. For every effector domain, we would like to know the following: What role does the cell type, signaling state, and targeted context have on activation, silencing, and epigenetic memory? Large-scale measurements of transcriptional activities can help systematically answer these questions and identify general rules for how all these parameters affect effector domain activities. Last, we discuss what steps need to be taken to turn a newly discovered effector domain into a robust, precise epigenome editor. With more carefully considered high-throughput investigations, soon we will have better predictive control over the epigenome.

The C. elegans Myc-family of transcription factors coordinate a dynamic adaptive response to dietary restriction

Dietary restriction (DR), the process of decreasing overall food consumption over an extended period of time, has been shown to increase longevity across evolutionarily diverse species and delay the onset of age-associated diseases in humans. In Caenorhabditis elegans, the Myc-family transcription factors (TFs) MXL-2 (Mlx) and MML-1 (MondoA/ChREBP), which function as obligate heterodimers, and PHA-4 (orthologous to forkhead box transcription factor A) are both necessary for the full physiological benefits of DR. However, the adaptive transcriptional response to DR and the role of MML-1::MXL-2 and PHA-4 remains elusive. We identified the transcriptional signature of C. elegans DR, using the eat-2 genetic model, and demonstrate broad changes in metabolic gene expression in eat-2 DR animals, which requires both mxl-2 and pha-4. While the requirement for these factors in DR gene expression overlaps, we found many of the DR genes exhibit an opposing change in relative gene expression in eat-2;mxl-2 animals compared to wild-type, which was not observed in eat-2 animals with pha-4 loss. We further show functional deficiencies of the mxl-2 loss in DR outside of lifespan, as eat-2;mxl-2 animals exhibit substantially smaller brood sizes and lay a proportion of dead eggs, indicating that MML-1::MXL-2 has a role in maintaining the balance between resource allocation to the soma and to reproduction under conditions of chronic food scarcity. While eat-2 animals do not show a significantly different metabolic rate compared to wild-type, we also find that loss of mxl-2 in DR does not affect the rate of oxygen consumption in young animals. The gene expression signature of eat-2 mutant animals is consistent with optimization of energy utilization and resource allocation, rather than induction of canonical gene expression changes associated with acute metabolic stress -such as induction of autophagy after TORC1 inhibition. Consistently, eat-2 animals are not substantially resistant to stress, providing further support to the idea that chronic DR may benefit healthspan and lifespan through efficient use of limited resources rather than broad upregulation of stress responses, and also indicates that MML-1::MXL-2 and PHA-4 may have different roles in promotion of benefits in response to different pro-longevity stimuli.

Clusters of acidic and hydrophobic residues can predict 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, models for predicting activation domains from protein sequence have lagged, particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors. Here, we show how the accuracy of this human predictor arises from the clustering of aromatic, leucine, and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of convolutional neural network (CNN) models trained in yeast, the intersection is more accurate 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.

Frontiers and techniques in plant gene regulation

Understanding plant gene regulation has been a priority for generations of plant scientists. However, due to its complex nature, the regulatory code governing plant gene expression has yet to be deciphered comprehensively. Recently developed methods-often relying on next-generation sequencing technology and state-of-the-art computational approaches-have started to further our understanding of the gene regulatory logic used by plants. In this review, we discuss these methods and the insights into the regulatory code of plants that they can yield.

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 Statement

Activation 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.

Surprising connections between DNA binding and function for the near-complete set of yeast transcription factors

DNA sequence-specific transcription factors (TFs) modulate transcription and chromatin architecture, acting from regulatory sites in enhancers and promoters of eukaryotic genes. How TFs locate their DNA targets and how multiple TFs cooperate to regulate individual genes is still unclear. Most yeast TFs are thought to regulate transcription via binding to upstream activating sequences, situated within a few hundred base pairs upstream of the regulated gene. While this model has been validated for individual TFs and specific genes, it has not been tested in a systematic way with the large set of yeast TFs. Here, we have integrated information on the binding and expression targets for the near-complete set of yeast TFs. While we found many instances of functional TF binding sites in upstream regulatory regions, we found many more instances that do not fit this model. In many cases, rapid TF depletion affects gene expression where there is no detectable binding of that TF to the upstream region of the affected gene. In addition, for most TFs, only a small fraction of bound TFs regulates the nearby gene, showing that TF binding does not automatically correspond to regulation of the linked gene. Finally, we found that only a small percentage of TFs are exclusively strong activators or repressors with most TFs having dual function. Overall, our comprehensive mapping of TF binding and regulatory targets have both confirmed known TF relationships and revealed surprising properties of TF function.

The trans-regulatory landscape of gene networks in plants

The transcriptional effector domains of transcription factors play a key role in controlling gene expression; however, their functional nature is poorly understood, hampering our ability to explore this fundamental dimension of gene regulatory networks. To map the trans-regulatory landscape in a complex eukaryote, we systematically characterized the putative transcriptional effector domains of over 400 Arabidopsis thaliana transcription factors for their capacity to modulate transcription. We demonstrate that transcriptional effector activity can be integrated into gene regulatory networks capable of elucidating the functional dynamics underlying gene expression patterns. We further show how our characterized domains can enhance genome engineering efforts and reveal how plant transcriptional activators share regulatory features conserved across distantly related eukaryotes. Our results provide a framework to systematically characterize the regulatory role of transcription factors at a genome-scale in order to understand the transcriptional wiring of biological systems.

The molecular grammar of protein disorder guiding genome-binding locations

Intrinsically disordered regions (IDRs) direct transcription factors (TFs) towards selected genomic occurrences of their binding motif, as exemplified by budding yeast's Msn2. However, the sequence basis of IDR-directed TF binding selectivity remains unknown. To reveal this sequence grammar, we analyze the genomic localizations of >100 designed IDR mutants, each carrying up to 122 mutations within this 567-AA region. Our data points at multivalent interactions, carried by hydrophobic-mostly aliphatic-residues dispersed within a disordered environment and independent of linear sequence motifs, as the key determinants of Msn2 genomic localization. The implications of our results for the mechanistic basis of IDR-based TF binding preferences are discussed.

The next wave of interactomics: Mapping the SLiM-based interactions of the intrinsically disordered proteome

Short linear motifs (SLiMs) are a unique and ubiquitous class of protein interaction modules that perform key regulatory functions and drive dynamic complex formation. For decades, interactions mediated by SLiMs have accumulated through detailed low-throughput experiments. Recent methodological advances have opened this previously underexplored area of the human interactome to high-throughput protein-protein interaction discovery. In this article, we discuss that SLiM-based interactions represent a significant blind spot in the current interactomics data, introduce the key methods that are illuminating the elusive SLiM-mediated interactome of the human cell on a large scale, and discuss the implications for the field.

Surveying the global landscape of post-transcriptional regulators

Numerous proteins regulate gene expression by modulating mRNA translation and decay. To uncover the full scope of these post-transcriptional regulators, we conducted an unbiased survey that quantifies regulatory activity across the budding yeast proteome and delineates the protein domains responsible for these effects. Our approach couples a tethered function assay with quantitative single-cell fluorescence measurements to analyze ~50,000 protein fragments and determine their effects on a tethered mRNA. We characterize hundreds of strong regulators, which are enriched for canonical and unconventional mRNA-binding proteins. Regulatory activity typically maps outside the RNA-binding domains themselves, highlighting a modular architecture that separates mRNA targeting from post-transcriptional regulation. Activity often aligns with intrinsically disordered regions that can interact with other proteins, even in core mRNA translation and degradation factors. Our results thus reveal networks of interacting proteins that control mRNA fate and illuminate the molecular basis for post-transcriptional gene regulation.

Interaction modules that impart specificity to disordered protein

Intrinsically disordered regions (IDRs) are especially enriched among proteins that regulate chromatin and transcription. As a result, mechanisms that influence specificity of IDR-driven interactions have emerged as exciting unresolved issues for understanding gene regulation. We review the molecular elements frequently found within IDRs that confer regulatory specificity. In particular, we summarize the differing roles of disordered low-complexity regions (LCRs) and short linear motifs (SLiMs) towards selective nuclear regulation. Examination of IDR-driven interactions highlights SLiMs as organizers of selectivity, with widespread roles in gene regulation and integration of cellular signals. Analysis of recurrent interactions between SLiMs and folded domains suggests diverse avenues for SLiMs to influence phase-separated condensates and highlights opportunities to manipulate these interactions for control of biological activity.

Large-scale mapping and mutagenesis of human transcriptional effector domains

Human gene expression is regulated by more than 2,000 transcription factors and chromatin regulators1,2. Effector domains within these proteins can activate or repress transcription. However, for many of these regulators we do not know what type of effector domains they contain, their location in the protein, their activation and repression strengths, and the sequences that are necessary for their functions. Here, we systematically measure the effector activity of more than 100,000 protein fragments tiling across most chromatin regulators and transcription factors in human cells (2,047 proteins). By testing the effect they have when recruited at reporter genes, we annotate 374 activation domains and 715 repression domains, roughly 80% of which are new and have not been previously annotated3-5. Rational mutagenesis and deletion scans across all the effector domains reveal aromatic and/or leucine residues interspersed with acidic, proline, serine and/or glutamine residues are necessary for activation domain activity. Furthermore, most repression domain sequences contain sites for small ubiquitin-like modifier (SUMO)ylation, short interaction motifs for recruiting corepressors or are structured binding domains for recruiting other repressive proteins. We discover bifunctional domains that can both activate and repress, some of which dynamically split a cell population into high- and low-expression subpopulations. Our systematic annotation and characterization of effector domains provide a rich resource for understanding the function of human transcription factors and chromatin regulators, engineering compact tools for controlling gene expression and refining predictive models of effector domain function.