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1. Conservation of Affinity Rather Than Sequence Underlies a Dynamic Evolution of the Motif-Mediated p53/MDM2 Interaction in Ray-Finned Fishes

   https://doi.org/10.1093/molbev/msae018  

   Mihalič, Filip; Arcila, Dahiana; Pettersson, Mats E; Farkhondehkish, Pouria; Andersson, Eva; Andersson, Leif; Betancur-R, Ricardo; Jemth, Per (February 2024, Molecular Biology and Evolution)

   O'Connell, Mary (Ed.)

   Abstract The transcription factor and cell cycle regulator p53 is marked for degradation by the ubiquitin ligase MDM2. The interaction between these 2 proteins is mediated by a conserved binding motif in the disordered p53 transactivation domain (p53TAD) and the folded SWIB domain in MDM2. The conserved motif in p53TAD from zebrafish displays a 20-fold weaker interaction with MDM2, compared to the interaction in human and chicken. To investigate this apparent difference, we tracked the molecular evolution of the p53TAD/MDM2 interaction among ray-finned fishes (Actinopterygii), the largest vertebrate clade. Intriguingly, phylogenetic analyses, ancestral sequence reconstructions, and binding experiments showed that different loss-of-affinity changes in the canonical binding motif within p53TAD have occurred repeatedly and convergently in different fish lineages, resulting in relatively low extant affinities (KD = 0.5 to 5 μM). However, for 11 different fish p53TAD/MDM2 interactions, nonconserved regions flanking the canonical motif increased the affinity 4- to 73-fold to be on par with the human interaction. Our findings suggest that compensating changes at conserved and nonconserved positions within the motif, as well as in flanking regions of low conservation, underlie a stabilizing selection of “functional affinity” in the p53TAD/MDM2 interaction. Such interplay complicates bioinformatic prediction of binding and calls for experimental validation. Motif-mediated protein–protein interactions involving short binding motifs and folded interaction domains are very common across multicellular life. It is likely that the evolution of affinity in motif-mediated interactions often involves an interplay between specific interactions made by conserved motif residues and nonspecific interactions by nonconserved disordered regions. 

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

   https://doi.org/10.1101/2024.08.19.608698  

   DelRosso, Nicole; Suzuki, Peter H; Griffith, Daniel; Lotthammer, Jeffrey M; Novak, Borna; Kocalar, Selin; Sheth, Maya U; Holehouse, Alex S; Bintu, Lacramioara; Fordyce, Polly (August 2024, bioRxiv)

   Abstract Sequence-specific activation by transcription factors is essential for gene regulation^1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors^3,4and recruit co-activators to initiate transcription⁵. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient^6,7. 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 (>1,500K_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, andin vitroaffinities 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. 

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3. Identification of plant transcriptional activation domains

   https://doi.org/10.1038/s41586-024-07707-3  

   Morffy, Nicholas; Van_den_Broeck, Lisa; Miller, Caelan; Emenecker, Ryan J; Bryant, John A; Lee, Tyler M; Sageman-Furnas, Katelyn; Wilkinson, Edward G; Pathak, Sunita; Kotha, Sanjana R; et al (August 2024, Nature)

   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. 

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4. Commonly asked questions about transcriptional activation domains

   https://doi.org/10.1016/j.sbi.2023.102732  

   Udupa, Aditya; Kotha, Sanjana R; Staller, Max V (February 2024, Current Opinion in Structural Biology)

   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 tran- scriptional 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 be- tween these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly under- stood because they are poorly conserved, intrinsically disor- dered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, de- scribes recent progress that the field has made in under- standing the sequence features that control activation domains and predicting them from sequence. 

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5. Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence

   https://doi.org/10.1093/genetics/iyad131  

   Kotha, Sanjana R; Staller, Max Valentín (July 2023, GENETICS)

   Kaplan, C (Ed.)

   Abstract 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. 

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