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      Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator

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          Abstract

          Gene activator proteins comprise distinct DNA-binding and transcriptional activation domains (ADs). Because few ADs have been described, we tested domains tiling all yeast transcription factors for activation in vivo and identified 150 ADs. By mRNA display, we showed that 73% of ADs bound the Med15 subunit of Mediator, and that binding strength was correlated with activation. AD-Mediator interaction in vitro was unaffected by a large excess of free activator protein, pointing to a dynamic mechanism of interaction. Structural modeling showed that ADs interact with Med15 without shape complementarity (‘fuzzy’ binding). ADs shared no sequence motifs, but mutagenesis revealed biochemical and structural constraints. Finally, a neural network trained on AD sequences accurately predicted ADs in human proteins and in other yeast proteins, including chromosomal proteins and chromatin remodeling complexes. These findings solve the longstanding enigma of AD structure and function and provide a rationale for their role in biology.

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          Cells adapt and respond to changes by regulating the activity of their genes. To turn genes on or off, they use a family of proteins called transcription factors. Transcription factors influence specific but overlapping groups of genes, so that each gene is controlled by several transcription factors that act together like a dimmer switch to regulate gene activity.

          The presence of transcription factors attracts proteins such as the Mediator complex, which activates genes by gathering the protein machines that read the genes. The more transcription factors are found near a specific gene, the more strongly they attract Mediator and the more active the gene is. A specific region on the transcription factor called the activation domain is necessary for this process. The biochemical sequences of these domains vary greatly between species, yet activation domains from, for example, yeast and human proteins are often interchangeable.

          To understand why this is the case, Sanborn et al. analyzed the genome of baker’s yeast and identified 150 activation domains, each very different in sequence. Three-quarters of them bound to a subunit of the Mediator complex called Med15. Sanborn et al. then developed a machine learning algorithm to predict activation domains in both yeast and humans. This algorithm also showed that negatively charged and greasy regions on the activation domains were essential to be activated by the Mediator complex.

          Further analyses revealed that activation domains used different poses to bind multiple sites on Med15, a behavior known as ‘fuzzy’ binding. This creates a high overall affinity even though the binding strength at each individual site is low, enabling the protein complexes to remain dynamic. These weak interactions together permit fine control over the activity of several genes, allowing cells to respond quickly and precisely to many changes.

          The computer algorithm used here provides a new way to identify activation domains across species and could improve our understanding of how living things grow, adapt and evolve. It could also give new insights into mechanisms of disease, particularly cancer, where transcription factors are often faulty.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              The Protein Data Bank.

              The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                27 April 2021
                2021
                : 10
                : e68068
                Affiliations
                [1 ]Department of Structural Biology, Stanford University School of Medicine StanfordUnited States
                [2 ]Department of Computer Science, Stanford University StanfordUnited States
                [3 ]The Center for Genome Architecture, Baylor College of Medicine HoustonUnited States
                [4 ]Center for Theoretical Biological Physics, Rice University HoustonUnited States
                Eunice Kennedy Shriver National Institute of Child Health and Human Development United States
                Harvard Medical School United States
                Eunice Kennedy Shriver National Institute of Child Health and Human Development United States
                Author information
                https://orcid.org/0000-0002-4725-8012
                http://orcid.org/0000-0001-9397-6392
                http://orcid.org/0000-0003-2183-0698
                https://orcid.org/0000-0002-2425-7519
                Article
                68068
                10.7554/eLife.68068
                8137143
                33904398
                185b52a1-7263-4ed4-bbba-00abe50a58ae
                © 2021, Sanborn et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 03 March 2021
                : 25 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01-DK121366
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Award ID: Scientific Discovery through Advanced Computing (SciDAC) program
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 4D Nucleome Grant (U01HL130010)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: Encyclopedia of DNA Elements Mapping Center Award (UM1HG009375)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: PHY-2019745, Center for Theoretical Biological Physics
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000928, Welch Foundation;
                Award ID: Q-1866
                Award Recipient :
                Funded by: McNair Medical Institute;
                Award ID: Scholar Award
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006221, United States - Israel Binational Science Foundation;
                Award ID: 2019276
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004316, IBM Corporation;
                Award ID: In-kind support
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000005, U.S. Department of Defense;
                Award ID: National Defense Science & Engineering Graduate (NDSEG) Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Award ID: Office of Science Graduate Student Research (SCGSR) program (DE-SC0014664)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: F32-GM126704
                Award Recipient :
                Funded by: Illumina;
                Award ID: In-kind support
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01-AI021144
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 4D Nucleome Grant (U01HL156059)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: PHY-1427654, Center for Theoretical Biological Physics
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DBI-2021795, Behavioral Plasticity Research Institute
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Chromosomes and Gene Expression
                Computational and Systems Biology
                Custom metadata
                Transcriptional activation domains achieve rapid, dynamic, specific interaction with Mediator through binding of an unstructured peptide to multiple hydrophobic surfaces without particular amino acid side chain interactions.

                Life sciences
                transcription factor,activation domain,mediator complex,machine learning,high-throughput screening,molecular simulation,human,s. cerevisiae

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