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

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cris.lastimport.scopus2024-02-12T20:15:29Z
dc.abstract.enAcidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, “fuzzy” interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorFishburn, James
dc.contributor.authorSohrabi-Jahromi, Salma
dc.contributor.authorErijman, Ariel
dc.contributor.authorNoble, William S.
dc.contributor.authorSchreiber, Jacob
dc.contributor.authorWarfield, Linda
dc.contributor.authorHahn, Steven
dc.contributor.authorSöding, Johannes
dc.contributor.authorKozłowski, Łukasz
dc.date.accessioned2024-01-24T17:45:57Z
dc.date.available2024-01-24T17:45:57Z
dc.date.issued2020
dc.description.financePublikacja bezkosztowa
dc.description.number5
dc.description.volume78
dc.identifier.doi10.1016/J.MOLCEL.2020.04.020
dc.identifier.issn1097-2765
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/101592
dc.identifier.weblinkhttps://api.elsevier.com/content/article/PII:S1097276520302628?httpAccept=text/xml
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.ispartofMolecular Cell
dc.relation.pages890-902.e6
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleA High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning
dc.typeJournalArticle
dspace.entity.typePublication