Birte Höcker

Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTCatalytic Versatility, Stability, and Evolution of the (βα)8-Barrel Enzyme FoldReinhard Sterner and Birte HöckerView Author Information Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstrasse 31, D-93053 Regensburg, Germany, and Department of Biochemistry, Duke University Medical Center, Box 3711, Durham, North Carolina 27710 Cite this: Chem. Rev. 2005, 105, 11, 4038–4055Publication Date (Web):November 9, 2005Publication History Received16 June 2005Published online9 November 2005Published inissue 1 November 2005https://pubs.acs.org/doi/10.1021/cr030191zhttps://doi.org/10.1021/cr030191zresearch-articleACS PublicationsCopyright © 2005 American Chemical SocietyRequest reuse permissionsArticle Views2142Altmetric-Citations166LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Chemical structure,Monomers,Peptides and proteins,Reaction mechanisms,Stability Get e-Alerts

Abstract One of the most important regulatory small molecules in plants is indole-3-acetic acid, also known as auxin. Its dynamic redistribution has an essential role in almost every aspect of plant life, ranging from cell shape and division to organogenesis and responses to light and gravity 1,2 . So far, it has not been possible to directly determine the spatial and temporal distribution of auxin at a cellular resolution. Instead it is inferred from the visualization of irreversible processes that involve the endogenous auxin-response machinery 3–7 ; however, such a system cannot detect transient changes. Here we report a genetically encoded biosensor for the quantitative in vivo visualization of auxin distribution. The sensor is based on the Escherichia coli tryptophan repressor 8 , the binding pocket of which is engineered to be specific to auxin. Coupling of the auxin-binding moiety with selected fluorescent proteins enables the use of a fluorescence resonance energy transfer signal as a readout. Unlike previous systems, this sensor enables direct monitoring of the rapid uptake and clearance of auxin by individual cells and within cell compartments in planta. By responding to the graded spatial distribution along the root axis and its perturbation by transport inhibitors—as well as the rapid and reversible redistribution of endogenous auxin in response to changes in gravity vectors—our sensor enables real-time monitoring of auxin concentrations at a (sub)cellular resolution and their spatial and temporal changes during the lifespan of a plant.