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Titel:

Modular protein scaffold architecture and AI-guided sequence optimization facilitate de novo metalloenzyme engineering

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
17.10.2025
Verantwortlich:
Zeymer, Cathleen
Autorinnen / Autoren:
Wagner Egea, Paula; Delhommel, Florent; Mustafa, Ghulam; Leiss-Maier, Florian; Klimper, Lisa; Badmann, Thomas; Heider, Anna; Wille, Idoia; Groll, Michael; Sattler, Michael; Zeymer, Cathleen
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2025mp1798728
Enddatum der Datenerzeugung:
01.09.2025
Fachgebiet:
BIO Biowissenschaften; CHE Chemie; NAT Naturwissenschaften (allgemein)
zusätzliche Fachgebiete:
Biochemistry, Structural Biology
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations; Simulationen / simulations
Datentyp:
Bilder / images ; mehrdimensionale Visualisierungen oder Modelle / models; Texte / texts; Tabellen / tables
Methode der Datenerhebung:
All data were generated with standard laboratory instrumentation such as NMR spectroscopy, UV/Vis spectroscopy, CD spectroscopy, fluorescence spectroscopy or different types of chromatography, available to our lab at TUM. The exact instruments and experimental conditions are specified in the publication. Furthermore, molecular dynamics (MD) simulations are included in the deposited data set.
Beschreibung:
Here, we make available all data included in the manuscript "Modular protein scaffold architecture and AI-guided sequence optimization facilitate de novo metalloenzyme engineering" as raw data files.
Links:
This dataset relates to the publication: http://doi.org/10.1016/j.str.2025.10.010
Schlagworte:
Protein Design; Enzyme Engineering; Conformational Dynamics; X-ray Crystallography; NMR Spectroscopy; MD simulations
Technische Hinweise:
View and download ( 447 MB total, 990 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1798728):
rsync rsync://m1798728@dataserv.ub.tum.de/m1798728/
Sprache:
en
Rechte:
by-nc-nd, http://creativecommons.org/licenses/by-nc-nd/4.0
Horizon 2020:
ERC Starting Grant “PhotoLanZyme” (101039592)
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