Result Details

Mobilizing the Biocatalysis Community for Reproducible and Reusable Data Collection

MARQUES, S.; PLANAS-IGLESIAS, J.; VELECKY, J.; MUSIL, M.; ASANO, Y.; BOROWSKI, T.; BRISSOS, V.; CESPUGLI, M.; CHOROZIAN, K.; DADASHIPOUR, M.; ERDEM, E.; FERRANDI, E.; GRIGORAKIS, K.; KLUZA, A.; LAWNICZEK, J.; MAKRYNIOTIS, K.; MONTI, D.; NESTL, B.; NGO, A.; NIKOLAIVITS, E.; PATTI, S.; PENTARI, C.; RODRIGUES, C.; SCHOPPER, T.; SEWERYN-OZOG, K.; SZALENIEC, M.; TABORDA, A.; TATARUCH, M.; TISCHLER, D.; TOPAKAS, E.; WANG, J.; WOJCIK, P.; WOJTKIEWICZ, A.; WOODLEY, J.; ZASTAWNY, O.; MARTINS, L.; FRAAIJE, M.; PLEISS, J.; SCHNELL, S.; DAMBORSKY, J.; MAZURENKO, S.; BEDNAR, D. Mobilizing the Biocatalysis Community for Reproducible and Reusable Data Collection. ACS Catalysis, 2026, vol. 16, iss. 10, p. 8858-8868.
Type
journal article
Language
English
Authors
Marques Sergio M.
Planas-Iglesias Joan
Velecky Jan
Musil Miloš, Ing., Ph.D., DIFS (FIT)
Asano Yasuhisa
Borowski Tomasz
Brissos Vania
Cespugli Marco
Chorozian Koar
Dadashipour Mohammad
Erdem Elif
Ferrandi Erica Elisa
Grigorakis Konstantinos
Kluza Anna
Lawniczek Janina
Makryniotis Konstantinos
Monti Daniela
Nestl Bettina
Ngo Anna C.
Nikolaivits Efstratios
Patti Stefania
Pentari Christina
Rodrigues Carolina F.
Schopper Tobias
Seweryn-Ozog Karolina
Szaleniec Maciej
Taborda Andre
Tataruch Mateusz
Tischler Dirk
Topakas Evangelos
Wang Jingyu
Wojcik Patrycja
Wojtkiewicz Agnieszka M.
Woodley John M.
Zastawny Olga
Martins Ligia O.
Fraaije Marco
Pleiss Jurgen
Schnell Santiago
Damborsky Jiri
Mazurenko Stanislav
Bednar David
Abstract

Science is an ever-evolving endeavor, with all new research grounded in knowledge gained in previous studies and publications. This applies not only at the level of theory and fundamental knowledge, but also at the level of specific data. In the context of enzyme research, that includes information on properties such as protein production and folding, protein solubility, stability, catalytic activity, together with specificity and stereoselectivity, as well as regulatory effects as activation and inhibition, and kinetics, which are crucial for multiple practical reasons. In the fields of biology and biochemistry, the availability of high-quality experimental data has already contributed to several breakthroughs over time. One example is AlphaFold 2, released in 2021, a machine learning-based tool that predicts the 3D structures of proteins with unprecedented accuracy. Its release represented a major breakthrough in structural biology, addressing a long-standing challenge that had persisted for decades. A key element in the success of AlphaFold was the large number of experimental protein structures available in the Protein Data Bank (ca. 159,000 in 2019). This was made possible because the deposition of crystallographic, nuclear magnetic resonance (NMR), and electron microscopy (cryo-EM) structures in a uniform format into databases became the gold standard and a strict requirement for their publication three decades before the AlphaFold release. Thanks to the high quality and the large volume of its data, the Protein Data Bank also enabled the development of molecular docking and other tools. Other examples are UniProt and BRENDA, databases that contributed to functional prediction tools, metabolic modeling, and large-scale enzyme design efforts. Their success relies heavily on community contributions, data quality checks, and manual curation.

Keywords

Data management; FAIR principles; scientific data

URL
Published
2026
Pages
8858–8868
Journal
ACS Catalysis, vol. 16, no. 10, ISSN
Publisher
Amer Chemical Soc
DOI
10.1021/acscatal.5c07904
UT WoS
001757682000001
EID Scopus
2-s2.0-105039219711
BibTeX 
@article{BUT212408,
  author="{} and  {} and  {} and Miloš {Musil} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {} and  {}",
  title="Mobilizing the Biocatalysis Community for Reproducible and Reusable Data Collection",
  journal="ACS Catalysis",
  year="2026",
  volume="16",
  number="10",
  pages="8858--8868",
  doi="10.1021/acscatal.5c07904",
  issn="2155-5435",
  url="https://pubs.acs.org/accacs/article/16/10/8858/5069605/Mobilizing-the-Biocatalysis-Community-for"
}
Departments
Department of Information Systems (DIFS)
Loschmidtovy laboratoře (LL PrF MU)
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