3D structure of biomolecules: A “dictionary” allows access to fluorescence-based data

Data from fluorescence experiments are processed with the help of a “dictionary” and made available along with an integrated structural model in a database. Credit: Nature Methods (2024). DOI: 10.1038/s41592-024-02428-x
A German and US research team led by Heinrich Heine University (HHU) in Düsseldorf has developed a data description that can provide the results of fluorescence measurements for structural and dynamic modeling of large biomolecules.
The authors explain in the journal Nature Methods that for the first time, other researchers have access to an integrated fluorescence-based structural model and its dynamics through the database. This provides experiment-based training data for next-generation AI tools for dynamic structural modeling.
Proteins and nucleic acids are the central building blocks of life in all living organisms. These biomolecules are made up of many individual building blocks, such as amino acids in the case of proteins.
When individual components are assembled within cells, biomolecules form complex three-dimensional structures. Their specific shape is determined by the components and the forces between them. However, to understand the function of biomolecules, it is important to consider not only their three-dimensional structure but also the number of different structural states and the exchange dynamics between them.
For a long time, determining the 3D structure of biomolecules using classical biophysical methods was extremely complex and time-consuming. To progressively simplify and systematize this work, all these three-dimensional structures have been collected in the “Protein Data Bank” (PDB) since 1971. These 220,000+ structures are used in AI-based tools such as AlphaFold. “This development, which won the Nobel Prize in Chemistry this year, was used as training data for a neural network.
AI systems are already successfully predicting the structure of biomolecules. However, due to the lack of experimental data, the tool cannot predict the dynamics at this time.
Therefore, it is important to use powerful experimental methods such as fluorescence spectroscopy that combine to provide information about the dynamics and structure of complex biomolecules. In fluorescence experiments, specific interesting parts of biomolecules are marked with small dye molecules that light up when excited externally, revealing their location. Integrated modeling methods combine such experimental data with computational methods to achieve higher structural resolution and account for mechanics.
“Fluorescence experiments provide detailed information and are therefore an excellent data source for integrated modeling,” said Dr. Christian Hanke, a postdoctoral researcher in the HHU Institute of Molecular Physical Chemistry and lead author of the paper. To fully exploit this wealth of information, it must be made accessible and usable by the broader scientific community.”
In this publication, a research team from HHU, Rutgers State University, New Jersey, and the University of California, San Francisco presents standardized data descriptions in the form of three linked “dictionaries” organized in a common library. I am.
Professor Klaus Seidel of HHU, one of the two corresponding authors, said: “This construction principle combined with the dictionary allows researchers for the first time to store integrated structural models based on fluorescence data together with kinetic information. At the same time, this general definition can be used in other ways to record the dynamic properties of biomolecules together with their structures in databases.
This approach is necessary to connect static structural models to the fundamental energy landscape, that is, the energy differences between different three-dimensional arrangements of components within biomolecules. “This information will enable the development and training of the next generation of AI-based programs to predict dynamic biomolecular structures,” Professor Seidel said. The data obtained can make a very important contribution.”
Further information: Christian A. Hanke et al, Making fluorescent-based integral structure and association kinetic information available, Nature Methods (2024). DOI: 10.1038/s41592-024-02428-x
Provided by Heinrich-Heine University of Düsseldorf
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