PDB Correlator - how can it help you to establish a link between structure and functionality?

It has been almost two years since Google has launched its AlphaFold Protein Structure Database.   Since then, thanks to our pre-existing PDB Correlator tool, decades of knowledge gathered in Protein Data Bank, and AlphaFold AI are within just a few clicks to any of the FIDA technology users.

The correlation of structural data and FIDA measurements sets a whole new standard for streamlined and data-driven protein analysis with the increased data reliability required for transformative discoveries in  biomolecular research. In this article from our QC series, we will brief you on the  benefits that comes from integrating biophysical technologies and AI tools.

Tracing the Evolution of Protein Structure Research

From early observations in the late 19th century to the cutting-edge techniques and AI-based tools of the last decade, scientists have made remarkable strides in unraveling the intricate world of protein structure. The research on protein structure has been a captivating revolution in our understanding of life's building blocks.

Now, the structures (PDB files) generated by AlphaFold can be imported into the Fida Software’s PDB correlator the same way as measured structures from the PDB.

This allows FIDA users to seamlessly work with their PDB files across either FIDA Software, AlphaFold or Protein Data Bank.

Decades of knowledge within reach of a click

The PDB Correlator performs size quality control in a few seconds, leveraging on existing and validated knowledge. It allows for the direct correlation of structural information with experimental data, facilitating the identification of potential discrepancies and guiding further investigation. Eventually, this leads to a substantial enhancement in data reliability.

The edge brought by precision

To use the PDB database or AlphaFold as a QC tool, one needs access to a protein analysis technology that fulfils two basic conditions. Firstly, the technology must be able in itself to reach high levels of precision; a size-based technology should be able to reliably differentiate less then 5% size changes. Secondly, such a technology must deliver structurally relevant data i.e. absolute size.

In order to foster a greater understanding, let us turn to the example provided above. A Fida 1 absolute size measurement of a bovine beta-lactoglobulin sample yields a Rh of 2.77nm ± 0.06. According to the FIDA PDB correlator, a significant discrepancy of 28% is discovered (predicted vs Fida1). This reveals  that the in-solution structure of the protein  is not a monomer. As a next step, the researcher uses PyMOL to predict how the structure would be if it were a homodimer, which yields a predicted size of 2.85 nm. i.e., indications are that the analysed sample is a homodimeric structure of BLG.

Clearly, in a scenario where the researcher operates with technology of limited precision, (particularly in terms of size), or does not have direct integration with the entire PDB database, the identification of the homodimer would not have been such a straightforward process.

Interested in this example? Access the application note for more details.

Decoding How Your Protein Behaves in Solution

An attentive reader has spotted a key piece of information contained in the previous paragraph – this experiment was conducted in solution. Working in-solution increases the validity and applicability of the result and allows the user to unravel the complex stability interactions between structure, dynamics  and the oligomeric states. On top of that, since FIDA has high tolerance, it can be used for analysis of oligomeric states under diverse in-solution conditions (buffers, pH, matrices, saltines).

• We have written more about the importance of studying proteins ‘in their natural habitat’ in our article about hydrodynamic radius.
• Note that hydrodynamic radius is related to the molecular mass of the protein, to read more about it, see our technical note here.

By scientists, for scientists

As new technologies and algorithms continue to expand the boundaries of protein structure analysis, the integration with the PDB database provides a solid foundation for researchers to adapt and integrate these advancements into their workflows.  FIDA’s first principle foundation and integration with structural data ensures that researchers can keep pace with emerging tools, methodologies, and data analysis techniques.

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