In a collaborative effort with the Sinz lab, our Post-doc Christian led a project on the utilization of AI-derived protein modeling based on mass spectrometry data, in order to interpret unknown cryo-EM maps from within native cell extracts!
This type of data integration is definitely the way to improve our understanding of native protein complexes. You can read all about this work HERE.
This is a really exciting piece of work 👏
Combining AI-based structure prediction with proteomics and cryo-EM data feels like a powerful step forward, especially for tackling heterogeneous, native cell extracts where traditional approaches struggle. Using AlphaFold2 models to help interpret unknown cryo-EM maps makes a lot of sense and nicely bridges the gap between computational predictions and experimental data.
Great to see such a strong collaboration with the Sinz lab and congratulations to Christian on leading this project. This kind of integrative strategy really does seem like the future for understanding native protein complexes in their physiological context. Looking forward to seeing how this approach is applied to even more complex systems!
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Mapping IP addresses to locations can add an extra layer of context when analyzing large proteomics datasets, especially if the data access spans multiple regions. Tools like IP to country API make it easier to see geographic trends in data usage or collaborations.
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Interesting insights on how police clearance processes rely on accurate and reliable data, much like how AlphaFold2 uses proteomics data to improve predictions. Ensuring the integrity of information is crucial whether in biological research or administrative procedures. This makes me appreciate the parallels between data validation in science and official documentation workflows.
It’s fascinating how integrating structural predictions with proteomics data can reveal hidden constraints in cellular pathways. Using Bottleneck Analysis on these pathways could help pinpoint critical steps that limit overall process efficiency. This approach might make it easier to prioritize targets for experimental validation.
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