WikiSysop: Created page with "== Citation == K. D. Carr et al., “Protein identification using cryo-EM and artificial intelligence guides improved sample purification,” Journal of Structural Biology: X, p. 100120, 2025. == Abstract == Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However..."

2025-07-01T06:46:03Z

Created page with "== Citation == K. D. Carr et al., “Protein identification using cryo-EM and artificial intelligence guides improved sample purification,” Journal of Structural Biology: X, p. 100120, 2025. == Abstract == Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the determination of protein structures, the study of biological mechanisms, and the characterization of both natural and de novo designed proteins. However..."

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== Citation ==

K. D. Carr et al., “Protein identification using cryo-EM and artificial intelligence guides improved sample purification,” Journal of Structural Biology: X, p. 100120, 2025.

== Abstract ==

Protein purification is essential in protein biochemistry, structural biology, and protein design, enabling the
determination of protein structures, the study of biological mechanisms, and the characterization of both natural
and de novo designed proteins. However, standard purification strategies often encounter challenges, such as
unintended co-purification of contaminants alongside the target protein. This issue is particularly problematic for
self-assembling protein nanomaterials, where unexpected geometries may reflect novel assembly states, crosscontamination,
or native proteins originating from the expression host. Here, we used an automated structureto-
sequence pipeline to first identify an unknown co-purifying protein found in several purified designed protein
samples. By integrating cryo-electron microscopy (Cryo-EM), ModelAngelo’s sequence-agnostic modelbuilding,
and Protein BLAST, we identified the contaminant as dihydrolipoamide succinyltransferase (DLST).
This identification was validated through comparisons with DLST structures in the Protein Data Bank, AlphaFold
3 predictions based on the DLST sequence from our E. coli expression vector, and traditional biochemical
methods. The identification informed subsequent modifications to our purification protocol, which successfully
excluded DLST from future preparations. To explore the potential broader utility of this approach, we benchmarked
four computational methods for DLST identification across varying resolution ranges. This study demonstrates
the successful application of a structure-to-sequence protein identification workflow, integrating Cryo-
EM, ModelAngelo, Protein BLAST, and AlphaFold 3 predictions, to identify and ultimately help guide the removal
of DLST from sample purification efforts. It highlights the potential of combining Cryo-EM with AI-driven
tools for accurate protein identification and addressing purification challenges across diverse contexts in protein
science.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S2590152425000017

== Related software ==

== Related methods ==

== Comments ==

2025Carr Map2Seq - Revision history