bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.09.11.612515

Protein identification using cryo-EM and artificial intelligence guides improved sample purification

Carr, K. D.·Zambrano, D. E. D.·Weidle, C.·Goodson, A.·Eisenach, H. E.·Pyles, H.·Courbet, A.·King, N. P.·Borst, A. J.
View original source ↗Export metadata ↓

Abstract

Protein purification is essential in protein biochemistry, structural biology, and protein design. It enables the determination of protein structures, the study of biological mechanisms, and the biochemical and biophysical characterization of both natural and de novo designed proteins. Despite the broad application of various protein purification protocols, standard strategies can still encounter challenges, such as the unintended co-purification of unknown contaminants alongside the target protein. In particular, co-purification issues pose significant challenges for designed self-assembling protein nanomaterials, as it is difficult to determine whether unexpected observed geometries represent novel assembly states of the designed system, cross-contamination from other assemblies, or native proteins originating from the expression host. In this study, we assessed the ability of an automated structure-to-sequence pipeline to unambiguously identify an unknown co-purifying protein found across several purified designed protein samples. Using cryo-electron microscopy (Cryo-EM), ModelAngelos sequence-agnostic automated model-building feature, and the Basic Local Alignment Search Tool (BLAST), we identified the unknown protein as dihydrolipoamide succinyltransferase (DLST). This identification was further confirmed by comparing the cryo-EM data with available DLST structures in the Protein Data Bank (PDB) and AlphaFold 3 predictions from the top BLAST hits. The clear identification of DLST informed our subsequent literature search and led to the rational modification of our protein purification protocol, ultimately enabling the exclusion of the contaminant from preparations of our target nanoparticle. This study demonstrates the successful application of a structure-to-sequence workflow, integrating Cryo-EM, ModelAngelo, protein BLAST, PDB structures, and AlphaFold 3 predictions, to identify and remove an unknown protein from a purified sample. It also highlights the broader potential of integrating Cryo-EM with AI-driven tools for accurate protein identification across various samples and contexts within protein science. HighlightsO_LIAn unknown protein was consistently found in multiple de novo designed protein samples. C_LIO_LIThe protein was identified as dihydrolipoamide succinyltransferase (DLST) using Cryo-EM, ModelAngelo and BLAST, and further verified using AlphaFold 3 and the PDB. C_LIO_LIIdentification enabled rational modification of the purification protocol to exclude the contaminant. C_LIO_LIThis method enables accurate protein identification without requiring near-atomic resolution or prior sequence and structural data, making it broadly applicable to various areas of protein science. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/612515v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@592410org.highwire.dtl.DTLVardef@4ea3f3org.highwire.dtl.DTLVardef@edd0bcorg.highwire.dtl.DTLVardef@122fba2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

biochemistryPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Carr, K. D., Zambrano, D. E. D., Weidle, C., Goodson, A., Eisenach, H. E., Pyles, H., Courbet, A., King, N. P., Borst, A. J.. 2024-09-12. Protein identification using cryo-EM and artificial intelligence guides improved sample purification. https://doi.org/10.1101/2024.09.11.612515

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Hierarchical cysteine oxidation controls reversible amyloid formation in an ankyrin repeat protein

The formation of amyloids, including functional amyloids, is observed for an increasing number of proteins but the molecular mechanisms that control this structural transition remain poorly understood. Here we report that the kinase inhibitor protein P18 (drP18) from Danio rerio (zebrafish), which contains two cysteine residues, undergoes a complex and hierarchical redox switch that strictly governs reversible amyloid formation. We identify cysteine 50 (C50) acting as a regulatory residue. Upon oxidation, C50 forms an intramolecular disulfide bond with the executioner cysteine 128 (C128), thereby blocking it. C50 can become S-glutathionylated, and upon oxidation, C128 then forms intermolecular disulfides that lead to rapid transition into amyloid fibrils. S-glutathionylation of C50 therefore enables amyloid formation of drP18 and the outcome is oxidant-dependent with diamide, hydrogen peroxide, peroxymonocarbonate and hypothiocyanous acid each leading to amyloid assembly with distinct kinetics and morphologies. These amyloids are fully reversible, where disulfide reduction is leading to disassembly. Whereas monomeric drP18 inhibits CDK4-mediated retinoblastoma phosphorylation, the amyloid conformation abolishes this inhibition, and reduction restores both structure and function. Expression of drP18 in zebrafish embryos yields Congo red-positive, oxidation-dependent aggregates in vivo. Together, our findings show that a regulatory cysteine controls an executioner cysteine to induce reversible, functional amyloid formation, revealing that proteins can encode sophisticated mechanisms to control amyloid assembly.

biochemistry↗
bioRxiv2026

Snapshots from the Catalytic Landscape of Chalcone Isomerase

Chalcone isomerase (CHI) catalyzes the cyclization of 3-ring scaffolds of flavonoids, a class of plant-based natural products important for nutrition and disease prevention. A persistent question has been whether the enzyme uses dynamics to facilitate conformational rearrangements of substrates within the active site. To help resolve this question, CHI was crystallized with phloretin, a flexible substrate analogue that cannot undergo cyclization. The crystal structure possesses eight protein molecules per asymmetric unit, revealing different active site conformations that accommodate different bound conformers of phloretin. Together, the structural snapshots depict a series of coordinated, dynamic chemical interactions that lower barriers to substrate rearrangements approaching bond formation. Differential scanning fluorimetry combined with mutational analysis and enzyme kinetics further confirm that phloretin binds to the enzyme active site and that it acts as a competitive inhibitor of CHI. Together these findings answer outstanding questions about the flexibility and dynamics of CHI catalysis, information that may be useful for future biosynthetic design and enzyme engineering goals. Overall, this work supports a catalytic model in which the CHI enzyme operates as a dynamic ensemble of structures necessary to facilitate catalytic substrate rearrangements.

biochemistry↗
bioRxiv2026

Structures of pUG-fold RNA bound to DNMT1 reveal a mechanism for RNA-mediated epigenetic regulation

Many chromatin-associated proteins have been found to bind RNA as a means of epigenetic regulation. Specifically, DNA methyltransferase 1 (DNMT1), which maintains cytosine methylation at CpG dinucleotides, is inhibited by RNA at transcribed DNA loci in cells. However, the mechanisms by which RNA binds DNMT1 and inhibits its activity remain unknown. Here, we determine a series of cryogenic electron microscopy (cryo-EM) structures of human DNMT1 bound to pUG-fold RNA, a non-canonical G-quadruplex previously observed to inhibit activity, revealing two distinct RNA-binding modes. The pUG-fold RNA binds the surface of DNMT1 in its autoinhibited conformation across a positively charged surface between the methyltransferase domain and the CXXC domain, and it binds directly in the active site of an open DNMT1 conformation. RNA binding is sterically incompatible with substrate DNA engagement in both states. Our 2.5 [A] structure captures the intricate network of hydrogen bonds and electrostatic interactions between amino acids in the methyltransferase domain and the tetrad layers of pUG-fold RNA. Metadynamics molecular dynamics simulations provide an orthogonal view of the conformational landscape of DNMT1, revealing the two distinct RNA-binding modes. Furthermore, our analysis of published DNMT1 RIP-seq and eCLIP-seq data confirms that DNMT1-interacting RNAs in cells exhibit a strong propensity to form non-canonical G-quadruplex RNA structures. Collectively, our study provides the first structural basis for pUG-fold RNA recognition by a protein and illustrates how cryo-EM and AI-based methods for protein and RNA structure prediction synergize to inform the mechanism of RNA-mediated regulation of DNMT1.

biochemistry↗