Biology subjects

Courbet, A.

Publications and source records attributed to Courbet, A..

13 recordsLinked to original sources

bioRxiv2025

Single-molecule observation of multi-scale dynamic heterogeneity in the molecular bearing of the bacterial flagellum

The bacterial flagellar motor (BFM) is a protein-based rotary machine that drives bacterial motility. It comprises a stator anchored to the cell wall and a rotor in the cytoplasmic membrane, linked via the flagellar rod to the extracellular hook and filament. We describe high-speed polarization microscopy of gold nanorods attached to the hook, allowing unprecedented continuous recording of single BFMs with [~]0.5{degrees} resolution at 250 kHz for tens of minutes. The angular distribution of actively rotating motors exhibited multiple periodicities that are consistent with structure of the bearing between the rod and LP-ring. We observed passive diffusional rotation of this bearing, revealing complex dynamics at odds with the expected classical Ornstein-Uhlenbeck process in a smooth potential. Resolved transitions between the main 26-fold bearing states exhibited highly non-Poissonian kinetics spanning four orders of magnitude in timescale. Using the average transition times, we estimated the interaction barrier in the bearing to be close to 13 kBT, suggesting that the previously estimated torque generated by a single stator unit is just sufficient to overcome this barrier. At sub-millisecond timescales we observed anomalous ultra-slow diffusion typically associated with multi-scale disordered systems, despite the ordered crystalline atomic structure of the bearing revealed by cryo-Electron Microscopy. Over longer periods, we observed dynamic shifts in the preferred angular positions, indicating that the motors potential energy landscape evolves over time. We speculate that these newly discovered rich dynamics may be a universal feature of protein machines.

biophysics↗

bioRxiv2025

Solubilization of Membrane Proteins using designed protein WRAPS

The development of therapies and vaccines targeting integral membrane proteins has been complicated by their extensive hydrophobic surfaces, which can make production and structural characterization difficult. Here we describe a general deep learning-based design approach for solubilizing native membrane proteins while preserving their sequence, fold, and function using genetically encoded de novo protein WRAPs (Water-soluble RFdiffused Amphipathic Proteins) that surround the lipid-interacting hydrophobic surfaces, rendering them stable and water-soluble without the need for detergents. We design WRAPs for both beta-barrel outer membrane and helical multi-pass transmembrane proteins, and show that the solubilized proteins retain the binding and enzymatic functions of the native targets with enhanced stability. Syphilis vaccine development has been hindered by difficulties in characterizing and producing the outer membrane protein antigens; we generated soluble versions of four Treponema pallidum outer membrane beta barrels which are potential syphilis vaccine antigens. A 4.0 [A] cryo-EM map of WRAPed TP0698 is closely consistent with the design model. WRAPs should be broadly useful for facilitating biochemical and structural characterization of integral membrane proteins, enabling therapeutic discovery by screening against purified soluble targets, and generating antigenically intact immunogens for vaccine development.

biochemistry↗

bioRxiv2024

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

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

biochemistry↗

bioRxiv2023

Blueprinting expandable nanomaterials with standardized protein building blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures1. Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

biochemistry↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗

bioRxiv2022

Hallucination of closed repeat proteins containing central pockets

In pseudocyclic proteins such as TIM barrels, {beta} barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity which can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in E. coli are consistent with the design models, and two crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small molecule binders or enzymes.

molecular biology↗

bioRxiv2022

Hallucinating protein assemblies

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 [A]), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summaryDeep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

biophysics↗

bioRxiv2022

Robust deep learning based protein sequence design using ProteinMPNN

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summaryA deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

biophysics↗

bioRxiv2021

Computational design of nanoscale rotational mechanics in de novo protein assemblies

Natural nanomachines like the F1/F0-ATPase contain protein components that undergo rotation relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein rotary machinery from designed axle and ring components. Using cryoelectron microscopy, we find that axle-ring systems assemble as designed and populate diverse rotational states depending on symmetry match or mismatch and the designed interface energy landscape. These mechanical systems with internal rotational degrees of freedom are a step towards the systematic design of genetically encodable nanomachines. One-Sentence SummaryComputationally designed self-assembling protein rotary machines sample internal degrees of freedom sculpted within the energy landscape.

synthetic biology↗

bioRxiv2021

Reconfigurable asymmetric protein assemblies through implicit negative design

Asymmetric multi-protein complexes that undergo subunit exchange play central roles in biology, but present a challenge for protein design. The individual components must contain interfaces enabling reversible addition to and dissociation from the complex, but be stable and well behaved in isolation. Here we employ a set of implicit negative design principles to generate beta sheet mediated heterodimers which enable the generation of a wide variety of structurally well defined asymmetric assemblies. Crystal structures of the heterodimers are very close to the design models, and unlike previously designed orthogonal heterodimer sets, the subunits are stable, folded and monomeric in isolation and rapidly assemble upon mixing. Rigid fusion of individual heterodimer halves to repeat proteins yields central assembly hubs that can bind two or three different proteins across different interfaces. We use these connectors to assemble linearly arranged hetero-oligomers with up to 6 unique components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub, and demonstrate such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

synthetic biology↗

bioRxiv2020

Hierarchical design of multi-scale protein complexes by combinatorial assembly of oligomeric helical bundle and repeat protein building blocks

A goal of de novo protein design is to develop a systematic and robust approach to generating complex nanomaterials from stable building blocks. Due to their structural regularity and simplicity, a wide range of monomeric repeat proteins and oligomeric helical bundle structures have been designed and characterized. Here we describe a stepwise hierarchical approach to building up multi-component symmetric protein assemblies using these structures. We first connect designed helical repeat proteins (DHRs) to designed helical bundle proteins (HBs) to generate a large library of heterodimeric and homooligomeric building blocks; the latter have cyclic symmetries ranging from C2 to C6. All of the building blocks have repeat proteins with accessible termini, which we take advantage of in a second round of architecture guided rigid helical fusion (WORMS) to generate larger symmetric assemblies including C3 and C5 cyclic and D2 dihedral rings, a tetrahedral cage, and a 120 subunit icosahedral cage. Characterization of the structures by small angle x-ray scattering, x-ray crystallography, and cryo-electron microscopy demonstrates that the hierarchical design approach can accurately and robustly generate a wide range of macromolecular assemblies; with a diameter of 43nm, the icosahedral nanocage is the largest structurally validated designed cage to date. The computational methods and building block sets described here provide a very general route to new de novo designed symmetric protein nanomaterials.

biochemistry↗

bioRxiv2020

Generation of ordered protein assemblies using rigid three-body fusion

Protein nanomaterial design is an emerging discipline with applications in medicine and beyond. A longstanding design approach uses genetic fusion to join protein homo-oligomer subunits via -helical linkers to form more complex symmetric assemblies, but this method is hampered by linker flexibility and a dearth of geometric solutions. Here, we describe a general computational method that performs rigid three-body fusion of homo-oligomer and spacer building blocks to generate user-defined architectures, while at the same time significantly increasing the number of geometric solutions over typical symmetric fusion. The fusion junctions are then optimized using Rosetta to minimize flexibility. We apply this method to design and test 92 dihedral symmetric protein assemblies from a set of designed homo-dimers and repeat protein building blocks. Experimental validation by native mass spectrometry, small angle X-ray scattering, and negative-stain single-particle electron microscopy confirms the assembly states for 11 designs. Most of these assemblies are constructed from DARPins (designed ankyrin repeat proteins), anchored on one end by -helical fusion and on the other by a designed homo-dimer interface, and we explored their use for cryo-EM structure determination by incorporating DARPin variants selected to bind targets of interest. Although the target resolution was limited by preferred orientation effects, small scaffold size, and the low-order symmetry of these dihedral scaffolds, we found that the dual anchoring strategy reduced the flexibility of the target-DARPIN complex with respect to the overall assembly, suggesting that multipoint anchoring of binding domains could contribute to cryo-EM structure determination of small proteins.

synthetic biology↗

bioRxiv2020

Self-assembly based post-translational protein oscillators

Recent advances in synthetic post-translational protein circuits are significantly impacting the landscape of biomimicry engineering. However, designing sustained dynamic phenomena in these circuits remains an outstanding challenge. Inspired by the KaiABC system regulating the circadian clock in cyanobacteria, we develop two experimentally realizable post-translational oscillators. The oscillators rely on a small number of components interacting only through reversible binding and phosphorylation/dephosphorylation reactions.

systems biology↗