Cotranslational protein assembly imposes evolutionary constraints on homomeric proteins

Cotranslational protein folding can facilitate rapid formation of functional structures. However, it might also cause premature assembly of protein complexes, if two interacting nascent chains are in close proximity. By analyzing known protein structures, we show that homomeric protein contacts are enriched towards the C-termini of polypeptide chains across diverse proteomes. We hypothesize that this is the result of evolutionary constraints for folding to occur prior to assembly. Using high-throughput imaging of protein homomers in vivo in E. coli and engineered protein constructs with N- and C-terminal oligomerization domains, we show that, indeed, proteins with C-terminal homomeric interface residues consistently assemble more efficiently than those with N-terminal interface residues. Using in vivo, in vitro and in silico experiments, we identify features that govern successful assembly of homomers, which have implications for protein design and expression optimization

Altered co-translational processing plays a role in Huntington's pathogenesis-a hypothesis

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by the expansion of a CAG codon repeat region in the HTT gene's first exon that results in huntingtin protein aggregation and neuronal cell death. The development of therapeutic treatments for HD is hindered by the fact that while the etiology and symptoms of HD are understood, the molecular processes connecting this genotype to its phenotype remain unclear. Here, we propose the novel hypothesis that the perturbation of a co-translational process affects mutant huntingtin due to altered translation-elongation kinetics. These altered kinetics arise from the shift of a proline-induced translational pause site away from Htt's localization sequence due to the expansion of the CAG-repeat segment between the poly-proline and localization sequences. Motivation for this hypothesis comes from recent experiments in the field of protein biogenesis that illustrate the critical role that temporal coordination of co-translational processes plays in determining the function, localization, and fate of proteins in cells. We show that our hypothesis is consistent with various experimental observations concerning HD pathology, including the dependence of the age of symptom onset on CAG repeat number. Finally, we suggest three experiments to test our hypothesis

Altered co-translational processing plays a role in Huntington's pathogenesis-a hypothesis

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder caused by the expansion of a CAG codon repeat region in the HTT gene's first exon that results in huntingtin protein aggregation and neuronal cell death. The development of therapeutic treatments for HD is hindered by the fact that while the etiology and symptoms of HD are understood, the molecular processes connecting this genotype to its phenotype remain unclear. Here, we propose the novel hypothesis that the perturbation of a co-translational process affects mutant huntingtin due to altered translation-elongation kinetics. These altered kinetics arise from the shift of a proline-induced translational pause site away from Htt's localization sequence due to the expansion of the CAG-repeat segment between the poly-proline and localization sequences. Motivation for this hypothesis comes from recent experiments in the field of protein biogenesis that illustrate the critical role that temporal coordination of co-translational processes plays in determining the function, localization, and fate of proteins in cells. We show that our hypothesis is consistent with various experimental observations concerning HD pathology, including the dependence of the age of symptom onset on CAG repeat number. Finally, we suggest three experiments to test our hypothesis

Structural origins of FRET-observed nascent chain compaction on the ribosome

A fluorescence signal arising from a Förster resonance energy transfer process was used to monitor conformational changes of a domain within the E. coli protein HemK during its synthesis by the ribosome. An increase in fluorescence was observed to begin 10 s after translation was initiated, indicating the domain became more compact in size. Since fluorescence only reports a single value at each time point it contains very little information about the structural ensemble that gives rise to it. Here, we supplement this experimental information with coarse-grained simulations that describe protein conformations and transitions at a spatial resolution of 3.8 Å. We use these simulations to test three hypotheses that might explain the cause of domain compaction: (1) that poor solvent quality conditions drive the unfolded state to compact, (2) that a change in the dimension of the space the domain occupies upon moving outside the exit tunnel causes compaction, or (3) that domain folding causes compaction. We find that domain folding and dimensional collapse are both consistent with the experimental data, while poor-solvent collapse is inconsistent. We identify alternative dye labeling positions on HemK that upon fluorescence can differentiate between the domain folding and dimensional collapse mechanisms. Partial folding of domains has been observed in C-terminally truncated forms of proteins. Therefore, it is likely that the experimentally observed compact state is a partially folded intermediate consisting, according to our simulations, of the first three helices of the HemK N-terminal domain adopting a native, tertiary configuration. With these simulations we also identify the possible cotranslational folding pathways of HemK

Current structure predictors are not learning the physics of protein folding

Summary Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/</p

Current structure predictors are not learning the physics of protein folding

Summary: Motivation Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/ Supplementary information Supplementary data are available at Bioinformatics online

Domain topology, stability, and translation speed determine mechanical force generation on the ribosome

The concomitant folding of a nascent protein domain with its synthesis can generate mechanical forces that act on the ribosome and alter translation speed. Such changes in speed can affect the structure and function of the newly synthesized protein as well as cellular phenotype. The domain properties that govern force generation have yet to be identified and understood, and the influence of translation speed is unknown because all reported measurements have been carried out on arrested ribosomes. Here, using coarse-grained molecular simulations and statistical mechanical modeling of protein synthesis, we demonstrate that force generation is determined by a domain’s stability and topology, as well as translation speed. The statistical mechanical models we create predict how force profiles depend on these properties. These results indicate that force measurements on arrested ribosomes will not always accurately reflect what happens in a cell, especially for slow-folding domains, and suggest the possibility that certain domain properties may be enriched or depleted across the structural proteome of organisms through evolutionary selection pressures to modulate protein synthesis speed and posttranslational protein behavior

Does SARS-CoV-2 bind to human ACE2 stronger than SARS-CoV?

The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, was declared a pandemic by the World Health Organization in March 2020. Genetically, SARS-CoV-2 is closely related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries from 2002 to 2003. Given the significant morbidity and mortality rate, the current pandemic poses a danger to all of humanity, prompting us to understand the activity of SARS-CoV-2 at the atomic level. Experimental studies have revealed that spike proteins of both SARS-CoV-2 and SARS-CoV bind to angiotensin-converting enzyme 2 (ACE2) before entering the cell for replication. However, the binding affinities reported by different groups seem to contradict each other. Wrapp et al. (Science2020, 367, 1260–1263) showed that the spike protein of SARS-CoV-2 binds to the ACE2 peptidase domain (ACE2-PD) more strongly than does SARS-CoV, and this fact may be associated with a greater severity of the new virus. However, Walls et al. (Cell2020, 181, 281–292) reported that SARS-CoV-2 exhibits a higher binding affinity, but the difference between the two variants is relatively small. To understand the binding mechanism and experimental results, we investigated how the receptor binding domain (RBD) of SARS-CoV (SARS-CoV-RBD) and SARS-CoV-2 (SARS-CoV-2-RBD) interacts with a human ACE2-PD using molecular modeling. We applied a coarse-grained model to calculate the dissociation constant and found that SARS-CoV-2 displays a 2-fold higher binding affinity. Using steered all-atom molecular dynamics simulations, we demonstrate that, like a coarse-grained simulation, SARS-CoV-2-RBD was associated with ACE2-PD more strongly than was SARS-CoV-RBD, as evidenced by a higher rupture force and larger pulling work. We show that the binding affinity of both viruses to ACE2 is driven by electrostatic interactions

Electrostatic interactions govern extreme nascent protein ejection times from ribosomes and can delay ribosome recycling

The ejection of nascent proteins out of the ribosome exit tunnel, after their covalent bond to transfer-RNA has been broken, has not been experimentally studied due to challenges in sample preparation. Here, we investigate this process using a combination of multiscale modeling, ribosome profiling, and gene ontology analyses. Simulating the ejection of a representative set of 122 E. coli proteins we find a greater than 1000-fold variation in ejection times. Nascent proteins enriched in negatively charged residues near their C-terminus eject the fastest, while nascent chains enriched in positively charged residues tend to eject much more slowly. More work is required to pull slowly ejecting proteins out of the exit tunnel than quickly ejecting proteins, according to all-atom simulations. An energetic decomposition reveals, for slowly ejecting proteins, that this is due to the strong attractive electrostatic interactions between the nascent chain and the negatively charged ribosomal-RNA lining the exit tunnel, and for quickly ejecting proteins, it is due to their repulsive electrostatic interactions with the exit tunnel. Ribosome profiling data from E. coli reveals that the presence of slowly ejecting sequences correlates with ribosomes spending more time at stop codons, indicating that the ejection process might delay ribosome recycling. Proteins that have the highest positive charge density at their C-terminus are overwhelmingly ribosomal proteins, suggesting the possibility that this sequence feature may aid in the cotranslational assembly of ribosomes by delaying the release of nascent ribosomal proteins into the cytosol. Thus, nascent chain ejection times from the ribosome can vary greatly between proteins due to differential electrostatic interactions, can influence ribosome recycling, and could be particularly relevant to the synthesis and cotranslational behavior of some proteins