SARS-CoV-2 breakthrough infection induces rapid memory and de novo T cell responses.

Although the protective role of neutralizing antibodies against COVID-19 is well established, questions remain about the relative importance of cellular immunity. Using 6 pMHC multimers in a cohort with early and frequent sampling, we define the phenotype and kinetics of recalled and primary T cell responses following Delta or Omicron breakthrough infection in previously vaccinated individuals. Recall of spike-specific CD4+ T cells was rapid, with cellular proliferation and extensive activation evident as early as 1 day post symptom onset. Similarly, spike-specific CD8+ T cells were rapidly activated but showed variable degrees of expansion. The frequency of activated SARS-CoV-2-specific CD8+ T cells at baseline and peak inversely correlated with peak SARS-CoV-2 RNA levels in nasal swabs and accelerated viral clearance. Our study demonstrates that a rapid and extensive recall of memory T cell populations occurs early after breakthrough infection and suggests that CD8+ T cells contribute to the control of viral replication in breakthrough SARS-CoV-2 infections.

Spheromers reveal robust T cell responses to the Pfizer/BioNTech vaccine and attenuated peripheral CD8+ T cell responses post SARS-CoV-2 infection.

T cells are a critical component of the response to SARS-CoV-2, but their kinetics after infection and vaccination are insufficiently understood. Using "spheromer" peptide-MHC multimer reagents, we analyzed healthy subjects receiving two doses of the Pfizer/BioNTech BNT162b2 vaccine. Vaccination resulted in robust spike-specific T cell responses for the dominant CD4+ (HLA-DRB1∗15:01/S191) and CD8+ (HLA-A∗02/S691) T cell epitopes. Antigen-specific CD4+ and CD8+ T cell responses were asynchronous, with the peak CD4+ T cell responses occurring 1 week post the second vaccination (boost), whereas CD8+ T cells peaked 2 weeks later. These peripheral T cell responses were elevated compared with COVID-19 patients. We also found that previous SARS-CoV-2 infection resulted in decreased CD8+ T cell activation and expansion, suggesting that previous infection can influence the T cell response to vaccination.

Structure, dynamics, assembly, and evolution of protein complexes.

The assembly of individual proteins into functional complexes is fundamental to nearly all biological processes. In recent decades, many thousands of homomeric and heteromeric protein complex structures have been determined, greatly improving our understanding of the fundamental principles that control symmetric and asymmetric quaternary structure organization. Furthermore, our conception of protein complexes has moved beyond static representations to include dynamic aspects of quaternary structure, including conformational changes upon binding, multistep ordered assembly pathways, and structural fluctuations occurring within fully assembled complexes. Finally, major advances have been made in our understanding of protein complex evolution, both in reconstructing evolutionary histories of specific complexes and in elucidating general mechanisms that explain how quaternary structure tends to evolve. The evolution of quaternary structure occurs via changes in self-assembly state or through the gain or loss of protein subunits, and these processes can be driven by both adaptive and nonadaptive influences.

Characterization of the SF3B1-SUGP1 interface reveals how numerous cancer mutations cause mRNA missplicing.

The spliceosomal gene SF3B1 is frequently mutated in cancer. While it is known that SF3B1 hotspot mutations lead to loss of splicing factor SUGP1 from spliceosomes, the cancer-relevant SF3B1-SUGP1 interaction has not been characterized. To address this issue, we show by structural modeling that two regions flanking the SUGP1 G-patch make numerous contacts with the region of SF3B1 harboring hotspot mutations. Experiments confirmed that all the cancer-associated mutations in these regions, as well as mutations affecting other residues in the SF3B1-SUGP1 interface, not only weaken or disrupt the interaction but also alter splicing similarly to SF3B1 cancer mutations. Finally, structural modeling of a trimeric protein complex reveals that the SF3B1-SUGP1 interaction "loops out" the G-patch for interaction with the helicase DHX15. Our study thus provides an unprecedented molecular view of a protein complex essential for accurate splicing and also reveals that numerous cancer-associated mutations disrupt the critical SF3B1-SUGP1 interaction.

AI-assisted proofreading of RNA splicing.

RNA helicases orchestrate proofreading mechanisms that facilitate accurate intron removal from pre-mRNAs. How these activities are recruited to spliceosome/pre-mRNA complexes remains poorly understood. In this issue of Genes & Development, Zhang and colleagues (pp. 968-983) combine biochemical experiments with AI-based structure prediction methods to generate a model for the interaction between SF3B1, a core splicing factor essential for the recognition of the intron branchpoint, and SUGP1, a protein that bridges SF3B1 with the helicase DHX15. Interaction with SF3B1 exposes the G-patch domain of SUGP1, facilitating binding to and activation of DHX15. The model can explain the activation of cryptic 3' splice sites induced by mutations in SF3B1 or SUGP1 frequently found in cancer.

PD-1-Expressing SARS-CoV-2-Specific CD8+ T Cells Are Not Exhausted, but Functional in Patients with COVID-19.

Memory T cell responses have been demonstrated in COVID-19 convalescents, but ex vivo phenotypes of SARS-CoV-2-specific T cells have been unclear. We detected SARS-CoV-2-specific CD8+ T cells by MHC class I multimer staining and examined their phenotypes and functions in acute and convalescent COVID-19. Multimer+ cells exhibited early differentiated effector-memory phenotypes in the early convalescent phase. The frequency of stem-like memory cells was increased among multimer+ cells in the late convalescent phase. Cytokine secretion assays combined with MHC class I multimer staining revealed that the proportion of interferon-γ (IFN-γ)-producing cells was significantly lower among SARS-CoV-2-specific CD8+ T cells than those specific to influenza A virus. Importantly, the proportion of IFN-γ-producing cells was higher in PD-1+ cells than PD-1- cells among multimer+ cells, indicating that PD-1-expressing, SARS-CoV-2-specific CD8+ T cells are not exhausted, but functional. Our current findings provide information for understanding of SARS-CoV-2-specific CD8+ T cells elicited by infection or vaccination.

Substrate-dependent effects of quaternary structure on RNase E activity.

RNase E is an essential, multifunctional ribonuclease encoded in E. coli by the rne gene. Structural analysis indicates that the ribonucleolytic activity of this enzyme is conferred by rne-encoded polypeptide chains that (1) dimerize to form a catalytic site at the protein-protein interface, and (2) multimerize further to generate a tetrameric quaternary structure consisting of two dimerized Rne-peptide chains. We identify here a mutation in the Rne protein's catalytic region (E429G), as well as a bacterial cell wall peptidoglycan hydrolase (Amidase C [AmiC]), that selectively affect the specific activity of the RNase E enzyme on long RNA substrates, but not on short synthetic oligonucleotides, by enhancing enzyme multimerization. Unlike the increase in specific activity that accompanies concentration-induced multimerization, enhanced multimerization associated with either the E429G mutation or interaction of the Rne protein with AmiC is independent of the substrate's 5' terminus phosphorylation state. Our findings reveal a previously unsuspected substrate length-dependent regulatory role for RNase E quaternary structure and identify cis-acting and trans-acting factors that mediate such regulation.

Using AlphaFold Multimer to discover interkingdom protein-protein interactions.

Structural prediction by artificial intelligence can be powerful new instruments to discover novel protein-protein interactions, but the community still grapples with the implementation, opportunities and limitations. Here, we discuss and re-analyse our in silico screen for novel pathogen-secreted inhibitors of immune hydrolases to illustrate the power and limitations of structural predictions. We discuss strategies of curating sequences, including controls, and reusing sequence alignments and highlight important limitations caused by different platforms, sequence depth and computing times. We hope these experiences will support similar interactomic screens by the research community.

Improved the heterodimer protein complex prediction with protein language models.

AlphaFold-Multimer has greatly improved the protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e. interologs) of the complex under prediction. Here we propose a novel method, ESMPair, that can identify interologs of a complex using protein language models. We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). By systematically analyzing the impact factors of our algorithm we find that the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.

Assessing protein model quality based on deep graph coupled networks using protein language model.

Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality is inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared with the CASP15 residue-level interface evaluation methods among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers (https://www.cameo3d.org/quality-estimation). GraphCPLMQA also outperforms state-of-the-art methods on 19, 035 models in CASP13 and CASP14 monomer test set.

Identification of type VI secretion system effector-immunity pairs using structural bioinformatics.

The type VI secretion system (T6SS) is an important mediator of microbe-microbe and microbe-host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein-protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.

Protein complexes in cells by AI-assisted structural proteomics.

Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Alpha-synuclein dynamics bridge Type-I Interferon response and SARS-CoV-2 replication in peripheral cells.

BACKGROUND: Increasing evidence suggests a double-faceted role of alpha-synuclein (α-syn) following infection by a variety of viruses, including SARS-CoV-2. Although α-syn accumulation is known to contribute to cell toxicity and the development and/or exacerbation of neuropathological manifestations, it is also a key to sustaining anti-viral innate immunity. Consistently with α-syn aggregation as a hallmark of Parkinson's disease, most studies investigating the biological function of α-syn focused on neural cells, while reports on the role of α-syn in periphery are limited, especially in SARS-CoV-2 infection. RESULTS: Results herein obtained by real time qPCR, immunofluorescence and western blot indicate that α-syn upregulation in peripheral cells occurs as a Type-I Interferon (IFN)-related response against SARS-CoV-2 infection. Noteworthy, this effect mostly involves α-syn multimers, and the dynamic α-syn multimer:monomer ratio. Administration of excess α-syn monomers promoted SARS-CoV-2 replication along with downregulation of IFN-Stimulated Genes (ISGs) in epithelial lung cells, which was associated with reduced α-syn multimers and α-syn multimer:monomer ratio. These effects were prevented by combined administration of IFN-ß, which hindered virus replication and upregulated ISGs, meanwhile increasing both α-syn multimers and α-syn multimer:monomer ratio in the absence of cell toxicity. Finally, in endothelial cells displaying abortive SARS-CoV-2 replication, α-syn multimers, and multimer:monomer ratio were not reduced following exposure to the virus and exogenous α-syn, suggesting that only productive viral infection impairs α-syn multimerization and multimer:monomer equilibrium. CONCLUSIONS: Our study provides novel insights into the biology of α-syn, showing that its dynamic conformations are implicated in the innate immune response against SARS-CoV-2 infection in peripheral cells. In particular, our results suggest that promotion of non-toxic α-syn multimers likely occurs as a Type-I IFN-related biological response which partakes in the suppression of viral replication. Further studies are needed to replicate our findings in neuronal cells as well as animal models, and to ascertain the nature of such α-syn conformations.

Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.

We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.

Improved multimer prediction using massive sampling with AlphaFold in CASP15.

AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: http://wallnerlab.org/AFsample/.

Prediction of protein assemblies by structure sampling followed by interface-focused scoring.

Proteins often function as part of permanent or transient multimeric complexes, and understanding function of these assemblies requires knowledge of their three-dimensional structures. While the ability of AlphaFold to predict structures of individual proteins with unprecedented accuracy has revolutionized structural biology, modeling structures of protein assemblies remains challenging. To address this challenge, we developed a protocol for predicting structures of protein complexes involving model sampling followed by scoring focused on the subunit-subunit interaction interface. In this protocol, we diversified AlphaFold models by varying construction and pairing of multiple sequence alignments as well as increasing the number of recycles. In cases when AlphaFold failed to assemble a full protein complex or produced unreliable results, additional diverse models were constructed by docking of monomers or subcomplexes. All the models were then scored using a newly developed method, VoroIF-jury, which relies only on structural information. Notably, VoroIF-jury is independent of AlphaFold self-assessment scores and therefore can be used to rank models originating from different structure prediction methods. We tested our protocol in CASP15 and obtained top results, significantly outperforming the standard AlphaFold-Multimer pipeline. Analysis of our results showed that the accuracy of our assembly models was capped mainly by structure sampling rather than model scoring. This observation suggests that better sampling, especially for the antibody-antigen complexes, may lead to further improvement. Our protocol is expected to be useful for modeling and/or scoring protein assemblies.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

The effects of KTKEGV repeat motif and intervening ATVA sequence on α-synuclein solubility and assembly.

Alpha-synuclein (αS), the key protein in Parkinson's disease, is typically described as an intrinsically disordered protein. Consistent with this notion, several context-dependent folding states may coexist in neurons. Unfolded soluble monomers, helical monomers at membranes and helical multimers (soluble or at membranes) have all been reported and may be in an equilibrium with each other. We previously found that αS can be stabilized in its membrane-associated monomeric form by genetically increasing the hydrophobicity of the membrane-embedded half of the αS helix. αS amphipathic helix formation at membranes is governed by up to nine 11-amino acid repeats with the core motif KTKEGV. However, this repeat is only imperfectly conserved; for example, it consists of KAKEGV in repeat #1, KTKEQV in repeat #5, and AVVTGV in the poorly conserved repeat #6. Here we explored the effect of perfecting the αS core repeat to nine times KTKEGV ("9KV") and found by sequential protein extraction that this engineered mutant accumulates in the cytosolic phase of neural cells. Intact-cell cross-linking trapped a part of the cytosolic portion at multimeric positions (30, 60, 80, 100 kDa). Thus, compared to wild-type αS, αS 9KV seems less prone to populating the membrane-associated monomeric form. Removing the "ATVA" intervening amino-acid sequence between repeats 4 and 5 slightly increased cytosolic localization while adding "ATVA" in between all repeats 1-8 caused αS to be trapped as a monomer in membrane fractions. Our results contribute to an ongoing debate on the dynamic structure of αS, highlighting that wild-type αS is unlikely to be fully multimeric/monomeric or fully cytosolic/membrane-associated in cells, but protein engineering can create αS variants that preferentially adopt a certain state. Overall, the imperfect nature of the KTKEGV repeat motifs and the presence of ATVA in between repeats 4 and 5 seem to prevent a strong cytosolic localization of αS and thus play a major role in the protein's ability to dynamically populate cytosolic vs. membrane-associated and monomeric vs. multimeric states.

Deep learning structural insights into heterotrimeric alternatively spliced P2X7 receptors.

P2X7 receptors (P2X7Rs) are membrane-bound ATP-gated ion channels that are composed of three subunits. Different subunit structures may be expressed due to alternative splicing of the P2RX7 gene, altering the receptor's function when combined with the wild-type P2X7A subunits. In this study, the application of the deep-learning method, AlphaFold2-Multimer (AF2M), for the generation of trimeric P2X7Rs was validated by comparing an AF2M-generated rat wild-type P2X7A receptor with a structure determined by cryogenic electron microscopy (cryo-EM) (Protein Data Bank Identification: 6U9V). The results suggested AF2M could firstly, accurately predict the structures of P2X7Rs and secondly, accurately identify the highest quality model through the ranking system. Subsequently, AF2M was used to generate models of heterotrimeric alternatively spliced P2X7Rs consisting of one or two wild-type P2X7A subunits in combination with one or two P2X7B, P2X7E, P2X7J, and P2X7L splice variant subunits. The top-ranking models were deemed valid based on AF2M's confidence measures, stability in molecular dynamics simulations, and consistent flexibility of the conserved regions between the models. The structure of the heterotrimeric receptors, which were missing key residues in the ATP binding sites and carboxyl terminal domains (CTDs) compared to the wild-type receptor, help to explain their observed functions. Overall, the models produced in this study (available as supplementary material) unlock the possibility of structure-based studies into the heterotrimeric P2X7Rs.

Branched short elastin-like peptides with temperature responsiveness obtained by EDTA-mediated multimerization.

Elastin-like peptides (ELPs) exhibit a reversible phase transition, known as coacervation, triggered by temperature changes. This property makes them useful as stimuli-responsive molecular materials for various applications. Among ELPs, short peptide chain lengths have some advantages over long peptide chain lengths because short ELPs can be easily obtained by chemical synthesis, allowing the use of various amino acids, including D-type and unnatural amino acids, at any position in the sequence. Moreover, the incorporated amino acids readily affect the temperature-responsive behavior of ELPs. However, to be utilized in various applications, it is necessary to develop short ELPs and to investigate their temperature-responsive properties. To obtain further insights into the temperature-responsive behavior of the short ELPs, we investigated branched short ELP analogs composed of (FPGVG)n chains (n = 1 or 2, abbreviated as F1 and F2, respectively). We synthesized multimers composed of four F1 chains or two to four F2 chains using ethylenediaminetetraacetic acid (EDTA) as a central component of multimerization. Our results show that the multimers obtained exhibited coacervation in aqueous solutions whereas linear F1 or F2 did not. Furthermore, the structural features of the obtained multimers were the same as those of linear (FPGVG)4 . In this study, we demonstrated that molecules capable of coacervation can be obtained by multimerization of F1 or F2. The temperature-responsive molecules obtained using short ELPs make it possible to use them as easy-to-synthesize peptide tags to confer temperature responsiveness to various molecules, which will aid the development of temperature-responsive biomaterials with a wide variety of functions.