Coronavirus relevancy for human health has surged over the past 20 years as they have a propensity for spillover into humans from animal reservoirs resulting in pandemics such as COVID-19. The diversity within the Coronavirinae subfamily and high infection frequency in animal species worldwide creates a looming threat that calls for research across all genera within the Coronavirinae subfamily. We sought to contribute to the limited structural knowledge within the Gammacoronavirus genera and determined the structure of the viral core replication-transcription complex (RTC) from Infectious Bronchitis Virus (IBV) using single-particle cryo-EM. Comparison between our IBV structure with published RTC structures from other Coronavirinae genera reveals structural differences across genera. Using in vitro biochemical assays, we characterized these differences and revealed their differing involvement in core RTC formation across different genera. Our findings highlight the value of cross-genera Coronavirinae studies, as they show genera specific features in coronavirus genome replication. A broader knowledge of coronavirus replication will better prepare us for future coronavirus spillovers.

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

BACKGROUND: The postsynaptic density is an elaborate protein network beneath the postsynaptic membrane involved in the molecular processes underlying learning and memory. The postsynaptic density is built up from the same major proteins but its exact composition and organization differs between synapses. Mutations perturbing protein: protein interactions generally occurring in this network might lead to effects specific for cell types or processes, the understanding of which can be especially challenging.
RESULTS: In this work we use systems biology-based modeling of protein complex distributions in a simplified set of major postsynaptic proteins to investigate the effect of a hypomorphic Shank mutation perturbing a single well-defined interaction. We use data sets with widely variable abundances of the constituent proteins. Our results suggest that the effect of the mutation is heavily dependent on the overall availability of all the protein components of the whole network and no trivial correspondence between the expression level of the directly affected proteins and overall complex distribution can be observed.
CONCLUSIONS: Our results stress the importance of context-dependent interpretation of mutations. Even the weakening of a generally occurring protein: protein interaction might have well-defined effects, and these can not easily be predicted based only on the abundance of the proteins directly affected. Our results provide insight on how cell-specific effects can be exerted by a mutation perturbing a generally occurring interaction even when the wider interaction network is largely similar.

The Rcs pathway is repressed by the inner membrane protein IgaA under non-stressed conditions. This repression is hypothesized to be relieved by the binding of the outer membrane-anchored RcsF to IgaA. However, the precise mechanism by which RcsF binding triggers the signaling remains unclear. Here, we present the 1.8 Å resolution crystal structure capturing the interaction between IgaA and RcsF. Our comparative structural analysis, examining both the bound and unbound states of the periplasmic domain of IgaA (IgaAp), highlights rotational flexibility within IgaAp. Conversely, the conformation of RcsF remains unchanged upon binding. Our in vivo and in vitro studies do not support the model of a stable complex involving RcsF, IgaAp, and RcsDp. Instead, we demonstrate that the elements beyond IgaAp play a role in the interaction between IgaA and RcsD. These findings collectively allow us to propose a potential mechanism for the signaling across the inner membrane through IgaA.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

The global outbreak of Mpox, caused by the monkeypox virus (MPXV), has attracted international attention and become another major infectious disease event after COVID-19. The mRNA cap N7 methyltransferase (RNMT) of MPXV methylates the N7 position of the added guanosine to the 5\'-cap structure of mRNAs and plays a vital role in evading host antiviral immunity. MPXV RNMT is composed of the large subunit E1 and the small subunit E12. How E1 and E12 of MPXV assembly remains unclear. Here, we report the crystal structures of E12, the MTase domain of E1 with E12 (E1CTD-E12) complex, and the E1CTD-E12-SAM ternary complex, revealing the detailed conformations of critical residues and the structural changes upon E12 binding to E1. Functional studies suggest that E1CTD N-terminal extension (Asp545-Arg562) and the small subunit E12 play an essential role in the binding process of SAM. Structural comparison of the AlphaFold2-predicted E1, E1CTD-E12 complex, and the homologous D1-D12 complex of vaccinia virus (VACV) indicates an allosteric activating effect of E1 in MPXV. Our findings provide the structural basis for the MTase activity stimulation of the E1-E12 complex and suggest a potential interface for screening the anti-poxvirus inhibitors.

In Saccharomyces cerevisiae (S. cerevisiae), Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homolog with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known about how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling of biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, supporting the structural model. Finally, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN.

N-terminal acetyltransferases (NAT) are the protein complexes that deposit the abundant N-terminal acetylation (Nt-Ac) on eukaryotic proteins, with seven human complexes currently identified. Despite the increasing recognition of their biological and clinical importance, NAT regulation remains elusive. In this study, we performed a bioinformatic investigation to identify transcriptional and post-transcriptional processes that could be involved in the regulation of human NAT complexes. First, co-expression analysis of independent transcriptomic datasets revealed divergent pathway associations for human NAT, which are potentially connected to their distinct cellular functions. One interesting connection uncovered was the coordinated regulation of the NatA and proteasomal genes in cancer and immune cells, confirmed by analysis of multiple datasets and in isolated primary T cells. Another distinctive association was of NAA40 (NatD) with DNA replication, in cancer and non-cancer settings. The link between NAA40 transcription and DNA replication is potentially mediated through E2F1, which we have experimentally shown to bind the promoter of this NAT. Second, the coupled examination of transcriptomic and proteomic datasets revealed a much greater intra-complex concordance of NAT subunits at the protein compared to the transcript level, indicating the predominance of post-transcriptional processes for achieving their coordination. In agreement with this concept, we also found that the effects of somatic copy number alterations affecting NAT genes are attenuated post-transcriptionally. In conclusion, this study provides novel insights into the regulation of human NAT complexes.

The identification of protein complexes from protein interaction networks is crucial in the understanding of protein function, cellular processes and disease mechanisms. Existing methods commonly rely on the assumption that protein interaction networks are highly reliable, yet in reality, there is considerable noise in the data. In addition, these methods fail to account for the regulatory roles of biomolecules during the formation of protein complexes, which is crucial for understanding the generation of protein interactions. To this end, we propose a SpatioTemporal constrained RNA-protein heterogeneous network for Protein Complex Identification (STRPCI). STRPCI first constructs a multiplex heterogeneous protein information network to capture deep semantic information by extracting spatiotemporal interaction patterns. Then, it utilizes a dual-view aggregator to aggregate heterogeneous neighbor information from different layers. Finally, through contrastive learning, STRPCI collaboratively optimizes the protein embedding representations under different spatiotemporal interaction patterns. Based on the protein embedding similarity, STRPCI reweights the protein interaction network and identifies protein complexes with core-attachment strategy. By considering the spatiotemporal constraints and biomolecular regulatory factors of protein interactions, STRPCI measures the tightness of interactions, thus mitigating the impact of noisy data on complex identification. Evaluation results on four real PPI networks demonstrate the effectiveness and strong biological significance of STRPCI. The source code implementation of STRPCI is available from https://github.com/LI-jasm/STRPCI.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.