Spatially resolved protein map of intact human cytomegalovirus virions

Harnessing the 14-3-3 protein-protein interaction network

Protein-protein interactions (PPIs) play a critical role in cellular signaling and represent interesting targets for therapeutic intervention. 14-3-3 proteins integrate many signaling targets via PPIs and are frequently implicated in disease, making them intriguing drug targets. Here, we review the recent advances in the 14-3-3 field. It will discuss the roles 14-3-3 proteins play within the cell, elucidation of their expansive interactome, and the complex mechanisms that underpin their function. In addition, the review will discuss significant advances in the development of molecular glues that target 14-3-3 PPIs. In particular, it will focus on novel drug discovery and development methodologies that have delivered selective, potent, and drug-like molecules that could open new avenues for the development of precision molecular tools and medicines.

Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topologies

The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome. Biochemical and imaging-based follow-up studies confirm that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. We also validate the CLASP concept in synaptic vesicles, demonstrating its applicability to different sub-cellular compartments. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, and establishes a method for concomitant profiling of sub-organelle and membrane proteomes.

Crystal structure of the tegument protein UL82 (pp71) from human cytomegalovirus

Human cytomegalovirus (HCMV) is an opportunistic pathogen that infects a majority of the world population. It may cause severe disease in immunocompromised people and lead to pregnancy loss or grave disabilities of the fetus upon congenital infection. For effective replication and lifelong persistence in its host, HCMV relies on diverse functions of its tegument protein UL82, also known as pp71. Up to now, little is known about the molecular mechanisms underlying the multiple functions of this crucial viral protein. Here, we describe the X-ray structure of full-length UL82 to a resolution of 2.7 Å. A single polypeptide chain of 559 amino acids mainly folds into three ß-barrels. We show that UL82 forms a dimer in the crystal as well as in solution. We identify point mutations that disturb the dimerization interface and show that the mutant protein is monomeric in solution and upon expression in human cells. On the basis of the three-dimensional structure, we identify structural homologs of UL82 from other herpesviruses and analyze whether their functions are preserved in UL82. We demonstrate that UL82, despite its structural homology to viral deoxyuridinetriphosphatases (dUTPases), does not possess dUTPase activity. Prompted by the structural homology of UL82 to the ORF10 protein of murine herpesvirus 68 (MHV68), which is known to interact with the RNA export factor ribonucleic acid export 1 (Rae1), we performed coimmunoprecipitations and demonstrated that UL82 indeed interacts with Rae1. This suggests that HCMV UL82 may play a role in mRNA export from the nucleus similar to ORF10 encoded by the gammaherpesviruses MHV68.

The incredible bulk: Human cytomegalovirus tegument architectures uncovered by AI-empowered cryo-EM

The compartmentalization of eukaryotic cells presents considerable challenges to the herpesvirus life cycle. The herpesvirus tegument, a bulky proteinaceous aggregate sandwiched between herpesviruses' capsid and envelope, is uniquely evolved to address these challenges, yet tegument structure and organization remain poorly characterized. We use deep-learning-enhanced cryogenic electron microscopy to investigate the tegument of human cytomegalovirus virions and noninfectious enveloped particles (NIEPs; a genome packaging-aborted state), revealing a portal-biased tegumentation scheme. We resolve atomic structures of portal vertex-associated tegument (PVAT) and identify multiple configurations of PVAT arising from layered reorganization of pUL77, pUL48 (large tegument protein), and pUL47 (inner tegument protein) assemblies. Analyses show that pUL77 seals the last-packaged viral genome end through electrostatic interactions, pUL77 and pUL48 harbor a head-linker-capsid-binding motif conducive to PVAT reconfiguration, and pUL47/48 dimers form 45-nm-long filaments extending from the portal vertex. These results provide a structural framework for understanding how herpesvirus tegument facilitates and evolves during processes spanning viral genome packaging to delivery.

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control

We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.