When the JCVI-syn3.0 genome was designed and implemented in 2016 as the minimal genome of a free-living organism, approximately one-third of the 438 protein-coding genes had no known function. Subsequent refinement into JCVI-syn3A led to inclusion of 16 additional protein-coding genes, including several unknown functions, resulting in an improved growth phenotype. Here, we seek to unveil the biological roles and protein-protein interaction (PPI) networks for these poorly characterized proteins using state-of-the-art deep learning contact-assisted structure prediction, followed by structure-based annotation of functions and PPI predictions. Our pipeline is able to confidently assign functions for many previously unannotated proteins such as putative vitamin transporters, which suggest the importance of nutrient uptake even in a minimized genome. Remarkably, despite the artificial selection of genes in the minimal syn3 genome, our reconstructed PPI network still shows a power law distribution of node degrees typical of naturally evolved bacterial PPI networks. Making use of our framework for combined structure/function/interaction modeling, we are able to identify both fundamental aspects of network biology that are retained in a minimal proteome and additional essential functions not yet recognized among the poorly annotated components of the syn3.0 and syn3A proteomes.

SARS-CoV-2, a novel coronavirus causing overwhelming death and infection worldwide, has emerged as a pandemic. Compared to its predecessor SARS-CoV, SARS-CoV-2 is more infective for being highly contagious and exhibiting tighter binding with host angiotensin-converting enzyme 2 (hACE-2). The entry of the virus into host cells is mediated by the interaction of its spike protein with hACE-2. Thus, a peptide that has a resemblance to hACE-2 but can overpower the spike protein-hACE-2 interaction will be a potential therapeutic to contain this virus. The non-interacting residues in the receptor-binding domain of hACE-2 have been mutated to generate a library of 136 new peptides. Out of this library, docking and virtual screening discover seven peptides that can exert a stronger interaction with the spike protein than hACE-2. A peptide derived from simultaneous mutation of all the non-interacting residues of hACE-2 yields almost three-fold stronger interaction than hACE-2 and thus turns out here to be the best peptide inhibitor of the novel coronavirus. The binding of the best peptide inhibitor with the spike protein is explored further by molecular dynamics, free energy, and principal component analysis, which demonstrate its efficacy compared to hACE-2. The delivery of the screened inhibitors with nanocarriers like metal-organic frameworks will be worthy of further consideration to boost their efficacy.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a pandemic infection in March 2020. As of December 2020, two COVID-19 vaccines have been authorized for emergency use by the U.S. Food and Drug Administration, but there are no effective drugs to treat COVID-19, and pandemic mitigation efforts like physical distancing have had acute social and economic consequences. In this perspective, we discuss how the proteomic research community can leverage technologies and expertise to address the pandemic by investigating four key areas of study in SARS-CoV-2 biology. Specifically, we discuss how (1) mass spectrometry-based structural techniques can overcome limitations and complement traditional structural approaches to inform the dynamic structure of SARS-CoV-2 proteins, complexes, and virions; (2) virus-host protein-protein interaction mapping can identify the cellular machinery required for SARS-CoV-2 replication; (3) global protein abundance and post-translational modification profiling can characterize signaling pathways that are rewired during infection; and (4) proteomic technologies can aid in biomarker identification, diagnostics, and drug development in order to monitor COVID-19 pathology and investigate treatment strategies. Systems-level high-throughput capabilities of proteomic technologies can yield important insights into SARS-CoV-2 biology that are urgently needed during the pandemic, and more broadly, can inform coronavirus virology and host biology.

To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https://figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage.

The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance (1H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.

In the context of bacterial infections, it is imperative that physiological responses can be studied in an integrated manner, meaning a simultaneous analysis of both the host and the pathogen responses. To improve the sensitivity of detection, data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows in identifying and quantifying low-abundant proteins. Here, by making use of representative bacterial pathogen/host proteome samples, we report an optimized hybrid library generation workflow for DIA mass spectrometry relying on the use of data-dependent and in silico-predicted spectral libraries. When compared to searching DDA experiment-specific libraries only, the use of hybrid libraries significantly improved peptide detection to an extent suggesting that infection-relevant host-pathogen conditions could be profiled in sufficient depth without the need of a priori bacterial pathogen enrichment when studying the bacterial proteome. Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD017904 and PXD017945.

Three-dimensional (3D) human induced pluripotent stem cell-derived engineered cardiac tissues (hiPSC-ECTs) have emerged as a promising alternative to two-dimensional hiPSC-cardiomyocyte monolayer systems because hiPSC-ECTs are a closer representation of endogenous cardiac tissues and more faithfully reflect the relevant cardiac pathophysiology. The ability to perform functional and molecular assessments using the same hiPSC-ECT construct would allow for more reliable correlation between observed functional performance and underlying molecular events, and thus is critically needed. Herein, for the first time, we have established an integrated method that permits sequential assessment of functional properties and top-down proteomics from the same single hiPSC-ECT construct. We quantitatively determined the differences in isometric twitch force and the sarcomeric proteoforms between two groups of hiPSC-ECTs that differed in the duration of time of 3D-ECT culture. Importantly, by using this integrated method we discovered a new and strong correlation between the measured contractile parameters and the phosphorylation levels of alpha-tropomyosin between the two groups of hiPSC-ECTs. The integration of functional assessments together with molecular characterization by top-down proteomics in the same hiPSC-ECT construct enables a holistic analysis of hiPSC-ECTs to accelerate their applications in disease modeling, cardiotoxicity, and drug discovery. Data are available via ProteomeXchange with identifier PXD022814.

Protein phosphorylation is a key regulatory mechanism involved in nearly every eukaryotic cellular process. Increasingly sensitive mass spectrometry approaches have identified hundreds of thousands of phosphorylation sites, but the functions of a vast majority of these sites remain unknown, with fewer than 5% of sites currently assigned a function. To increase our understanding of functional protein phosphorylation we developed an approach (phospho-DIFFRAC) for identifying the phosphorylation-dependence of protein assemblies in a systematic manner. A combination of nonspecific protein phosphatase treatment, size-exclusion chromatography, and mass spectrometry allowed us to identify changes in protein interactions after the removal of phosphate modifications. With this approach we were able to identify 316 proteins involved in phosphorylation-sensitive interactions. We recovered known phosphorylation-dependent interactors such as the FACT complex and spliceosome, as well as identified novel interactions such as the tripeptidyl peptidase TPP2 and the supraspliceosome component ZRANB2. More generally, we find phosphorylation-dependent interactors to be strongly enriched for RNA-binding proteins, providing new insight into the role of phosphorylation in RNA binding. By searching directly for phosphorylated amino acid residues in mass spectrometry data, we identified the likely regulatory phosphosites on ZRANB2 and FACT complex subunit SSRP1. This study provides both a method and resource for obtaining a better understanding of the role of phosphorylation in native macromolecular assemblies. All mass spectrometry data are available through PRIDE (accession #PXD021422).

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.