Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology

Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.

Streptococcus pyogenes Forms Serotype- and Local Environment-Dependent Interspecies Protein Complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surfaces of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes forms serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modeling of one of the M proteins, M28, revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by the binding of monomeric IgA and C4b-binding protein (C4BP). This indicates that an upsurge of C4BP in the local microenvironment due to damage to the mucosal membrane drives the binding of C4BP and monomeric IgA to M28. These results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat human immune surveillance during both mucosal and systemic infections.

IMPORTANCEStreptococcus pyogenes (group A Streptococcus [GAS]), is a human-specific Gram-positive bacterium. Each year, the bacterium affects 700 million people globally, leading to 160,000 deaths. The clinical manifestations of S. pyogenes are diverse, ranging from mild and common infections like tonsillitis and impetigo to life-threatening systemic conditions such as sepsis and necrotizing fasciitis. S. pyogenes expresses multiple virulence factors on its surface to localize and initiate infections in humans. Among all these expressed virulence factors, the M protein is the most important antigen. In this study, we perform an in-depth characterization of the human protein interactions formed around one of the foremost human pathogens. This strategy allowed us to decipher the protein interaction networks around different S. pyogenes strains on a global scale and to compare and visualize how such interactions are mediated by M proteins.

Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219

The reconstruction and analysis of the protein-protein interaction (PPI) network is a powerful approach to understand the complex biological and molecular functions in normal and disease states of the cell. The interactome of most organisms is largely unidentified except some model organisms. The current study focused on the construction of PPI network for the human pathogen Mycobacterium tuberculosis (MTB)-resistant strain XDR1219 using computational methods. In this work, a bioinformatics approach was employed to reveal potential drug targets. The pipeline adopted the combination of an extensive integrated network analysis that led to identify 22 key proteins involved in drug resistance, resistant metabolic pathways, virulence, pathogenesis and persistency of the infection. The MTB XDR1219 interactome consists of 11,383 non-redundant PPIs among 1499 proteins covering 38% of the entire MTB XDR1219 proteome. The overall quality of the network was assessed and topological parameters of the PPI were calculated. The predicted interactions were functionally annotated and their relevance was assessed with the functional similarity. The study attempts to present the interactome of previously unidentified MTB XDR1219 and revealed potential drug targets that can be further explored by scientific community.

Imaging Infection Across Scales of Size: From Whole Animals to Single Molecules

Infectious diseases are a leading cause of global morbidity and mortality, and the threat of infectious diseases to human health is steadily increasing as new diseases emerge, existing diseases reemerge, and antimicrobial resistance expands. The application of imaging technology to the study of infection biology has the potential to uncover new factors that are critical to the outcome of host-pathogen interactions and to lead to innovations in diagnosis and treatment of infectious diseases. This article reviews current and future opportunities for the application of imaging to the study of infectious diseases, with a particular focus on the power of imaging objects across a broad range of sizes to expand the utility of these approaches. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Host-Pathogen Adhesion as the Basis of Innovative Diagnostics for Emerging Pathogens

Infectious diseases are an existential health threat, potentiated by emerging and re-emerging viruses and increasing bacterial antibiotic resistance. Targeted treatment of infectious diseases requires precision diagnostics, especially in cases where broad-range therapeutics such as antibiotics fail. There is thus an increasing need for new approaches to develop sensitive and specific in vitro diagnostic (IVD) tests. Basic science and translational research are needed to identify key microbial molecules as diagnostic targets, to identify relevant host counterparts, and to use this knowledge in developing or improving IVD. In this regard, an overlooked feature is the capacity of pathogens to adhere specifically to host cells and tissues. The molecular entities relevant for pathogen–surface interaction are the so-called adhesins. Adhesins vary from protein compounds to (poly-)saccharides or lipid structures that interact with eukaryotic host cell matrix molecules and receptors. Such interactions co-define the specificity and sensitivity of a diagnostic test. Currently, adhesin-receptor binding is typically used in the pre-analytical phase of IVD tests, focusing on pathogen enrichment. Further exploration of adhesin–ligand interaction, supported by present high-throughput “omics” technologies, might stimulate a new generation of broadly applicable pathogen detection and characterization tools. This review describes recent results of novel structure-defining technologies allowing for detailed molecular analysis of adhesins, their receptors and complexes. Since the host ligands evolve slowly, the corresponding adhesin interaction is under selective pressure to maintain a constant receptor binding domain. IVD should exploit such conserved binding sites and, in particular, use the human ligand to enrich the pathogen. We provide an inventory of methods based on adhesion factors and pathogen attachment mechanisms, which can also be of relevance to currently emerging pathogens, including SARS-CoV-2, the causative agent of COVID-19.

In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology

Biological systems have evolved to utilize proteins to accomplish nearly all functional roles needed to sustain life. A majority of biological functions occur within the crowded environment inside cells and subcellular compartments where proteins exist in a densely packed complex network of protein-protein interactions. The structural biology field has experienced a renaissance with recent advances in crystallography, NMR, and CryoEM that now produce stunning models of large and complex structures previously unimaginable. Nevertheless, measurements of such structural detail within cellular environments remain elusive. This review will highlight how advances in mass spectrometry, chemical labeling, and informatics capabilities are merging to provide structural insights on proteins, complexes, and networks that exist inside cells. Because of the molecular detection specificity provided by mass spectrometry and proteomics, these approaches provide systems-level information that not only benefits from conventional structural analysis, but also is highly complementary. Although far from comprehensive in their current form, these approaches are currently providing systems structural biology information that can uniquely reveal how conformations and interactions involving many proteins change inside cells with perturbations such as disease, drug treatment, or phenotypic differences. With continued advancements and more widespread adaptation, systems structural biology based on in-cell labeling and mass spectrometry will provide an even greater wealth of structural knowledge.

In vivo Cross-Linking MS of the Complement System MAC Assembled on Live Gram-Positive Bacteria

Protein–protein interactions are central in many biological processes, but they are challenging to characterize, especially in complex samples. Protein cross-linking combined with mass spectrometry (MS) and computational modeling is gaining increased recognition as a viable tool in protein interaction studies. Here, we provide insights into the structure of the multicomponent human complement system membrane attack complex (MAC) using in vivo cross-linking MS combined with computational macromolecular modeling. We developed an affinity procedure followed by chemical cross-linking on human blood plasma using live Streptococcus pyogenes to enrich for native MAC associated with the bacterial surface. In this highly complex sample, we identified over 100 cross-linked lysine–lysine pairs between different MAC components that enabled us to present a quaternary model of the assembled MAC in its native environment. Demonstrating the validity of our approach, this MAC model is supported by existing X-ray crystallographic and electron cryo-microscopic models. This approach allows the study of protein–protein interactions in native environment mimicking their natural milieu. Its high potential in assisting and refining data interpretation in electron cryo-tomographic experiments will be discussed.

Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology

Streptococcus pyogenes (Group A streptococcus; GAS) is an important human pathogen responsible for mild to severe, life-threatening infections. GAS expresses a wide range of virulence factors, including the M family proteins. The M proteins allow the bacteria to evade parts of the human immune defenses by triggering the formation of a dense coat of plasma proteins surrounding the bacteria, including IgGs. However, the molecular level details of the M1-IgG interaction have remained unclear. Here, we characterized the structure and dynamics of this interaction interface in human plasma on the surface of live bacteria using integrative structural biology, combining cross-linking mass spectrometry and molecular dynamics (MD) simulations. We show that the primary interaction is formed between the S-domain of M1 and the conserved IgG Fc-domain. In addition, we show evidence for a so far uncharacterized interaction between the A-domain and the IgG Fc-domain. Both these interactions mimic the protein G-IgG interface of group C and G streptococcus. These findings underline a conserved scavenging mechanism used by GAS surface proteins that block the IgG-receptor (FcγR) to inhibit phagocytic killing. We additionally show that we can capture Fab-bound IgGs in a complex background and identify XLs between the constant region of the Fab-domain and certain regions of the M1 protein engaged in the Fab-mediated binding. Our results elucidate the M1-IgG interaction network involved in inhibition of phagocytosis and reveal important M1 peptides that can be further investigated as future vaccine targets.

Streptococcus pyogenes is a human specific pathogen causing both mild and invasive infections. It employs sophisticated mechanisms to evade and circumvent parts of the host’s immune defenses, in part via its major surface associated virulence factor, the family of M proteins. Of these, the M1 protein is the most prevalent serotype. The M1 protein creates a dense coat-like structure with multiple host proteins on the bacterial surface to disguise itself from opsonizing antibodies. It specifically interacts in a non-immune way with human immunoglobulin G (IgG) Fc-domains to disarm their receptor binding site. The molecular level details of this interaction have not been characterized. Here, we describe these interactions from minimally perturbed samples of human plasma adsorbed onto living bacteria using an integrative structural biology approach including cross-linking mass spectrometry, molecular modeling, and molecular dynamics simulations. We identify two distinct M1-peptides that bind IgGs and reveal the stability of these interactions. We show that both peptides block the Fc-receptor binding sites through capturing IgGs via their Fc-domains. These results highlight the importance of describing novel pathogen-derived peptides mediating host immune evasion as potential vaccine targets in future studies.