Rapid determination of quaternary protein structures in complex biological samples

HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy. Here, we developed HDXRank, a graph neural network (GNN)-based framework for candidate structure ranking utilizing alignment with HDX experimental data. Trained on a newly curated HDX data set, HDXRank captures nuanced local structural features critical for accurate HDX profile prediction. This versatile framework can be integrated with a variety of protein complex modeling tools, transforming the HDX profile alignment into a model quality metric. HDXRank demonstrates effectiveness at ranking models generated by rigid docking or AlphaFold, successfully prioritizing functionally relevant models and improving prediction quality across all tested protein targets. These findings underscore HDXRank's potential to become a pivotal tool for understanding molecular recognition in complex biological systems.

Epitope Mapping with Sidewinder: An XL-MS and Structural Modeling Approach

Antibodies are critical to the host's immune defense against bacterial pathogens. Understanding the mechanisms of antibody-antigen interactions is essential for developing new targeted immunotherapies. Building computational workflows that can identify where an antibody binds its cognate antigen and deconvoluting the interaction interface in a high-throughput manner are critical for advancing this field. Cross-linking mass spectrometry (XL-MS) integrated with structural modeling offers a flexible and high-resolution strategy to map protein-protein interactions from low sample amounts. However, cross-linking and in silico modeling have limitations that require robust analytical workflows to make accurate inferences. In this study, we introduce Sidewinder, a modular high-throughput pipeline combining state-of-the-art computational structural prediction and molecular docking with rapid XL-MS analysis, enabling comprehensive interrogation of antibody-antigen systems. We validated this pipeline on antibodies targeting two Streptococcus pyogenes virulence factors. Using recently published data, we identified a well-defined monoclonal antibody epitope on Streptolysin O by generating and querying a large ensemble of interaction models probabilistically. We also showcased the utility of the Sidewinder pipeline by analyzing a more complex system, involving monoclonal antibodies that target the cell wall-anchored M1 protein. The flexibility and robustness of the Sidewinder pipeline provide a powerful framework for future studies of complex antibody-antigen systems, potentially leading to new therapeutic strategies.

Energy Resolved Mass Spectrometry Data from Surfaced Induced Dissociation Improves Prediction of Protein Complex Structure

Native Mass Spectrometry (nMS) is a versatile technique for elucidating protein structure. Surface-Induced Dissociation (SID) is an activation method in tandem MS predominantly employed for determining protein complex stoichiometry alongside information about interface strengths. SID-nMS data can be collected over a range of acceleration energies, yielding Energy Resolved Mass Spectrometry (ERMS) data. Previous work demonstrated that the onset and appearance energy from SID-nMS can be used in integrative computational and experimental modeling to guide multimeric structure determination in some cases. However, the appearance energy is a single data point, while the ERMS data provide a full pattern of interface breakage. We hypothesized that incorporation of ERMS data into multimeric protein structure prediction would significantly outperform appearance energy. To test this hypothesis, we generated models of 20 protein complexes with RosettaDock using subunits generated from AlphaFold2. We simulated the ERMS data for each predicted model and rescored based on its agreement to experimental ERMS data. We demonstrated that more accurately predicted models exhibited simulated ERMS data in better agreement with the experimental data. As part of our ERMS-based rescoring, we matched or improved the RMSD of the best scoring model compared to Rosetta in 16 out of 20 cases, with 4 out of 20 cases improving to become a highly accurate (below 5 Å) structure. Finally, we benchmarked our method against our previously published appearance energy-based rescoring and showed improvement in 14 out of 20 cases, with 6 out of 20 becoming a highly accurate (below 5 Å) model. Our method is freely available through Rosetta Commons, with a usage tutorial and test files provided in the Supporting Information.

Streptolysin O accelerates the conversion of plasminogen to plasmin

Group A Streptococcus (GAS) is a human-specific bacterial pathogen that can exploit the plasminogen-plasmin fibrinolysis system to dismantle blood clots and facilitate its spread and survival within the human host. In this study, we use affinity-enrichment mass spectrometry to decipher the host-pathogen protein-protein interaction between plasminogen and streptolysin O, a key cytolytic toxin produced by GAS. This interaction accelerates the conversion of plasminogen to plasmin by both the host tissue-type plasminogen activator and streptokinase, a bacterial plasminogen activator secreted by GAS. Integrative structural mass spectrometry analysis shows that the interaction induces local conformational shifts in plasminogen. These changes lead to the formation of a stabilised intermediate plasminogen-streptolysin O complex that becomes significantly more susceptible to proteolytic processing by plasminogen activators. Our findings reveal a conserved and moonlighting pathomechanistic function for streptolysin O that extends beyond its well-characterised cytolytic activity.

Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O

An important element of antibody-guided vaccine design is the use of neutralizing or opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multimodal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass-spectrometry-based de novo sequencing, followed by chemical cross-linking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in domain 3, likely preventing oligomerization and subsequent secondary structure transitions critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.

Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein

Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host-pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.

The hinge-engineered IgG1-IgG3 hybrid subclass IgGh47 potently enhances Fc-mediated function of anti-streptococcal and SARS-CoV-2 antibodies

Streptococcus pyogenes can cause invasive disease with high mortality despite adequate antibiotic treatments. To address this unmet need, we have previously generated an opsonic IgG1 monoclonal antibody, Ab25, targeting the bacterial M protein. Here, we engineer the IgG2-4 subclasses of Ab25. Despite having reduced binding, the IgG3 version promotes stronger phagocytosis of bacteria. Using atomic simulations, we show that IgG3's Fc tail has extensive movement in 3D space due to its extended hinge region, possibly facilitating interactions with immune cells. We replaced the hinge of IgG1 with four different IgG3-hinge segment subclasses, IgGhxx. Hinge-engineering does not diminish binding as with IgG3 but enhances opsonic function, where a 47 amino acid hinge is comparable to IgG3 in function. IgGh47 shows improved protection against S. pyogenes in a systemic infection mouse model, suggesting that IgGh47 has promise as a preclinical therapeutic candidate. Importantly, the enhanced opsonic function of IgGh47 is generalizable to diverse S. pyogenes strains from clinical isolates. We generated IgGh47 versions of anti-SARS-CoV-2 mAbs to broaden the biological applicability, and these also exhibit strongly enhanced opsonic function compared to the IgG1 subclass. The improved function of the IgGh47 subclass in two distant biological systems provides new insights into antibody function.

Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver

Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).

Shigella IpaA mediates actin bundling through diffusible vinculin oligomers with activation imprint

Upon activation, vinculin reinforces cytoskeletal anchorage during cell adhesion. Activating ligands classically disrupt intramolecular interactions between the vinculin head and tail domains that bind to actin filaments. Here, we show that Shigella IpaA triggers major allosteric changes in the head domain, leading to vinculin homo-oligomerization. Through the cooperative binding of its three vinculin-binding sites (VBSs), IpaA induces a striking reorientation of the D1 and D2 head subdomains associated with vinculin oligomerization. IpaA thus acts as a catalyst producing vinculin clusters that bundle actin at a distance from the activation site and trigger the formation of highly stable adhesions resisting the action of actin relaxing drugs. Unlike canonical activation, vinculin homo-oligomers induced by IpaA appear to keep a persistent imprint of the activated state in addition to their bundling activity, accounting for stable cell adhesion independent of force transduction and relevant to bacterial invasion.

Group A streptococci induce stronger M protein-fibronectin interaction when specific human antibodies are bound

Group A streptococcus (GAS) is a highly adapted, human-specific pathogen that is known to manipulate the immune system through various mechanisms. GAS' M protein constitutes a primary target of the immune system due to its spatial configuration and dominance on the bacterial surface. Antibody responses targeting the M protein have been shown to favor the conserved C region. Such antibodies (Abs) circumvent antigenic escape and efficiently bind to various M types. The ability of GAS to bind to fibronectin (Fn), a high molecular weight glycoprotein of the extracellular matrix, has long been known to be essential for the pathogen's evolutionary success and fitness. However, some strains lack the ability to efficiently bind Fn. Instead, they have been found to additionally bind Fn via the A-B domains of their M proteins. Here, we show that human Abs can induce increased Fn-binding affinity in M proteins, likely by enhancing the weak A-B domain binding. We found that this enhanced Fn binding leads to a reduction in Ab-mediated phagocytosis, indicating that this constitutes a GAS immune escape mechanism. We could show that the Fc domain of Abs is necessary to trigger this phenomenon and that Ab flexibility may also play a key role. We, moreover, saw that our Abs could enhance Fn binding in 3 out of 5 emm type strains tested, belonging to different clades, making it likely that this is a more generalizable phenomenon. Together our results suggest a novel synergistic interplay of GAS and host proteins which ultimately benefits the bacterium.

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.

Affinity-Purification Combined with Crosslinking Mass Spectrometry for Identification and Structural Modeling of Host-Pathogen Protein-Protein Complexes

Host-pathogen protein-protein interactions are highly complex and dynamic and mediate key steps in pathogen adhesion to host, host invasion, and colonization as well as immune evasion. In bacteria, these interactions most often involve specialized virulence factors or effector proteins that specifically target central host proteins. Here, I present a mass spectrometry-based proteomics approach starting with the identification of host-pathogen interactions by affinity-purification followed by mapping the specific host-pathogen protein-protein interaction interfaces by crosslinking mass spectrometry and structural modeling of the complexes.

A human monoclonal antibody bivalently binding two different epitopes in streptococcal M protein mediates immune function

Group A streptococci have evolved multiple strategies to evade human antibodies, making it challenging to create effective vaccines or antibody treatments. Here, we have generated antibodies derived from the memory B cells of an individual who had successfully cleared a group A streptococcal infection. The antibodies bind with high affinity in the central region of the surface-bound M protein. Such antibodies are typically non-opsonic. However, one antibody could effectively promote vital immune functions, including phagocytosis and in vivo protection. Remarkably, this antibody primarily interacts through a bivalent dual-Fab cis mode, where the Fabs bind to two distinct epitopes in the M protein. The dual-Fab cis-binding phenomenon is conserved across different groups of M types. In contrast, other antibodies binding with normal single-Fab mode to the same region cannot bypass the M protein's virulent effects. A broadly binding, protective monoclonal antibody could be a candidate for anti-streptococcal therapy. Our findings highlight the concept of dual-Fab cis binding as a means to access conserved, and normally non-opsonic regions, regions for protective antibody targeting.

Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction.

Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation

Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.

Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook

Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.

Interaction of Bartonella henselae with Fibronectin Represents the Molecular Basis for Adhesion to Host Cells

Deciphering the mechanisms of bacterial host cell adhesion is a clue for preventing infections. We describe the underestimated role that the extracellular matrix protein fibronectin plays in the adhesion of human-pathogenic Bartonella henselae to host cells.

Nanoscale binding site localization by molecular distance estimation on native cell surfaces using topological image averaging

Antibody binding to cell surface proteins plays a crucial role in immunity, and the location of an epitope can altogether determine the immunological outcome of a host-target interaction. Techniques available today for epitope identification are costly, time-consuming, and unsuited for high-throughput analysis. Fast and efficient screening of epitope location can be useful for the development of therapeutic monoclonal antibodies and vaccines. Cellular morphology typically varies, and antibodies often bind heterogeneously across a cell surface, making traditional particle-averaging strategies challenging for accurate native antibody localization. In the present work, we have developed a method, SiteLoc, for imaging-based molecular localization on cellular surface proteins. Nanometer-scale resolution is achieved through localization in one dimension, namely, the distance from a bound ligand to a reference surface. This is done by using topological image averaging. Our results show that this method is well suited for antibody binding site measurements on native cell surface morphology and that it can be applied to other molecular distance estimations as well.

Antibodies play a key role in the immune system. These proteins stick to harmful substances, such as bacteria and other disease-causing pathogens, marking them for destruction or blocking their attack. Antibodies are highly selective, and this ability has been used to target particular molecules in research, diagnostics and therapies.

Typically, antibodies need to stick to a particular segment, or ‘epitope’, on the surface of a cell in order to trigger an immune response. Knowing where these regions are can help explain how these immune proteins work and aid the development of more effective drugs and diagnostic tools.

One way to identify these sites is to measure the nano-distance between antibodies and other features on the cell surface. To do this, researchers take multiple images of the cell the antibody is attached to using light microscopy. Various statistical methods are then applied to create an ‘average image’ that has a higher resolution and can therefore be used to measure the distance between these two points more accurately. While this approach works on fixed shapes, like a perfect circle, it cannot handle human cells and bacteria which are less uniform and have more complex surfaces.

Here, Kumra Ahnlide et al. have developed a new method called SiteLoc which can overcome this barrier. The method involves two fluorescent probes: one attached to a specific site on the cell’s surface, and the other to the antibody or another molecule of interest. These two probes emit different colours when imaged with a fluorescent microscope. To cope with objects that have uneven surfaces, such as cells and bacteria, the two signals are transformed to ‘follow’ the same geometrical shape. The relative distance between them is then measured using statistical methods. Using this approach, Kumra Ahnlide et al. were able to identify epitopes on a bacterium, and measure distances on the surface of human red blood cells.

The SiteLoc system could make it easier to develop antibody-based treatments and diagnostic tools. Furthermore, it could also be beneficial to the wider research community who could use it to probe other questions that require measuring nanoscale distances.

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.

Spike-Dependent Opsonization Indicates Both Dose-Dependent Inhibition of Phagocytosis and That Non-Neutralizing Antibodies Can Confer Protection to SARS-CoV-2

Spike-specific antibodies are central to effective COVID19 immunity. Research efforts have focused on antibodies that neutralize the ACE2-Spike interaction but not on non-neutralizing antibodies. Antibody-dependent phagocytosis is an immune mechanism enhanced by opsonization, where typically, more bound antibodies trigger a stronger phagocyte response. Here, we show that Spike-specific antibodies, dependent on concentration, can either enhance or reduce Spike-bead phagocytosis by monocytes independently of the antibody neutralization potential. Surprisingly, we find that both convalescent patient plasma and patient-derived monoclonal antibodies lead to maximum opsonization already at low levels of bound antibodies and is reduced as antibody binding to Spike protein increases. Moreover, we show that this Spike-dependent modulation of opsonization correlate with the outcome in an experimental SARS-CoV-2 infection model. These results suggest that the levels of anti-Spike antibodies could influence monocyte-mediated immune functions and propose that non-neutralizing antibodies could confer protection to SARS-CoV-2 infection by mediating phagocytosis.

Distinct serotypes of streptococcal M proteins mediate fibrinogen-dependent platelet activation and pro-inflammatory effects

Sepsis is a life-threatening complication of infection that is characterized by a dysregulated inflammatory state and disturbed hemostasis. Platelets are the main regulators of hemostasis, and they also respond to inflammation. The human pathogen Streptococcus pyogenes can cause local infection that may progress to sepsis. There are more than 200 serotypes of S. pyogenes defined according to sequence variations in the M protein. The M1 serotype is among 10 serotypes that are predominant in invasive infection. M1 protein can be released from the surface and has previously been shown to generate platelet, neutrophil, and monocyte activation. The platelet-dependent proinflammatory effects of other serotypes of M protein associated with invasive infection (M3, M5, M28, M49, and M89) are now investigated using a combination of multiparameter flow cytometry, enzyme-linked immunosorbent assay (ELISA), aggregometry, and quantitative mass spectrometry. We demonstrate that only M1, M3, and M5 protein serotypes can bind fibrinogen in plasma and mediate fibrinogen- and IgG-dependent platelet activation and aggregation, release of granule proteins, upregulation of CD62P to the platelet surface, and complex formation with neutrophils and monocytes. Neutrophil and monocyte activation, determined as upregulation of surface CD11b, is also mediated by M1, M3, and M5 protein serotypes, while M28, M49, and M89 proteins failed to mediate activation of platelets or leukocytes. Collectively, our findings reveal novel aspects of the immunomodulatory role of fibrinogen acquisition and platelet activation during streptococcal infections.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Protein Structure Prediction with Mass Spectrometry Data

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Integration of Mass Spectrometry Data for Structural Biology

Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.

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.

Cheetah-MS: a web server to model protein complexes using tandem cross-linking mass spectrometry data

Summary

Protein–protein interactions (PPIs) are central in many biological processes but difficult to characterize, especially in complex, unfractionated samples. Chemical cross-linking combined with mass spectrometry (MS) and computational modeling is gaining recognition as a viable tool in protein interaction studies. Here, we introduce Cheetah-MS, a web server for predicting the PPIs in a complex mixture of samples. It combines the capability and sensitivity of MS to analyze complex samples with the power and resolution of protein–protein docking. It produces the quaternary structure of the PPI of interest by analyzing tandem MS/MS data (also called MS2). Combining MS analysis and modeling increases the sensitivity and, importantly, facilitates the interpretation of the results.

Availability and implementation

Cheetah-MS is freely available as a web server at https://www.txms.org.

Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy

A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD₁₀₀ of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD₁₀₀ less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.

Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

The structure of human dermatan sulfate epimerase 1 emphasizes the importance of C5-epimerization of glucuronic acid in higher organisms

Dermatan sulfate epimerase 1 (DS-epi1, EC 5.1.3.19) catalyzes the conversion of d-glucuronic acid to l-iduronic acid on the polymer level, a key step in the biosynthesis of the glycosaminoglycan dermatan sulfate. Here, we present the first crystal structure of the catalytic domains of DS-epi1, solved at 2.4 Å resolution, as well as a model of the full-length luminal protein obtained by a combination of macromolecular crystallography and targeted cross-linking mass spectrometry. Based on docking studies and molecular dynamics simulations of the protein structure and a chondroitin substrate, we suggest a novel mechanism of DS-epi1, involving a His/double-Tyr motif. Our work uncovers detailed information about the domain architecture, active site, metal-coordinating center and pattern of N-glycosylation of the protein. Additionally, the structure of DS-epi1 reveals a high structural similarity to proteins from several families of bacterial polysaccharide lyases. DS-epi1 is of great importance in a range of diseases, and the structure provides a necessary starting point for design of active site inhibitors.

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.

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges

Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies

Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known 3D structures of representative protein complexes. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue.

The current standard approach for estimating error in proteome-scale crosslinking-mass spectrometry datasets has severe limitations. A proposed set of data-quality metrics provides a more accurate assessment of error rate.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

A protocol for studying structural dynamics of proteins by quantitative crosslinking mass spectrometry and data-independent acquisition

Quantitative crosslinking mass spectrometry (QCLMS) reveals structural details of protein conformations in solution. QCLMS can benefit from data-independent acquisition (DIA), which maximises accuracy, reproducibility and throughput of the approach. This DIA-QCLMS protocol comprises of three main sections: sample preparation, spectral library generation and quantitation. The DIA-QCLMS workflow supports isotope-labelling as well as label-free quantitation strategies, uses xiSEARCH for crosslink identification, and xiDIA-Library to create a spectral library for a peptide-centric quantitative approach. We integrated Spectronaut, a leading quantitation software, to analyse DIA data. Spectronaut supports DIA-QCLMS data to quantify crosslinks. It can be used to reveal the structural dynamics of proteins and protein complexes, even against a complex background. In combination with photoactivatable crosslinkers (photo-DIA-QCLMS), the workflow can increase data density and better capture protein dynamics due to short reaction times. Additionally, this can reveal conformational changes caused by environmental influences that would otherwise affect crosslinking itself, such as changing pH conditions. SIGNIFICANCE: This protocol is an detailed step-by-step description on how to implement our previously published DIA-QCLMS workflow (Müller et al. Mol Cell Proteomics. 2019 Apr;18(4):786-795). It includes sample preparation for QCLMS, Optimization of DIA strategies, implementation of the Spectronaut software and required python scripts and guideline on how to analyse quantitative crosslinking data. The DIA-QCLMS workflow widen the scope for a range of new crosslinking applications and this step-by-step protocol enhances the accessibility to a broad scientific user base.

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

A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.

A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies

A fundamental challenge in medical microbiology is to characterize the dynamic protein–protein interaction networks formed at the host–pathogen interface. Here, we generate a quantitative interaction map between the significant human pathogen, Streptococcus pyogenes, and proteins from human saliva and plasma obtained via complementary affinity-purification and bacterial-surface centered enrichment strategies and quantitative mass spectrometry. Perturbation of the network using immunoglobulin protease cleavage, mixtures of different concentrations of saliva and plasma, and different S. pyogenes serotypes and their isogenic mutants, reveals how changing microenvironments alter the interconnectivity of the interaction map. The importance of host immunoglobulins for the interaction with human complement proteins is demonstrated and potential protective epitopes of importance for phagocytosis of S. pyogenes cells are localized. The interaction map confirms several previously described protein–protein interactions; however, it also reveals a multitude of additional interactions, with possible implications for host–pathogen interactions involving other bacterial species.

Characterizing host-pathogen protein interactions can help elucidate the molecular basis of bacterial infections. Here, the authors use an integrative proteomics approach to generate a quantitative map of protein interactions between Streptococcus pyogenes and human saliva and plasma.

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Greedy de novo motif discovery to construct motif repositories for bacterial proteomes

BACKGROUND

Bacterial surfaces are complex systems, constructed from membranes, peptidoglycan and, importantly, proteins. The proteins play crucial roles as critical regulators of how the bacterium interacts with and survive in its environment. A full catalog of the motifs in protein families and their relative conservation grade is a prerequisite to target the protein-protein interaction that bacterial surface protein makes to host proteins.

RESULTS

In this paper, we propose a greedy approach to identify conserved motifs in large sequence families iteratively. Each iteration discovers a motif de novo and masks all occurrences of that motif. Remaining unmasked sequences are subjected to the next round of motif detection until no more significant motifs can be found. We demonstrate the utility of the method through the construction of a proteome-wide motif repository for Group A Streptococcus (GAS), a significant human pathogen. GAS produce numerous surface proteins that interact with over 100 human plasma proteins, helping the bacteria to evade the host immune response. We used the repository to find that proteins part of the bacterial surface has motif architectures that differ from intracellular proteins.

CONCLUSIONS

We elucidate that the M protein, a coiled-coil homodimer that extends over 500 A from the cell wall, has a motif architecture that differs between various GAS strains. As the M protein is known to bind a variety of different plasma proteins, the results indicate that the different motif architectures are responsible for the quantitative differences of plasma proteins that various strains bind. The speed and applicability of the method enable its application to all major human pathogens.