Breathing and Tilting: Mesoscale Simulations Illuminate Influenza Glycoprotein Vulnerabilities

Formulation development of a stable influenza recombinant neuraminidase vaccine candidate

Current influenza vaccines could be augmented by including recombinant neuraminidase (rNA) protein antigen to broaden protective immunity and improve efficacy. Toward this goal, we investigated formulation conditions to optimize rNA physicochemical stability. When rNA in sodium phosphate saline buffer (NaPBS) was frozen and thawed (F/T), the tetrameric structure transitioned from a "closed" to an "open" conformation, negatively impacting functional activity. Hydrogen deuterium exchange experiments identified differences in anchorage binding sites at the base of the open tetramer, offering a structural mechanistic explanation for the change in conformation and decreased functional activity. Change to the open configuration was triggered by the combined stresses of acidic pH and F/T. The desired closed conformation was preserved in a potassium phosphate buffer (KP), minimizing pH drop upon freezing and including 10% sucrose to control F/T stress. Stability was further evaluated in thermal stress studies where changes in conformation were readily detected by ELISA and size exclusion chromatography (SEC). Both tests were suitable indicators of stability and antigenicity and considered potential critical quality attributes (pCQAs). To understand longer-term stability, the pCQA profiles from thermally stressed rNA at 6 months were modeled to predict stability of at least 24-months at 5°C storage. In summary, a desired rNA closed tetramer was maintained by formulation selection and monitoring of pCQAs to produce a stable rNA vaccine candidate. The study highlights the importance of understanding and controlling vaccine protein structural and functional integrity.

Strategies of rational and structure-driven vaccine design for Arenaviruses

The COVID-19 outbreak has highlighted the importance of pandemic preparedness for the prevention of future health crises. One virus family with high pandemic potential are Arenaviruses, which have been detected almost worldwide, particularly in Africa and the Americas. These viruses are highly understudied and many questions regarding their structure, replication and tropism remain unanswered, making the design of an efficacious and molecularly-defined vaccine challenging. We propose that structure-driven computational vaccine design will contribute to overcome these challenges. Computational methods for stabilization of viral glycoproteins or epitope focusing have made progress during the last decades and particularly during the COVID-19 pandemic, and have proven useful for rational vaccine design and the establishment of novel diagnostic tools. In this review, we summarize gaps in our understanding of Arenavirus molecular biology, highlight challenges in vaccine design and discuss how structure-driven and computationally informed strategies will aid in overcoming these obstacles.

There and back again: bridging meso- and nano-scales to understand lipid vesicle patterning

We describe a complete methodology to bridge the scales between nanoscale molecular dynamics and (micrometer) mesoscale Monte Carlo simulations in lipid membranes and vesicles undergoing phase separation, in which curving molecular species are furthermore embedded. To go from the molecular to the mesoscale, we notably appeal to physical renormalization arguments enabling us to rigorously infer the mesoscale interaction parameters from its molecular counterpart. We also explain how to deal with the physical timescales at stake at the mesoscale. Simulating the as-obtained mesoscale system enables us to equilibrate the long wavelengths of the vesicles of interest, up to the vesicle size. Conversely, we then backmap from the meso- to the nano-scale, which enables us to equilibrate in turn the short wavelengths down to the molecular length-scales. By applying our approach to the specific situation of patterning a vesicle membrane, we show that macroscopic membranes can thus be equilibrated at all length-scales in achievable computational time offering an original strategy to address the fundamental challenge of timescale in simulations of large bio-membrane systems.

Protective human monoclonal antibodies target conserved sites of vulnerability on the underside of influenza virus neuraminidase

Continuously evolving influenza viruses cause seasonal epidemics and pose global pandemic threats. Although viral neuraminidase (NA) is an effective drug and vaccine target, our understanding of the NA antigenic landscape still remains incomplete. Here, we describe NA-specific human antibodies that target the underside of the NA globular head domain, inhibit viral propagation of a wide range of human H3N2, swine-origin variant H3N2, and H2N2 viruses, and confer both pre- and post-exposure protection against lethal H3N2 infection in mice. Cryo-EM structures of two such antibodies in complex with NA reveal non-overlapping epitopes covering the underside of the NA head. These sites are highly conserved among N2 NAs yet inaccessible unless the NA head tilts or dissociates. Our findings help guide the development of effective countermeasures against ever-changing influenza viruses by identifying hidden conserved sites of vulnerability on the NA underside.

All-Atom Biomolecular Simulation in the Exascale Era

Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.

Prefusion-stabilized SARS-CoV-2 S2-only antigen provides protection against SARS-CoV-2 challenge

Ever-evolving SARS-CoV-2 variants of concern (VOCs) have diminished the effectiveness of therapeutic antibodies and vaccines. Developing a coronavirus vaccine that offers a greater breadth of protection against current and future VOCs would eliminate the need to reformulate COVID-19 vaccines. Here, we rationally engineer the sequence-conserved S2 subunit of the SARS-CoV-2 spike protein and characterize the resulting S2-only antigens. Structural studies demonstrate that the introduction of interprotomer disulfide bonds can lock S2 in prefusion trimers, although the apex samples a continuum of conformations between open and closed states. Immunization with prefusion-stabilized S2 constructs elicits broadly neutralizing responses against several sarbecoviruses and protects female BALB/c mice from mouse-adapted SARS-CoV-2 lethal challenge and partially protects female BALB/c mice from mouse-adapted SARS-CoV lethal challenge. These engineering and immunogenicity results should inform the development of next-generation pan-coronavirus therapeutics and vaccines.

Integrating cellular electron microscopy with multimodal data to explore biology across space and time

Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.

Mechanisms of allostery at the viral surface through the eyes of molecular simulation

The outermost surface layer of any virus is formed by either a capsid shell or envelope. Such layers have traditionally been thought of as immovable structures, but it is becoming apparent that they cannot be viewed exclusively as static architectures protecting the viral genome. A limited number of proteins on the virion surface must perform a multitude of functions in order to orchestrate the viral life cycle, and allostery can regulate their structures at multiple levels of organization, spanning individual molecules, protomers, large oligomeric assemblies, or entire viral surfaces. Here, we review recent contributions from the molecular simulation field to viral surface allostery, with a particular focus on the trimeric spike glycoprotein emerging from the coronavirus surface, and the icosahedral flaviviral envelope complex. As emerging viral pathogens continue to pose a global threat, an improved understanding of viral dynamics and allosteric regulation will prove crucial in developing novel therapeutic strategies.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Targeting neuraminidase: the next frontier for broadly protective influenza vaccines

Current seasonal influenza vaccines, which mainly target hemagglutinin (HA), require annual updates due to the continuous antigenic drift of the influenza virus. Developing an influenza vaccine with increased breadth of protection will have significant public health benefits. The recent discovery of broadly protective antibodies to neuraminidase (NA) has provided important insights into developing a universal influenza vaccine, either by improving seasonal influenza vaccines or designing novel immunogens. However, further in-depth molecular characterizations of NA antibody responses are warranted to fully leverage broadly protective NA antibodies for influenza vaccine designs. Overall, we posit that focusing on NA for influenza vaccine development is synergistic with existing efforts targeting HA, and may represent a cost-effective approach to generating a broadly protective influenza vaccine.

Structure of a SARS-CoV-2 spike S2 subunit in a pre-fusion, open conformation

The 800 million human infections with SARS-CoV-2 and the likely emergence of new variants and additional coronaviruses necessitate a better understanding of the essential spike glycoprotein and the development of immunogens that foster broader and more durable immunity. The S2 fusion subunit is more conserved in sequence, is essential to function, and would be a desirable immunogen to boost broadly reactive antibodies. It is, however, unstable in structure and in its wild-type form, cannot be expressed alone without irreversible collapse into a six-helix bundle. In addition to the irreversible conformational changes of fusion, biophysical measurements indicate that spike also undergoes a reversible breathing action. However, spike in an open, "breathing" conformation has not yet been visualized at high resolution. Here we describe an S2-only antigen, engineered to remain in its relevant, pre-fusion viral surface conformation in the absence of S1. We also describe a panel of natural human antibodies specific for S2 from vaccinated and convalescent individuals. One of these mAbs, from a convalescent individual, afforded a high-resolution cryo-EM structure of the prefusion S2. The structure reveals a complex captured in an "open" conformation with greater stabilizing intermolecular interactions at the base and a repositioned fusion peptide. Together, this work provides an antigen for advancement of next-generation "booster" immunogens and illuminates the likely breathing adjustments of the coronavirus spike.

A broad antibody class engages the influenza virus hemagglutinin head at its stem interface

Influenza infection and vaccination impart strain-specific immunity that fails to protect against both seasonal antigenic variants and the next pandemic. However, antibodies directed to conserved sites can confer broad protection. We identify and characterize a class of human antibodies that engage a previously undescribed, conserved, epitope on the influenza hemagglutinin protein (HA). Prototype antibody S8V1-157 binds at the normally occluded interface between the HA head and stem. Antibodies to this HA head-stem interface epitope are non-neutralizing in vitro but protect against lethal infection in mice. Their breadth of binding extends across most influenza A serotypes and seasonal human variants. Antibodies to the head-stem interface epitope are present at low frequency in the memory B cell populations of multiple donors. The immunogenicity of the epitope warrants its consideration for inclusion in improved or "universal" influenza vaccines.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

Leveraging vaccination-induced protective antibodies to define conserved epitopes on influenza N2 neuraminidase

There is growing appreciation for neuraminidase (NA) as an influenza vaccine target; however, its antigenicity remains poorly characterized. In this study, we isolated three broadly reactive N2 antibodies from the plasmablasts of a single vaccinee, including one that cross-reacts with NAs from seasonal H3N2 strains spanning five decades. Although these three antibodies have diverse germline usages, they recognize similar epitopes that are distant from the NA active site and instead involve the highly conserved underside of NA head domain. We also showed that all three antibodies confer prophylactic and therapeutic protection in vivo, due to both Fc effector functions and NA inhibition through steric hindrance. Additionally, the contribution of Fc effector functions to protection in vivo inversely correlates with viral growth inhibition activity in vitro. Overall, our findings advance the understanding of NA antibody response and provide important insights into the development of a broadly protective influenza vaccine.

Glycosylation and Crowded Membrane Effects on Influenza Neuraminidase Stability and Dynamics

All protein simulations are conducted with varying degrees of simplification, oftentimes with unknown ramifications about how these simplifications affect the interpretability of the results. In this work, we investigated how protein glycosylation and lateral crowding effects modulate an array of properties characterizing the stability and dynamics of influenza neuraminidase. We constructed three systems: (1) glycosylated neuraminidase in a whole virion (i.e., crowded membrane) environment, (2) glycosylated neuraminidase in its own lipid bilayer, and (3) unglycosylated neuraminidase in its own lipid bilayer. We saw that glycans tend to stabilize the protein structure and reduce its conformational flexibility while restricting the solvent movement. Conversely, a crowded membrane environment encouraged exploration of the free energy landscape and a large-scale conformational change, while making the protein structure more compact. Understanding these effects informs what factors one must consider in attempting to recapture the desired level of physical accuracy.

Building the next generation of virtual cells to understand cellular biology

Cell science has made significant progress by focusing on understanding individual cellular processes through reductionist approaches. However, the sheer volume of knowledge collected presents challenges in integrating this information across different scales of space and time to comprehend cellular behaviors, as well as making the data and methods more accessible for the community to tackle complex biological questions. This perspective proposes the creation of next-generation virtual cells, which are dynamic 3D models that integrate information from diverse sources, including simulations, biophysical models, image-based models, and evidence-based knowledge graphs. These virtual cells would provide statistically accurate and holistic views of real cells, bridging the gap between theoretical concepts and experimental data, and facilitating productive new collaborations among researchers across related fields.

Glycosylation and Crowded Membrane Effects on Influenza Neuraminidase Stability and Dynamics

All protein simulations are conducted with varying degrees of simplifications, oftentimes with unknown ramifications on how these simplifications affect the interpretability of the results. In this work we investigated how protein glycosylation and lateral crowding effects modulate an array of properties characterizing the stability and dynamics of influenza neuraminidase. We constructed three systems: 1) Glycosylated neuraminidase in a whole virion (i.e. crowded membrane) environment 2) Glycosylated neuraminidase in its own lipid bilayer 3) Unglycosylated neuraminidase in its own lipid bilayer. We saw that glycans tend to stabilize the protein structure and reduce its conformational flexibility while restricting solvent movement. Conversely, a crowded membrane environment encouraged exploration of the free energy landscape and a large scale conformational change while making the protein structure more compact. Understanding these effects informs what factors one must consider while attempting to recapture the desired level of physical accuracy.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Understanding Virus Structure and Dynamics through Molecular Simulations

Viral outbreaks remain a serious threat to human and animal populations and motivate the continued development of antiviral drugs and vaccines, which in turn benefits from a detailed understanding of both viral structure and dynamics. While great strides have been made in characterizing these systems experimentally, molecular simulations have proven to be an essential, complementary approach. In this work, we review the contributions of molecular simulations to the understanding of viral structure, functional dynamics, and processes related to the viral life cycle. Approaches ranging from coarse-grained to all-atom representations are discussed, including current efforts at modeling complete viral systems. Overall, this review demonstrates that computational virology plays an essential role in understanding these systems.

Recent breakthroughs in computational structural biology harnessing the power of sequences and structures

Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.

A brief history of visualizing membrane systems in molecular dynamics simulations

Understanding lipid dynamics and function, from the level of single, isolated molecules to large assemblies, is more than ever an intensive area of research. The interactions of lipids with other molecules, particularly membrane proteins, are now extensively studied. With advances in the development of force fields for molecular dynamics simulations (MD) and increases in computational resources, the creation of realistic and complex membrane systems is now common. In this perspective, we will review four decades of the history of molecular dynamics simulations applied to membranes and lipids through the prism of molecular graphics.