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The PhoQP two-component system is a signaling complex essential for bacterial virulence and cationic antimicrobial peptide resistance. PhoQ is the histidine kinase chemoreceptor of this tandem machine and assembles in a homodimer conformation spanning the bacterial inner membrane. Currently, a full understanding of the PhoQ signal transduction is hindered by the lack of a complete atomistic structure. In this study, an atomistic model of the key transmembrane (TM) domain is assembled by using molecular simulations, guided by experimental cross-linking data. The formation of a polar pocket involving Asn202 in the lumen of the tetrameric TM bundle is crucial for the assembly and solvation of the domain. Moreover, a concerted displacement of the TM helices at the periplasmic side is found to modulate a rotation at the cytoplasmic end, supporting the transduction of the chemical signal through a combination of scissoring and rotational movement of the TM helices.

The M2 protein of influenza A virus is a membrane-spanning tetrameric proton channel targeted by the antiviral drugs amantadine and rimantadine (1). Resistance to these drugs has compromised their effectiveness against many influenza strains, including pandemic H1N1. A recent crystal structure of M2(22-46) showed electron densities attributed to a single amantadine in the amino-terminal half of the pore (2), indicating a physical occlusion mechanism for inhibition. However, a solution NMR structure of M2(18-60) showed four rimantadines bound to the carboxy-terminal lipid-facing surface of the helices (3), suggesting an allosteric mechanism. Here we show by solid-state NMR spectroscopy that two amantadine-binding sites exist in M2 in phospholipid bilayers. The high-affinity site, occupied by a single amantadine, is located in the N-terminal channel lumen, surrounded by residues mutated in amantadine-resistant viruses. Quantification of the protein-amantadine distances resulted in a 0.3 Å-resolution structure of the high-affinity binding site. The second, low-affinity, site was observed on the C-terminal protein surface, but only when the drug reaches high concentrations in the bilayer. The orientation and dynamics of the drug are distinct in the two sites, as shown by ²H NMR. These results indicate that amantadine physically occludes the M2 channel, thus paving the way for developing new antiviral drugs against influenza viruses. The study demonstrates the ability of solid-state NMR to elucidate small-molecule interactions with membrane proteins and determine high-resolution structures of their complexes., The M2 protein of influenza A viruses is a modular, multifunctional protein that plays important roles in the acidification and uncoating of the endosome-entrapped virus and in viral assembly and [...]

ChvE is a chromosomally encoded protein in Agrobacterium tumefaciens that mediates a sugar-induced increase in virulence (vir) gene expression through the activities of the VirA/VirG two-component system and has also been suggested to be involved in sugar utilization. The ChvE protein has homology to several bacterial periplasmic sugar-binding proteins, such as the ribose-binding protein and the galactose/glucose-binding protein of Escherichia coli. In this study, we provide direct evidence that ChvE specifically binds the vir gene-inducing sugar o-glucose with high affinity. Furthermore, ChvE mutations resulting in altered vir gene expression phenotypes have been isolated and characterized. Three distinct categories of mutants have been identified. Strains expressing the first class are defective in both virulence and D-glucose utilization as a result of mutations to residues lining the sugar-binding cleft. Strains expressing a second class of mutants are not adversely affected in sugar binding but are defective in virulence, presumably due to impaired interactions with the sensor kinase VirA. A subset of this second class of mutants includes variants of ChvE that also result in defective sugar utilization. We propose that these mutations affect not only interactions with VirA but also interactions with a sugar transport system. Examination of a homology model of ChvE shows that the mutated residues associated with the latter two phenotypes lie in two overlapping solvent-exposed sites adjacent to the sugar-binding cleft where conformational changes associated with the binding of sugar might have a maximal effect on ChvE's interactions with its distinct protein partners.

The M2 protein from influenza A virus is a pH-activated proton channel that mediates acidification of the interior of viral particles entrapped in endosomes. M2 is the target of the [...]

The spin labeling EPR spectroscopy was used to study the 38-residue M2 peptide from influenza A, a pH-activated proton channel that play a crucial role in viral life cycle and serves as a drug target. The M2 peptide spanning the transmembrane region and its C-terminal extension forms a membrane surface helix at both high and low pH, though the arrangement of the monomers within the tetramer changes with pH.

Identifying determinants of broadly neutralizing antibodies against hepatitis C virus (HCV) may guide HCV vaccine design. Here, the authors discover new anti-HCV antibodies using computational screening and analyze the amino acid composition and sequence-structure relationships in this antibody family.

Abstract Background Recent advances in DNA sequencing technologies have enabled significant leaps in capacity to generate large volumes of DNA sequence data, which has spurred a rapid growth in the use of bioinformatics as a means of interrogating antibody variable gene repertoires. Common tools used for annotation of antibody sequences are often limited in functionality, modularity and usability. Results We have developed PyIR, a Python wrapper and library for IgBLAST, which offers a minimal setup CLI and API, FASTQ support, file chunking for large sequence files, JSON and Python dictionary output, and built-in sequence filtering. Conclusions PyIR offers improved processing speed over multithreaded IgBLAST (version 1.14) when spawning more than 16 processes on a single computer system. Its customizable filtering and data encapsulation allow it to be adapted to a wide range of computing environments. The API allows for IgBLAST to be used in customized bioinformatics workflows.

Coronavirus disease 2019 (COVID-19) is an evolving global public health crisis in need of therapeutic options. Passive immunization of monoclonal antibodies (mAbs) represents a promising therapeutic strategy capable of conferring immediate protection from SARS-CoV-2 infection. Herein, we describe the discovery and characterization of neutralizing SARS-CoV-2 IgG and VHH antibodies from four large-scale phage libraries. Each library was constructed synthetically with shuffled complementarity-determining region loops from natural llama and human antibody repertoires. While most candidates targeted the receptor-binding domain of the S1 subunit of SARS-CoV-2 spike protein, we also identified a neutralizing IgG candidate that binds a unique epitope on the N-terminal domain. A select number of antibodies retained binding to SARS-CoV-2 variants Alpha, Beta, Gamma, Kappa and Delta. Overall, our data show that synthetic phage libraries can rapidly yield SARS-CoV-2 S1 antibodies with therapeutically desirable features, including high affinity, unique binding sites, and potent neutralizing activity in vitro, and a capacity to limit disease in vivo.

Abstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as “repertoire fingerprinting.” We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.

Abstract Background Carbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures. The covalent linkage of a carbohydrate to the nitrogen atom of an asparagine, a process referred to as N-linked glycosylation, plays an important role in the physiology of many living organisms. Most software for glycan modeling on a personal desktop computer requires knowledge of molecular dynamics to interface with specialized programs such as CHARMM or AMBER. There are a number of popular web-based tools that are available for modeling glycans (e.g., GLYCAM-WEB (http://https://dev.glycam.org/gp/) or Glycosciences.db (http://www.glycosciences.de/)). However, these web-based tools are generally limited to a few canonical glycan conformations and do not allow the user to incorporate glycan modeling into their protein structure modeling workflow. Results Here, we present Glycosylator, a Python framework for the identification, modeling and modification of glycans in protein structure that can be used directly in a Python script through its application programming interface (API) or through its graphical user interface (GUI). The GUI provides a straightforward two-dimensional (2D) rendering of a glycoprotein that allows for a quick visual inspection of the glycosylation state of all the sequons on a protein structure. Modeled glycans can be further refined by a genetic algorithm for removing clashes and sampling alternative conformations. Glycosylator can also identify specific three-dimensional (3D) glycans on a protein structure using a library of predefined templates. Conclusions Glycosylator was used to generate models of glycosylated protein without steric clashes. Since the molecular topology is based on the CHARMM force field, new complex sugar moieties can be generated without modifying the internals of the code. Glycosylator provides more functionality for analyzing and modeling glycans than any other available software or webserver at present. Glycosylator will be a valuable tool for the glycoinformatics and biomolecular modeling communities.

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community’s Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.

It is increasingly clear that an extraordinarily diverse range of clinically important conditions—including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers—leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person’s B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.