Vaccination induces broadly neutralizing antibody precursors to HIV gp41.

A key barrier to the development of vaccines that induce broadly neutralizing antibodies (bnAbs) against human immunodeficiency virus (HIV) and other viruses of high antigenic diversity is the design of priming immunogens that induce rare bnAb-precursor B cells. The high neutralization breadth of the HIV bnAb 10E8 makes elicitation of 10E8-class bnAbs desirable; however, the recessed epitope within gp41 makes envelope trimers poor priming immunogens and requires that 10E8-class bnAbs possess a long heavy chain complementarity determining region 3 (HCDR3) with a specific binding motif. We developed germline-targeting epitope scaffolds with affinity for 10E8-class precursors and engineered nanoparticles for multivalent display. Scaffolds exhibited epitope structural mimicry and bound bnAb-precursor human naive B cells in ex vivo screens, protein nanoparticles induced bnAb-precursor responses in stringent mouse models and rhesus macaques, and mRNA-encoded nanoparticles triggered similar responses in mice. Thus, germline-targeting epitope scaffold nanoparticles can elicit rare bnAb-precursor B cells with predefined binding specificities and HCDR3 features.

Tailored Immunogens Direct Affinity Maturation toward HIV Neutralizing Antibodies.

Induction of broadly neutralizing antibodies (bnAbs) is a primary goal of HIV vaccine development. VRC01-class bnAbs are important vaccine leads because their precursor B cells targeted by an engineered priming immunogen are relatively common among humans. This priming immunogen has demonstrated the ability to initiate a bnAb response in animal models, but recall and maturation toward bnAb development has not been shown. Here, we report the development of boosting immunogens designed to guide the genetic and functional maturation of previously primed VRC01-class precursors. Boosting a transgenic mouse model expressing germline VRC01 heavy chains produced broad neutralization of near-native isolates (N276A) and weak neutralization of fully native HIV. Functional and genetic characteristics indicate that the boosted mAbs are consistent with partially mature VRC01-class antibodies and place them on a maturation trajectory that leads toward mature VRC01-class bnAbs. The results show how reductionist sequential immunization can guide maturation of HIV bnAb responses.

Systematic identification of structure-specific protein-protein interactions.

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

A generic framework for hierarchical de novo protein design.

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

Bottom-up de novo design of functional proteins with complex structural features.

De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs. We applied the bottom-up strategy to successfully design five folds for four distinct binding motifs, including a bifunctionalized protein with two motifs. Crystal structures confirmed the atomic-level accuracy of the computational designs. These de novo proteins were functional as components of biosensors to monitor antibody responses and as orthogonal ligands to modulate synthetic signaling receptors in engineered mammalian cells. Our work demonstrates the potential of bottom-up approaches to accommodate complex structural motifs, which will be essential to endow de novo proteins with elaborate biochemical functions, such as molecular recognition or catalysis.

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen.

Throughout the last several decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g., respiratory syncytial virus [RSV], influenza, dengue, and others) have resisted vaccine development efforts, largely because of the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics.

BACKGROUND: Large-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function. Computational protein design (CPD) has emerged as an important structure-based approach to engineer proteins for novel functions. Generally, CPD workflows rely on the generation of large numbers of structural models to search for the optimal structure-sequence configurations. As such, an important step of the CPD process is the selection of a small subset of sequences to be experimentally characterized. Given the limitations of current CPD scoring functions, multi-step design protocols and elaborated analysis of the decoy populations have become essential for the selection of sequences for experimental characterization and the success of CPD strategies. RESULTS: Here, we present the rstoolbox, a Python library for the analysis of large-scale structural data tailored for CPD applications. rstoolbox is oriented towards both CPD software users and developers, being easily integrated in analysis workflows. For users, it offers the ability to profile and select decoy sets, which may guide multi-step design protocols or for follow-up experimental characterization. rstoolbox provides intuitive solutions for the visualization of large sequence/structure datasets (e.g. logo plots and heatmaps) and facilitates the analysis of experimental data obtained through traditional biochemical techniques (e.g. circular dichroism and surface plasmon resonance) and high-throughput sequencing. For CPD software developers, it provides a framework to easily benchmark and compare different CPD approaches. Here, we showcase the rstoolbox in both types of applications. CONCLUSIONS: rstoolbox is a library for the evaluation of protein structures datasets tailored for CPD data. It provides interactive access through seamless integration with IPython, while still being suitable for high-performance computing. In addition to its functionalities for data analysis and graphical representation, the inclusion of rstoolbox in protein design pipelines will allow to easily standardize the selection of design candidates, as well as, to improve the overall reproducibility and robustness of CPD selection processes.

Rosetta FunFolDes - A general framework for the computational design of functional proteins.

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.

Minimally Mutated HIV-1 Broadly Neutralizing Antibodies to Guide Reductionist Vaccine Design.

An optimal HIV vaccine should induce broadly neutralizing antibodies (bnAbs) that neutralize diverse viral strains and subtypes. However, potent bnAbs develop in only a small fraction of HIV-infected individuals, all contain rare features such as extensive mutation, insertions, deletions, and/or long complementarity-determining regions, and some are polyreactive, casting doubt on whether bnAbs to HIV can be reliably induced by vaccination. We engineered two potent VRC01-class bnAbs that minimized rare features. According to a quantitative features frequency analysis, the set of features for one of these minimally mutated bnAbs compared favorably with all 68 HIV bnAbs analyzed and was similar to antibodies elicited by common vaccines. This same minimally mutated bnAb lacked polyreactivity in four different assays. We then divided the minimal mutations into spatial clusters and dissected the epitope components interacting with those clusters, by mutational and crystallographic analyses coupled with neutralization assays. Finally, by synthesizing available data, we developed a working-concept boosting strategy to select the mutation clusters in a logical order following a germline-targeting prime. We have thus developed potent HIV bnAbs that may be more tractable vaccine goals compared to existing bnAbs, and we have proposed a strategy to elicit them. This reductionist approach to vaccine design, guided by antibody and antigen structure, could be applied to design candidate vaccines for other HIV bnAbs or protective Abs against other pathogens.

Correction: Minimally Mutated HIV-1 Broadly Neutralizing Antibodies to Guide Reductionist Vaccine Design.

[This corrects the article DOI: 10.1371/journal.ppat.1005815.].

Protein destabilization underlies pathogenic missense mutations in ARID1B.

ARID1B is a SWI/SNF subunit frequently mutated in human Coffin-Siris syndrome (CSS) and it is necessary for proliferation of ARID1A mutant cancers. While most CSS ARID1B aberrations introduce frameshifts or stop codons, the functional consequence of missense mutations found in ARID1B is unclear. We here perform saturated mutagenesis screens on ARID1B and demonstrate that protein destabilization is the main mechanism associated with pathogenic missense mutations in patients with Coffin-Siris Syndrome.

mRNA Display Identifies Potent, Paralog-Selective Peptidic Ligands for ARID1B.

The ARID1A and ARID1B subunits are mutually exclusive components of the BAF variant of SWI/SNF chromatin remodeling complexes. Loss of function mutations in ARID1A are frequently observed in various cancers, resulting in a dependency on the paralog ARID1B for cancer cell proliferation. However, ARID1B has never been targeted directly, and the high degree of sequence similarity to ARID1A poses a challenge for the development of selective binders. In this study, we used mRNA display to identify peptidic ligands that bind with nanomolar affinities to ARID1B and showed high selectivity over ARID1A. Using orthogonal biochemical, biophysical, and chemical biology tools, we demonstrate that the peptides engage two different binding pockets, one of which directly involves an ARID1B-exclusive cysteine that could allow covalent targeting by small molecules. Our findings impart the first evidence of the ligandability of ARID1B, provide valuable tools for drug discovery, and suggest opportunities for the development of selective molecules to exploit the synthetic lethal relationship between ARID1A and ARID1B in cancer.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications.

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.

Global, in situ analysis of the structural proteome in individuals with Parkinson's disease to identify a new class of biomarker.

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis-mass spectrometry (LiP-MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein α-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes.

De novo protein design enables the precise induction of RSV-neutralizing antibodies.

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a protein design algorithm called TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. In both mice and nonhuman primates, cocktails of three de novo-designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused preexisting antibody responses toward defined neutralization epitopes. Overall, our design approach opens the possibility of targeting specific epitopes for the development of vaccines and therapeutic antibodies and, more generally, will be applicable to the design of de novo proteins displaying complex functional motifs.

Structure-based immunogen design-leading the way to the new age of precision vaccines.

Vaccines have been one of the most successful interventions in global health. However, traditional vaccine development has proven insufficient to deal with pathogens that elude the immune system through highly variable and non-functional epitopes. Emerging B cell technologies have yielded potent monoclonal antibodies targeting conserved epitopes, and their structural characterization has provided templates for rational immunogen design. Here, we review immunogen design strategies that leverage structural information to steer bulk immune responses towards the induction of precise antibody specificities targeting key antigenic sites. Immunogens designed to elicit well-defined antibody responses will become the basis of what we dubbed precision vaccines. Such immunogens have been used to tackle long-standing vaccine problems and have demonstrated their potential to seed the next-generation of vaccines.

HIV-1 broadly neutralizing antibody precursor B cells revealed by germline-targeting immunogen.

Induction of broadly neutralizing antibodies (bnAbs) is a major HIV vaccine goal. Germline-targeting immunogens aim to initiate bnAb induction by activating bnAb germline precursor B cells. Critical unmet challenges are to determine whether bnAb precursor naïve B cells bind germline-targeting immunogens and occur at sufficient frequency in humans for reliable vaccine responses. Using deep mutational scanning and multitarget optimization, we developed a germline-targeting immunogen (eOD-GT8) for diverse VRC01-class bnAbs. We then used the immunogen to isolate VRC01-class precursor naïve B cells from HIV-uninfected donors. Frequencies of true VRC01-class precursors, their structures, and their eOD-GT8 affinities support this immunogen as a candidate human vaccine prime. These methods could be applied to germline targeting for other classes of HIV bnAbs and for Abs to other pathogens.