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

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

Expanding beyond the natural protein repertoire to engineer targeted vaccines and diagnostics

Proteins have evolved over millions of years to carry out the vast majority of biological functions that are fundamental to life. Their three-dimensional structures and functions have been in the focus of biomedical research for many decades, and substantial progress has been made in understanding pro-tein folding and structure-function relationships. The inverse problem of protein folding is called protein design ¿ the design of novel protein sequenc-es that fold into predefined three-dimensional structures. Towards this aim, computational tools have emerged as a powerful tool for the de novo design of proteins with structural and biophysical proper-ties that are not found in nature. To date, however, the vast majority of de novo designed proteins has been deprived of biological functions. Recently, the de novo design of proteins with customized mo-lecular and biological functions has gained momentum, aiming to exploit them to tackle outstanding biotechnological and biomedical challenges of the 21st century. One of these grand biomedical challenges that could be transformed by de novo protein design is the design of novel and more effective vaccines, especially for pathogens where traditional approaches for vaccine development have failed. Among these pathogens is the respiratory syncytial virus (RSV), which causes severe lower respiratory tract infections in young children and the elderly. Recently, numerous broadly protective, RSV neutralizing antibodies (nAbs) have been isolated from humans, and their structural characterization in complex with their target epitopes has greatly improved our mo-lecular understanding of an effective nAb response. A remaining challenge is the design of immuno-gens that effectively spotlight these antigenic sites, and elicit targeted nAb responses in vivo. My thesis work leverages de novo protein design for the design of epitope-focused immunogens that induce nAbs in vivo. Strikingly, we show how a cocktail of three de novo designed immunogens pre-senting selected neutralization epitopes elicit RSV nAbs in non-human primates. Furthermore, the designed immunogens bear unique potential as boosting immunogens in non-naïve subjects, allowing the focusing of nAbs onto defined antigenic sites. Together, these represent a substantial step forward for the use of immunogens based on computationally designed proteins, and offers a roadmap to em-ploy computational protein design in the engineering of precision immunogens for other pathogens. From a protein design perspective, my work introduces a `bottom-up¿ approach towards the de novo design of functional proteins. The bottom-up approach presents a general computational protocol to build de novo proteins with embedded binding motifs, including those that are structurally irregular or discontinuous, i.e. consist of multiple segments. Beyond immunogens, we exploit the designed proteins as biosensors to detect and quantify epitope-specific antibody responses, providing a practi-cal diagnostic tool to enable high-resolution immune monitoring. Altogether, my thesis showcases a versatile, function-centric de novo protein design approach, appli-cable to address challenges including, but not limited to, the design of immunogens and antibody biosensors

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

Immunogen

Polypeptides useful in the preparation of vaccine compositions against RSV are provided. Also disclosed are methods of enhancing subdominant antibody responses in a subject

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

BackgroundLarge-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.ResultsHere, 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.Conclusionsrstoolbox 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

The Physiological Landscape and Specificity of Antibody Repertoires

Diverse antibody repertoires spanning multiple lymphoid organs (e.g., bone marrow, spleen, lymph nodes) form the foundation of protective humoral immunity. Changes in their composition across lymphoid organs are a consequence of B-cell selection and migration events leading to a highly dynamic and unique physiological landscape of antibody repertoires upon antigenic challenge (e.g., vaccination). However, to what extent B cells encoding identical or similar antibody sequences (clones) are distributed across multiple lymphoid organs and how this is shaped by the strength of a humoral response, remains largely unexplored. Here, we performed an in-depth systems analysis of antibody repertoires across multiple distinct lymphoid organs of immunized mice, and discovered that organ-specific antibody repertoire features (e.g., germline V-gene usage and clonal expansion profiles) equilibrated upon a strong humoral response (multiple immunizations and high serum titers). This resulted in a surprisingly high degree of repertoire consolidation, characterized by highly connected and overlapping B-cell clones across multiple lymphoid organs. Finally, we revealed distinct physiological axes indicating clonal migrations and showed that antibody repertoire consolidation directly correlated with antigen-specificity. Our study uncovered how a strong humoral response resulted in a more uniform but redundant physiological landscape of antibody repertoires, indicating that increases in antibody serum titers were a result of synergistic contributions from antigen-specific B-cell clones distributed across multiple lymphoid organs. Our findings provide valuable insights for the assessment and design of vaccine strategies

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