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.

SBILib: a handle for protein modeling and engineering.

SUMMARY: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. AVAILABILITY AND IMPLEMENTATION: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

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.

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.

RosettaSurf-A surface-centric computational design approach.

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

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.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.

BACKGROUND: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Preclinical development of a humanized chimeric antigen receptor against B cell maturation antigen for multiple myeloma.

Multiple myeloma is a prevalent and incurable disease, despite the development of new and effective drugs. The recent development of chimeric antigen receptor (CAR)-T cell therapy has shown impressive results in the treatment of patients with relapsed or refractory hematological B cell malignancies. In the recent years, B-cell maturation antigen (BCMA) has appeared as a promising antigen to target using a variety of immuno-therapy treatments including CART cells, for MM patients. To this end, we generated clinical-grade murine CART cells directed against BCMA, named ARI2m cells. Having demonstrated its efficacy, and in an attempt to avoid the immune rejection of CART cells by the patient, the single chain variable fragment was humanized, creating ARI2h cells. ARI2h cells demonstrated comparable in vitro and in vivo efficacy to ARI2m cells, and superiority in cases of high tumor burden disease. In terms of inflammatory response, ARI2h cells showed a lower TNFα production and lower in vivo toxicity profile. Large-scale expansion of both ARI2m and ARI2h cells was efficiently conducted following Good Manufacturing Practice guidelines, obtaining the target CART cell dose required for treatment of multiple myeloma patients. Moreover, we demonstrate that soluble BCMA and BCMA released in vesicles impacts on CAR-BCMA activity. In summary, this study sets the bases for the implementation of a clinical trial (EudraCT code: 2019-001472-11) to study the efficacy of ARI2h cell treatment for multiple myeloma patients.

CDK2-dependent activation of PARP-1 is required for hormonal gene regulation in breast cancer cells.

Eukaryotic gene regulation implies that transcription factors gain access to genomic information via poorly understood processes involving activation and targeting of kinases, histone-modifying enzymes, and chromatin remodelers to chromatin. Here we report that progestin gene regulation in breast cancer cells requires a rapid and transient increase in poly-(ADP)-ribose (PAR), accompanied by a dramatic decrease of cellular NAD that could have broad implications in cell physiology. This rapid increase in nuclear PARylation is mediated by activation of PAR polymerase PARP-1 as a result of phosphorylation by cyclin-dependent kinase CDK2. Hormone-dependent phosphorylation of PARP-1 by CDK2, within the catalytic domain, enhances its enzymatic capabilities. Activated PARP-1 contributes to the displacement of histone H1 and is essential for regulation of the majority of hormone-responsive genes and for the effect of progestins on cell cycle progression. Both global chromatin immunoprecipitation (ChIP) coupled with deep sequencing (ChIP-seq) and gene expression analysis show a strong overlap between PARP-1 and CDK2. Thus, progestin gene regulation involves a novel signaling pathway that connects CDK2-dependent activation of PARP-1 with histone H1 displacement. Given the multiplicity of PARP targets, this new pathway could be used for the pharmacological management of breast cancer.

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.

On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures.

Motivation: The characterization of the protein-protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein-protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. Results: We present a new approach that relies on the unbound protein structures and protein docking to predict protein-protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. Availability and implementation: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock. Contact: j.planas-iglesias@warwick.ac.uk or baldo.oliva@upf.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

Bispecific light T-cell engagers for gene-based immunotherapy of epidermal growth factor receptor (EGFR)-positive malignancies.

The recruitment of T-cells by bispecific antibodies secreted from adoptively transferred, gene-modified autologous cells has shown satisfactory results in preclinical cancer models. Even so, the approach's translation into the clinic will require incremental improvements to its efficacy and reduction of its toxicity. Here, we characterized a tandem T-cell recruiting bispecific antibody intended to benefit gene-based immunotherapy approaches, which we call the light T-cell engager (LiTE), consisting of an EGFR-specific single-domain VHH antibody fused to a CD3-specific scFv. We generated two LiTEs with the anti-EGFR VHH and the anti-CD3 scFv arranged in both possible orders. Both constructs were well expressed in mammalian cells as highly homogenous monomers in solution with molecular weights of 43 and 41 kDa, respectively. In situ secreted LiTEs bound the cognate antigens of both parental antibodies and triggered the specific cytolysis of EGFR-expressing cancer cells without inducing T-cell activation and cytotoxicity spontaneously or against EGFR-negative cells. Light T-cell engagers are, therefore, suitable for future applications in gene-based immunotherapy approaches.

Amyloid-ß Peptide Nitrotyrosination Stabilizes Oligomers and Enhances NMDAR-Mediated Toxicity.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the pathological aggregation of the amyloid-ß peptide (Aß). Monomeric soluble Aß can switch from helicoidal to ß-sheet conformation, promoting its assembly into oligomers and subsequently to amyloid fibrils. Oligomers are highly toxic to neurons and have been reported to induce synaptic transmission impairments. The progression from oligomers to fibrils forming senile plaques is currently considered a protective mechanism to avoid the presence of the highly toxic oligomers. Protein nitration is a frequent post-translational modification under AD nitrative stress conditions. Aß can be nitrated at tyrosine 10 (Y10) by peroxynitrite. Based on our analysis of ThT binding, Western blot and electron and atomic force microscopy, we report that Aß nitration stabilizes soluble, highly toxic oligomers and impairs the formation of fibrils. We propose a mechanism by which fibril elongation is interrupted upon Y10 nitration: Nitration disrupts fibril-forming folds by preventing H14-mediated bridging, as shown with an Aß analog containing a single residue (H to E) replacement that mimics the behavior of nitrated Aß related to fibril formation and neuronal toxicity. The pathophysiological role of our findings in AD was highlighted by the study of these nitrated oligomers on mouse hippocampal neurons, where an increased NMDAR-dependent toxicity of nitrated Aß oligomers was observed. Our results show that Aß nitrotyrosination is a post-translational modification that increases Aß synaptotoxicity. SIGNIFICANCE STATEMENT: We report that nitration (i.e., the irreversible addition of a nitro group) of the Alzheimer-related peptide amyloid-ß (Aß) favors the stabilization of highly toxic oligomers and inhibits the formation of Aß fibrils. The nitrated Aß oligomers are more toxic to neurons due to increased cytosolic calcium levels throughout their action on NMDA receptors. Sustained elevated calcium levels trigger excitotoxicity, a characteristic event in Alzheimer's disease.

Targeting Importin-α7 as a Therapeutic Approach against Pandemic Influenza Viruses.

Viral drug resistance is believed to be less likely to occur if compounds are directed against cellular rather than viral proteins. In this study, we analyzed the feasibility of a crucial viral replication factor, namely, importin-α7, as a cellular drug target to combat pandemic influenza viruses. Surprisingly, only five viral lung-to-lung passages were required to achieve 100% lethality in importin-α7⁻/⁻ mice that otherwise are resistant. Viral escape from importin-α7 requirement was mediated by five mutations in the viral ribonucleoprotein complex and the surface glycoproteins. Moreover, the importin-α7⁻/⁻ mouse-adapted strain became even more virulent for wild-type mice than the parental strain. These studies show that targeting host proteins may still result in viral escape by alternative pathways, eventually giving rise to even more virulent virus strains. Thus, therapeutic intervention strategies should consider a multitarget approach to reduce viral drug resistance. IMPORTANCE Here, we investigated the long-standing hypothesis based on in vitro studies that viral drug resistance occurrence is less likely if compounds are directed against cellular rather than viral proteins. Here, we challenged this hypothesis by analyzing, in an in vivo animal model, the feasibility of targeting the cellular factor importin-α7, which is crucial for human influenza virus replication and pathogenesis, as an efficient antiviral strategy against pandemic influenza viruses. In summary, our studies suggest that resistance against cellular factors is possible in vivo, and the emergence of even more virulent viral escape variants calls for particular caution. Thus, therapeutic intervention strategies should consider a multitarget approach using compounds against viral as well as cellular factors to reduce the risk of viral drug resistance and potentially increased virulence.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research.

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

ArchDB 2014: structural classification of loops in proteins.

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. ß-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability--raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.

Frag'r'Us: knowledge-based sampling of protein backbone conformations for de novo structure-based protein design.

MOTIVATION: The remodeling of short fragment(s) of the protein backbone to accommodate new function(s), fine-tune binding specificities or change/create novel protein interactions is a common task in structure-based computational design. Alternative backbone conformations can be generated de novo or by redeploying existing fragments extracted from protein structures i.e. knowledge-based. We present Frag'r'Us, a web server designed to sample alternative protein backbone conformations in loop regions. The method relies on a database of super secondary structural motifs called smotifs. Thus, sampling of conformations reflects structurally feasible fragments compiled from existing protein structures. Availability and implementation Frag'r'Us has been implemented as web application and is available at http://www.bioinsilico.org/FRAGRUS.

iLoops: a protein-protein interaction prediction server based on structural features.

SUMMARY: Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier. AVAILABILITY: iLoops is available at http://sbi.imim.es/iLoops.php CONTACT: baldo.oliva@upf.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.