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.

A PD-L1/EGFR bispecific antibody combines immune checkpoint blockade and direct anti-cancer action for an enhanced anti-tumor response.

Immune checkpoint blockade (ICB) with antibodies has shown durable clinical responses in a wide range of cancer types, but the overall response rate is still limited. Other effective therapeutic modalities to increase the ICB response rates are urgently needed. New bispecific antibody (bsAb) formats combining the ICB effect and a direct action on cancer cells could improve the efficacy of current immunotherapies. Here, we report the development of a PD-L1/EGFR symmetric bsAb by fusing a dual-targeting tandem trimmer body with the human IgG1 hinge and Fc regions. The bsAb was characterized in vitro and the antitumor efficacy was evaluated in humanized mice bearing xenografts of aggressive triple-negative breast cancer and lung cancer. The IgG-like hexavalent bsAb, designated IgTT-1E, was able to simultaneously bind both EGFR and PD-L1 antigens, inhibit EGF-mediated proliferation, effectively block PD-1/PD-L1 interaction, and induce strong antigen-specific antibody-dependent cellular cytotoxicity activity in vitro. Potent therapeutic efficacies of IgTT-1E in two different humanized mouse models were observed, where tumor growth control was associated with a significantly increased proportion of CD8+ T cells. These results support the development of IgTT-1E for the treatment of EGFR+ cancers.

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.

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.

Mutational Hotspot in the SARS-CoV-2 Spike Protein N-Terminal Domain Conferring Immune Escape Potential.

Global efforts are being made to monitor the evolution of SARS-CoV-2, aiming for early identification of genotypes providing increased infectivity or virulence. However, viral lineage-focused tracking might fail in early detection of advantageous mutations emerging independently across phylogenies. Here, the emergence patterns of Spike mutations were investigated in sequences deposited in local and global databases to identify mutational hotspots across phylogenies and we evaluated their impact on SARS-CoV-2 evolution. We found a striking increase in the frequency of recruitment of diverse substitutions at a critical residue (W152), positioned in the N-terminal domain (NTD) of the Spike protein, observed repeatedly across independent phylogenetic and geographical contexts. These mutations might have an impact on the evasion of neutralizing antibodies. Finally, we found that NTD is a region exhibiting particularly high frequency of mutation recruitments, suggesting an evolutionary path in which the virus maintains optimal efficiency of ACE2 binding combined with the flexibility facilitating the immune escape. We conclude that adaptive mutations, frequently present outside of the receptor-binding domain, can emerge in virtually any SARS-CoV-2 lineage and at any geographical location. Therefore, surveillance should not be restricted to monitoring defined lineages alone.

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction.

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30-50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures.

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.

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/ .

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.

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.

Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions.

Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.

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.

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.

Author Correction: A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy.

Approaches to increase the activity of chimeric antigen receptor (CAR)-T cells against solid tumors may also increase the risk of toxicity and other side effects. To improve the safety of CAR-T-cell therapy, we computationally designed a chemically disruptable heterodimer (CDH) based on the binding of two human proteins. The CDH self-assembles, can be disrupted by a small-molecule drug and has a high-affinity protein interface with minimal amino acid deviation from wild-type human proteins. We incorporated the CDH into a synthetic heterodimeric CAR, called STOP-CAR, that has an antigen-recognition chain and a CD3ζ- and CD28-containing endodomain signaling chain. We tested STOP-CAR-T cells specific for two antigens in vitro and in vivo and found similar antitumor activity compared to second-generation (2G) CAR-T cells. Timed administration of the small-molecule drug dynamically inactivated the activity of STOP-CAR-T cells. Our work highlights the potential for structure-based design to add controllable elements to synthetic cellular therapies.

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF.

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences. We have computed theoretical position weight matrices (PWMs) of proteins composed by C2H2-ZF domains, with the only requirement of an input structure. We have predicted more than two-third of a single zinc-finger domain binding site for about 70% variants of Zif268, a classical member of this family. We have successfully matched between 60 and 90% of the binding-site motif of examples of proteins composed by three C2H2-ZF domains in JASPAR, a standard database of PWMs. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF. We offer a server to model the structure of a zinc-finger protein and predict its PWM.

Human Albumin Impairs Amyloid ß-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid ß-peptide (Aß), which induces neuronal death. Monomeric Aß is not toxic but tends to aggregate into ß-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aß assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aß impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aß, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aß1-42 simulations was significantly lower than that of the clusterin-Aß1-42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 µM Aß1-42 incubated with 5 µM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aß1-42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aß1-42 incubated with CTerm obtaining a significant protection against Aß-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aß aggregation and to promote disassemble of Aß aggregates protecting neurons.

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.

Alternative interaction sites in the influenza A virus nucleoprotein mediate viral escape from the importin-α7 mediated nuclear import pathway.

Influenza A viruses are able to adapt to restrictive conditions due to their high mutation rates. Importin-α7 is a component of the nuclear import machinery required for avian-mammalian adaptation and replicative fitness in human cells. Here, we elucidate the mechanisms by which influenza A viruses may escape replicative restriction in the absence of importin-α7. To address this question, we assessed viral evolution in mice lacking the importin-α7 gene. We show that three mutations in particular occur with high frequency in the viral nucleoprotein (NP) protein (G102R, M105K and D375N) in a specific structural area upon in vivo adaptation. Moreover, our findings suggest that the adaptive NP mutations mediate viral escape from importin-α7 requirement likely due to the utilization of alternative interaction sites in NP beyond the classical nuclear localization signal. However, viral escape from importin-α7 by mutations in NP is, at least in part, associated with reduced viral replication highlighting the crucial contribution of importin-α7 to replicative fitness in human cells.

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.