Biology subjects

Correia, B.

Publications and source records attributed to Correia, B..

27 records · Page 2Linked to original sources

bioRxiv2022

Rational design of chemically controlled antibodies and protein therapeutics

Protein-based therapeutics such as monoclonal antibodies and cytokines are important therapies in various pathophysiological conditions such as oncology, auto-immune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely cytokine storm syndrome, organ failure and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into CTLA4, anti-HER2 antibodies or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

bioengineering↗

bioRxiv2022

Decoding Surface Fingerprints for Protein-Ligand Interactions

AO_SCPLOWBSTRACTC_SCPLOWSmall molecules have been the preferred modality for drug development and therapeutic interventions. This molecular format presents a number of advantages, e.g. long half-lives and cell permeability, making it possible to access a wide range of therapeutic targets. However, finding small molecules that engage "hard-to-drug" protein targets specifically and potently remains an arduous process, requiring experimental screening of extensive compound libraries to identify candidate leads. The search continues with further optimization of compound leads to meet the required potency and toxicity thresholds for clinical applications. Here, we propose a new computational workflow for high-throughput fragment-based screening and binding affinity prediction where we leverage the available protein-ligand complex structures using a state-of-the-art protein surface embedding framework (dMaSIF). We developed a tool capable of finding suitable ligands and fragments for a given protein pocket solely based on protein surface descriptors, that capture chemical and geometric features of the target pocket. The identified fragments can be further combined into novel ligands. Using the structural data, our ligand discovery pipeline learns the signatures of interactions between surface patches and small pharmacophores. On a query target pocket, the algorithm matches known target pockets and returns either potential ligands or identifies multiple ligand fragments in the binding site. Our binding affinity predictor is capable of predicting the affinity of a given protein-ligand pair, requiring only limited information about the ligand pose. This enables screening without the costly step of first docking candidate molecules. Our framework will facilitate the design of ligands based on the targets surface information. It may significantly reduce the experimental screening load and ultimately reveal novel chemical compounds for targeting challenging proteins.

bioinformatics↗

bioRxiv2021

A deep mutational scanning platform to characterize the fitness landscape of anti-CRISPR proteins

Deep mutational scanning is a powerful method to explore the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, would facilitate their in-depth characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli combining synthetic gene circuits based on CRISPR interference with flow cytometry-coupled sequencing and mathematical modeling. Using this pipeline, we created and characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of Streptococcus pyogenes Cas9. The resulting mutational fitness landscapes revealed that both Acrs possess a considerable mutational tolerance as well as an intrinsic redundancy with respect to Cas9 inhibitory features, suggesting evolutionary pressure towards high plasticity and robustness. Finally, to demonstrate that our pipeline can inform the optimization and fine-tuning of Acrs for genome editing applications, we cross-validated a subset of AcrIIA4 mutants via gene editing assays in mammalian cells and in vitro affinity measurements. Together, our work establishes deep mutational scanning as powerful method for anti-CRISPR protein characterization and optimization.

synthetic biology↗

bioRxiv2021

A rational blueprint for the design of chemically-controlled protein switches

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches for basic and translational applications.

biochemistry↗

bioRxiv2020

Fast end-to-end learning on protein surfaces

Proteins biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science. In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features. To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.

bioinformatics↗

bioRxiv2020

Immune response to vaccine candidates based on different types of nanoscaffolded RBD domain of the SARS-CoV-2 spike protein

Effective and safe vaccines against SARS-CoV-2 are highly desirable to prevent casualties and societal cost caused by Covid-19 pandemic. The receptor binding domain (RBD) of the surface-exposed spike protein of SARS-CoV-2 represents a suitable target for the induction of neutralizing antibodies upon vaccination. Small protein antigens typically induce weak immune response while particles measuring tens of nanometers are efficiently presented to B cell follicles and subsequently to follicular germinal center B cells in draining lymph nodes, where B cell proliferation and affinity maturation occurs. Here we prepared and analyzed the response to several DNA vaccines based on genetic fusions of RBD to four different scaffolding domains, namely to the foldon peptide, ferritin, lumazine synthase and {beta}-annulus peptide, presenting from 6 to 60 copies of the RBD on each particle. Scaffolding strongly augmented the immune response with production of neutralizing antibodies and T cell response including cytotoxic lymphocytes in mice upon immunization with DNA plasmids. The most potent response was observed for the 24-residue {beta}-annulus peptide scaffold that forms large soluble assemblies, that has the advantage of low immunogenicity in comparison to larger scaffolds. Our results support the advancement of this vaccine platform towards clinical trials.

immunology↗

bioRxiv2020

A bottom-up approach for the de novo design of functional proteins

De novo protein design has enabled the creation of novel protein structures. To design novel functional proteins, state-of-the-art approaches use natural proteins or first design protein scaffolds that subsequently serve as templates for the transplantation of functional motifs. In these approaches, the templates are function-agnostic and motifs have been limited to those with regular secondary structure. Here, we present a bottom-up approach to build de novo proteins tailored to structurally complex functional motifs. We applied a bottom-up strategy to design scaffolds for four different binding motifs, including one bi-functionalized protein with two motifs. The de novo proteins were functional as biosensors to quantify epitope-specific antibody responses and as orthogonal ligands to activate a signaling pathway in engineered mammalian cells. Altogether, we present a versatile strategy for the bottom-up design of functional proteins, applicable to a wide range of functional protein design challenges.

bioengineering↗

bioRxiv2019

Trivalent cocktail of de novo designed immunogens enables the robust induction and focusing of functional antibodies in vivo

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 novel protein design algorithm, termed TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. Both in mice and non-human primates, cocktails of three de novo designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused pre-existing antibody responses towards defined neutralization epitopes. Overall, our de novo design approach opens the possibility of targeting specific epitopes for vaccine and therapeutic antibody development, and more generally will be applicable to design de novo proteins displaying complex functional motifs.

bioengineering↗

bioRxiv2018

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

Throughout the last 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 due to 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.

immunology↗