Biology subjects

Correia, B. E.

Publications and source records attributed to Correia, B. E..

28 records · Page 2Linked to original sources

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗

bioRxiv2022

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 to 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 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.

bioinformatics↗

bioRxiv2021

Assessing and enhancing foldability in designed proteins

Recent advances in protein-design methodology have led to a dramatic increase in reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major reason for design failure is inaccuracy and misfolding relative to the design model. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs (available on a web server https://pSUFER.weizmann.ac.il); second, we demonstrate that AlphaFold2 ab initio structure prediction flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme improving its catalytic efficiency by 330 fold. Furthermore, applied to a de novo designed protein that exhibited limited stability, the same approach markedly improved stability and expressibility. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

biochemistry↗

bioRxiv2021

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.

immunology↗

bioRxiv2021

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. Author SummaryFinely orchestrated protein-protein interactions are at the heart of virtually all fundamental cellular processes. Altering these processes or encoding new functions in proteins has been a long-standing goal in computational protein design. Protein design methods commonly rely on scoring functions that seek to identify amino acid sequences that optimize structural configurations of atoms while minimizing a variety of physics-based and statistical terms. The objectives of the large majority of computational design protocols have been focused on obtaining a predefined structural conformation. However, routinely introducing a functional aspect on designer proteins has been more challenging. Our results suggest that the molecular surface features can be a useful optimization parameter to guide the design process towards functional surfaces that mimic known protein binding sites and interact with their intended targets. Specifically, we demonstrate that our design method can optimize experimental libraries through computational screening, creating a basis for highly specific protein binders, as well as design a potent immunogen that engages with site-specific antibodies. The ability to create proteins with novel functions will be transformative for biomedical applications, providing many opportunities for the design of novel immunogens, protein components for synthetic biology, and other protein-based biotechnologies.

bioinformatics↗

bioRxiv2019

Optogenetic control of Neisseria meningitidis Cas9 genome editing using an engineered, light-switchable anti-CRISPR protein

Optogenetic control of CRISPR-Cas9 systems has significantly improved our ability to perform genome perturbations in living cells with high precision in time and space. As new Cas orthologues with advantageous properties are rapidly being discovered and engineered, the need for straightforward strategies to control their activity via exogenous stimuli persists. The Cas9 from Neisseria meningitidis (Nme) is a particularly small and target-specific Cas9 orthologue, and thus of high interest for in vivo genome editing applications. Here, we report the first optogenetic tool to control NmeCas9 activity in mammalian cells via an engineered, light-dependent anti-CRISPR (Acr) protein. Building on our previous Acr engineering work, we created hybrids between the NmeCas9 inhibitor AcrIIC3 and the LOV2 blue light sensory domain from Avena sativa. Two AcrIIC3-LOV2 hybrids from our collection potently blocked NmeCas9 activity in the dark, while permitting robust genome editing at various endogenous loci upon blue light irradiation. Structural analysis revealed that, within these hybrids, the LOV2 domain is located in striking proximity to the Cas9 binding surface. Together, our work demonstrates optogenetic regulation of a type II-C CRISPR effector and might suggest a new route for the design of optogenetic Acrs.

synthetic biology↗

bioRxiv2019

Computational design of anti-CRISPR proteins with improved inhibition potency and expanded specificity

Anti-CRISPR (Acr) proteins are bacteriophage-derived antagonists of CRISPR-Cas systems. To date, Acrs were obtained either by mining sequence databanks or experimentally screening phage collections, both of which yield a limited repertoire of naturally occurring variants. Here, we applied structure-based engineering on AcrIIC1, a broad-spectrum inhibitor of type II-C CRISPR systems, to improve its efficacy and expand its specificity. We first show that fusing exogenous protein domains into AcrIIC1 dramatically enhances inhibition of the natural Neisseria meningitidis Cas9 target. Then, using structure-guided design, we converted AcrIIC1 into AcrX, a potent inhibitor of the type II-A CRISPR-Cas9 from Staphylococcus aureus widely applied for in vivo genome editing. Our work introduces designer Acrs as important biotechnological tools and provides an innovative strategy to safeguard the CRISPR technology.

synthetic biology↗

bioRxiv2019

Deciphering interaction fingerprints from protein molecular surfaces

Predicting interactions between proteins and other biomolecules purely based on structure is an unsolved problem in biology. A high-level description of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a proteins modes of interactions with other biomolecules. We hypothesize that proteins performing similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF, a conceptual framework based on a new geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction, and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

bioinformatics↗

bioRxiv2018

rstoolbox: management and analysis of computationally designed structural ensembles.

MotivationComputational protein design (CPD) calculations rely on the generation of large amounts of data on the search for the best sequences. As such, CPD workflows generally include the batch generation of designed decoys (sampling) followed by ranking and filtering stages to select those with optimal metrics (scoring). Due to these factors, the proper analysis of the decoy population is a key element for the effective selection of designs for experimental validation.\n\nResultsHere, we present a set of tools for the analysis of protein design ensembles. The tool is oriented towards protein designers with basic coding training aiming to process efficiently their decoy sets as well as for protocol developers interested in benchmarking their new approaches. Although initially devised to process Rosetta design outputs, the library is extendable to other design tools.\n\nAvailability and Implementationrstoolbox is implemented for python2.7 and 3.5+. Code is freely available at https://github.com/lpdi-epfl/rstoolbox under the MIT license. Full documentation and examples can be found at https://lpdi-epfl.github.io/rstoolbox.

bioinformatics↗

bioRxiv2018

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, nevertheless, it remains a challenge for state-of-the-art methodologies. Here, we present a computational design approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins. We performed extensive benchmarks, where the most unexpected finding was that the design of function into proteins may not necessarily reside in the global minimum of the energetic landscape, which could have important implications in the field. We have computationally designed and experimentally characterized a distant structural template and a de novo \"functionless\" fold, two prototypical design challenges, to present important viral epitopes. Overall, we present an accessible strategy to repurpose old protein folds for new functions, which may lead to important improvements on the computational design of functional proteins.

bioinformatics↗