Search

Many pathogenic viruses, including influenza virus, Ebola virus, coronaviruses, and Pneumoviruses, rely on class I fusion proteins to fuse viral and cellular membranes. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more favorable and stable postfusion state. An increasing amount of evidence exists highlighting that antibodies targeting the prefusion conformation are the most potent. However, many mutations have to be evaluated before identifying prefusion-stabilizing substitutions. We therefore established a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. As a proof of concept, we applied this principle to the fusion protein of the RSV, hMPV, and SARS-CoV-2 viruses. For each protein, we tested less than a handful of designs to identify stable versions. Solved structures of designed proteins from the three different viruses evidenced the atomic accuracy of our approach. Furthermore, the immunological response of the RSV F design compared to a current clinical candidate in a mouse model. While the parallel design of two conformations allows identifying and selectively modifying energetically less optimized positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. We recaptured many approaches previously introduced manually for the stabilization of viral surface proteins, such as cavity-filling, optimization of polar interactions, as well as postfusion-disruptive strategies. Using our approach, it is possible to focus on the most impacting mutations and potentially preserve the immunogen as closely as possible to its native version. The latter is important as sequence re-design can cause perturbations to B and T cell epitopes. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1–5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder–target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein–protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Malaria is a substantial global health burden with 229 million cases in 2019 and 450,000 deaths annually. Plasmodium vivax is the most widespread malaria-causing parasite putting 2.5 billion people at risk of infection. P. vivax has a dormant liver stage and therefore can exist for long periods undetected. Its blood-stage can cause severe reactions and hospitalization. Few treatment and detection options are available for this pathogen. A unique characteristic of P. vivax is that it depends on the Duffy antigen/Receptor for chemokines (DARC) on the surface of host red blood cells for invasion. P. vivax employs the Duffy binding protein (DBP) to bind to DARC. We first de novo designed a three helical bundle scaffolding database which was screened via protease digestions for stability. Protease-resistant scaffolds highlighted thresholds for stability, which we utilized for selecting DARC mimetics that we subsequentially designed through grafting and redesign of these scaffolds. The optimized design small helical protein disrupts the DBP:DARC interaction. The inhibitor blocks the receptor binding site on DBP and thus forms a strong foundation for a therapeutic that will inhibit reticulocyte infection and prevent the pathogenesis of P. vivax malaria. This article is protected by copyright. All rights reserved.

MotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes.ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesignSupplementary informationSupplementary data are available at Bioinformatics online.

Infection with the protozoan parasite Trypanosoma cruzi elicits substantial CD8+ T cell responses that disproportionately target epitopes encoded in the large trans-sialidase (TS) gene family. Within the C57BL/6 infection model, a significant proportion (30–40%) of the T. cruzi–specific CD8+ T cell response targets two immunodominant TS epitopes, TSKb18 and TSKb20. However, both TS-specific CD8+ T cell responses are dispensable for immune control, and TS-based vaccines have no demonstrable impact on parasite persistence, a determinant of disease. Besides TS, the specificity and protective capacity of CD8+ T cells that mediate immune control of T. cruzi infection are unknown. With the goal of identifying alternative CD8+ T cell targets, we designed and screened a representative set of genome-wide, in silico–predicted epitopes. Our screen identified a previously uncharacterized, to our knowledge, T cell epitope MUCKb25, found within mucin family proteins, the third most expanded large gene family in T. cruzi. The MUCKb25-specific response was characterized by delayed kinetics, relative to TS-specific responses, and extensive cross-reactivity with a large number of endogenous epitope variants. Similar to TS-specific responses, the MUCKb25 response was dispensable for control of the infection, and vaccination to generate MUCK-specific CD8+ T cells failed to confer protection. The lack of protection by MUCK vaccination was partly attributed to the fact that MUCKb25-specific T cells exhibit limited recognition of T. cruzi–infected host cells. Overall, these results indicate that the CD8+ T cell compartment in many T. cruzi–infected mice is occupied by cells with minimal apparent effector potential.

Protein-protein interactions are part of most processes in life and thereby the ability to generate new ones to either control, detect or inhibit them has universal applications. However, to develop a new binding protein to bind to a specific site at atomic detail without any additional input is a challenging problem. After DeepMind entered the protein folding field, we have seen rapid advances in protein structure predictions thanks to the implementation of machine learning algorithms. Neural networks are part of machine learning and they can learn the regularities from their input data. Here, we took advantage of their capabilities by training multiple neural networks on co-crystal structures of natural protein complexes. Inspired by image caption algorithms, we developed an extensive set of NN-based models, referred to as iNNterfaceDesign. It predicts the positioning and the secondary structure for the new binding motifs and then designs the backbone atoms followed by amino acid sequence design. Our methods are capable of recapitulating native interactions, including antibody-antigen interactions, while they also capable to produce more diverse solutions to binding at the same sites. As it was trained on natural complexes, it learned their features and can therefore also highlight preferential binding sites, as found in natural protein-protein interactions. Our method is generally applicable, and we believe that this is the first deep learning model for one-sided design of protein-protein interactions.Abstract figure

Genetic selection combined with next-generation sequencing enables the simultaneous interrogation of the functionality and stability of large numbers of naturally occurring, engineered, or computationally designed protein variants in parallel. We display hundreds of engineered proteins on the surface of yeast cells, select for binding to a set of target molecules by flow cytometry, and sequence the starting pool as well as selected pools to obtain enrichment values for each displayed protein with each target. We show that this high-throughput workflow of multiplex genetic selections followed by large-scale sequencing and comparative analysis allows not only the determination of relative affinities, but also the monitoring of specificity profiles for hundreds to thousands of protein–protein and protein–small molecule interactions in parallel. The approach not only identifies new interactions of designed proteins, but also detects unintended and undesirable off-target interactions. This provides a general framework for screening of engineered protein binders, which often have no negative selection or design step as part of their development pipelines. Hence, this method will be generally useful in the development of protein-based therapeutics.

Malaria is a substantial global health burden with 229 million cases in 2019 and 450,000 deaths annually. Plasmodium vivax is the most widespread malaria-causing parasite putting 2.5 billion people at risk of infection. P. vivax has a dormant liver stage and therefore can exist for long periods undetected. Its blood-stage can cause severe reactions and hospitalization. Few treatment and detection options are available for this pathogen. To address this need, we developed nanomolar inhibitor that could serve as a therapeutic and a diagnostic. A unique characteristic of P. vivax is that it depends on the Duffy antigen/Receptor for chemokines (DARC) on the surface of host red blood cells for invasion. P. vivax employs the Duffy binding protein (DBP) to bind to DARC. We first de novo designed a three helical bundle scaffolding database which was screened via protease digestions for stability. Protease-resistant scaffolds highlighted thresholds for stability, which we utilized for selecting DARC mimetics that we subsequentially designed through grafting and redesign of these scaffolds. The optimized design small helical protein disrupts the DBP:DARC interaction. The inhibitor blocks the receptor binding site on DBP and thus forms a strong foundation for a therapeutic that will inhibit reticulocyte infection and prevent the pathogenesis of P. vivax malaria.TeaserDe novo designed proteins present a new alternative for the development of therapeutics. They can be small, highly stable and easily manufactured. Here we designed a potential new therapeutic to inhibit entry of Plasmodium vivax into red blood cells by interfering its interactions of surface displayed DBP molecules with the host receptor DARC.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model—despite weaknesses including a noisy data set—can be used to substantially increase the stability of both expert-designed and model-generated proteins.

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains an outstanding challenge. We describe a general solution to this problem which starts with a broad exploration of the very large space of possible binding modes and interactions, and then intensifies the search in the most promising regions. We demonstrate its very broad applicability by de novo design of binding proteins to 12 diverse protein targets with very different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of four of the binder-target complexes, and all four are very close to the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvement of both. Our approach now enables targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Influenza is a viral respiratory tract infection responsible for up to 5 million cases of severe infection and nearly 600,000 deaths worldwide each year. While treatments for influenza exist, diagnostics for the virus at the point of care are limited in their sensitivity and ability to differentiate between subtypes. We have developed an integrated two-dimensional paper network (2DPN) for the detection of the influenza virus by the surface glycoprotein, hemagglutinin. The hemagglutinin assay was developed using proteins computationally designed to bind with high affinity to the highly-conserved sialic acid binding site. The integrated 2DPN uses a novel geometry that allows automated introduction of an enzymatic amplification reagent directly to the detection zone. This assay was integrated into a prototype device and demonstrated successful detection of clinically relevant virus concentrations spiked into 70 μL of virus-free pediatric nasal swab samples. Using this novel geometry, we found improved assay performance on the device (compared to a manually-operated dipstick method), with a sensitivity of 4.45 × 10(2) TCID(50)/mL on device.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Nature only samples a small fraction in sequence space, yet many more amino acid combinations can fold into stable proteins. Furthermore, small structural variations in a single fold, which may only be a few amino acids different from the next homolog, define their molecular function. Hence, to design proteins with novel molecular functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling high shape diversity. We designed and evaluated about 30,000 de novo protein designs of 7 different folds. Among these designs, about 6,200 stable proteins were identified, with predicted structures having first-of-its-kind minimalized thioredoxin. Obtained data revealed more protein folding rules, such as helix connecting loops, which were in nature. Beyond providing a resource database for protein engineering, our data presents a large training data set for machine learning. We developed a high-accuracy classifier to predict the stability of our designed proteins. The methods and the wide range of new protein shapes provide a basis for the design of new protein function without compromising stability.

Skp1 is an adapter that links F-box proteins to cullin-1 in the Skp1/cullin-1/F-box (SCF) protein family of E3 ubiquitin ligases that targets specific proteins for polyubiquitination and subsequent protein degradation. Skp1 from the amoebozoan Dictyostelium forms a stable homodimer in vitro with a Kd of 2.5 µM as determined by sedimentation velocity studies, yet is monomeric in crystal complexes with F-box proteins. To investigate the molecular basis for the difference, we determined the solution NMR structure of a doubly truncated Skp1 homodimer (Skp1ΔΔ). The solution structure of Skp1ΔΔ dimer reveals a 2-fold symmetry with an interface that buries ∼750 Å2 of predominantly hydrophobic surface. The dimer interface overlaps with subsite-1 of the F-box interaction area, explaining why only the Skp1 monomer binds F-box proteins (FBPs). To confirm the model, Rosetta was used to predict amino acid substitutions that might disrupt the dimer interface, and the F97E substitution was chosen to potentially minimize interference with F-box interactions. A nearly full-length version of Skp1 with this substitution (Skp1ΔF97E) behaved as a stable monomer at concentrations up to 500 µM and actively bound a model FBP, mammalian Fbs1, which suggests that the dimeric state is not required for Skp1 to carry out a basic biochemical function. Finally, Skp1ΔF97E is expected to serve as a monomer model for high-resolution NMR studies previously hindered by dimerization.

Many viral surface glycoproteins and cell surface receptors are homo-oligomers1-4, and hence can potentially be targeted by geometrically matched homo-oligomers that engage all subunits simultaneously to attain high avidity and/or lock subunits together. The adaptive immune system cannot generally employ this strategy since the individual antibody binding sites are not arranged with appropriate geometry to simultaneously engage multiple sites in a single target homo-oligomer. We describe a general strategy for the computational design of homo-oligomeric protein assemblies with binding functionality precisely matched to homo-oligomeric target sites5-8. In the first step, a small protein is designed that binds a single site on the target. In the second step, the designed protein is assembled into a homo-oligomer such that the designed binding sites are aligned with the target sites. We used this approach to design high-avidity trimeric proteins that bind influenza A hemagglutinin (HA) at its conserved receptor binding site. The best characterized of these designed trimers can both capture and detect HA in a paper-based diagnostic format, neutralizes influenza in cell culture, and completely protects mice when given as a single dose 24 h before or after challenge with influenza.

Protein-protein interactions play critical roles in biology, and computational design of interactions could be useful in a range of applications. We describe in detail a general approach to de novo design of protein interactions based on computed, energetically optimized interaction hotspots, which was recently used to produce high-affinity binders of influenza hemagglutinin. We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the method's performance in natural-interface recapitulation. We show that the method generates binding surfaces that are more conformationally restricted than previous design methods, reducing opportunities for off-target interactions.

The development of novel paper-based diagnostic tests has surged in recent years, due to the suitability of these tests for use at the point of care. These emerging paper-based tests retain the low cost and ease of use of traditional lateral flow tests, while offering increased sophistication and capabilities that approach those of traditional microfluidic devices. Here, we report on the development of a novel paper-based test for the diagnosis of influenza, commonly known as the flu. Influenza is a ubiquitously occurring infection, affecting 5−20% of Americans and resulting in an average of 23,000 deaths in the U.S., and up to 500,000 deaths globally, each year. Despite its prevalence, the diagnosis of influenza remains unsatisfactory, especially at the point of care. In particular, lateral flow tests for influenza suffer from poor sensitivity and provide only limited information about the infecting flu virus. Point-of-care testing of influenza therefore stands to benefit substantially from improved technology. To this end, we have developed two different versions of a paper-based flu assay, both using computationally designed affinity proteins, or “binders,” that bind to the influenza hemagglutinin (HA) protein. One version of the assay utilizes an HA stem-region binder and the other an HA head-region binder. With these assays, we demonstrate the detection of clinically relevant concentrations of recombinant HA and intact influenza virus, as well as the translation of this paper-based system to a two-dimensional paper network (2DPN) folding card device.

Computational design provides the opportunity to program protein-protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (IgG) His-433. Using next-generation sequencing of naive and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds IgG with a Kd of ∼ 4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for IgG affinity purification and diagnostic devices.

The ability to design highly specific protein binders to disrupt protein-protein interactions would have immediate application to treatment of many diseases and for the dissection of protein-interaction networks. We recently developed a general computational method for the generation of de novo protein binders. Our methods focus on the incorporation of “hot-spot” residues onto protein scaffolds to generate protein binders to specific epitopes on proteins of interest. We demonstrate how combinations of a small number of newly generated high-affinity sidechain interactions with the target epitope of human IgG result in a new set of Fc binding proteins with new characteristics. By applying directed evolution methodologies via yeast surface display and deep sequencing, we optimized successful designs and explored their propensity for new properties, such as pH dependence and specificity for antibodies of different species. Our computationally designed and evolved proteins will have direct practical applications, such as providing new material for affinity chromatography for the purification of various antibodies or Fc-fusion proteins.

While the cost of DNA sequencing has dropped by five orders of magnitude in the past decade, DNA synthesis remains expensive for many applications. Although DNA microarrays have decreased the cost of oligonucleotide synthesis, the use of array-synthesized oligos in practice is limited by short synthesis lengths, high synthesis error rates, low yield and the challenges of assembling long constructs from complex pools. Toward addressing these issues, we developed a protocol for multiplex pairwise assembly of oligos from array-synthesized oligonucleotide pools. To evaluate the method, we attempted to assemble up to 2271 targets ranging in length from 192-252 bases using pairs of array-synthesized oligos. Within sets of complexity ranging from 131-250 targets, we observed error-free assemblies for 90.5% of all targets. When all 2271 targets were assembled in one reaction, we observed error-free constructs for 70.6%. While the assembly method intrinsically increased accuracy to a small degree, we further increased accuracy by using a high throughput 'Dial-Out PCR' protocol, which combines Illumina sequencing with an in-house set of unique PCR tags to selectively amplify perfect assemblies from complex synthetic pools. This approach has broad applicability to DNA assembly and high-throughput functional screens.

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

We describe a general computational method for designing proteins that bind a surface patch of interest on a target macromolecule. Favorable interactions between disembodied amino acid residues and the target surface are identified and used to anchor de novo designed interfaces. The method was used to design proteins that bind a conserved surface patch on the stem of the influenza hemagglutinin (HA) from the 1918 H1N1 pandemic virus. After affinity maturation, two of the designed proteins, HB36 and HB80, bind H1 and H5 HAs with low nanomolar affinity. Further, HB80 inhibits the HA fusogenic conformational changes induced at low pH. The crystal structure of HB36 in complex with 1918/H1 HA revealed that the actual binding interface is nearly identical to that in the computational design model. Such designed binding proteins may be useful for both diagnostics and therapeutics.

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins. © 2011 Fleishman et al.

Protein secretion and subcellular localization in E. coli has been under investigation for more than 60 years. While many details about the molecular mechanisms of these processes have been revealed, several facets of protein translocation still remain unclear. Bacteria secrete numerous proteins such as pathogenicity factors, toxins or degradative enzymes (Fernandez and Berenguer 2000). Six different secretion mechanisms for extruding proteins into the extracellular environment have been identified to-date. In Gram-negative bacteria such as E. coli, secretion into the extracellular medium requires crossing of two biological membranes, the inner and outer membranes of the cell. However, systems for protein translocation into the extracellular medium are generally highly protein-specific and with very few exceptions have not yet been engineered for the efficient export of recombinant proteins. More relevant from a technical and engineering standpoint, is the translocation of polypeptides from the cytoplasm into the periplasmic space, the main secretory compartment which is equivalent to the endoplasmic reticulum of eukaryotic cells.

We have developed a bacterial two-hybrid system for the detection of interacting proteins that capitalizes on the folding quality control mechanism of the Twin Arginine Transporter (Tat) pathway. The Tat export pathway is responsible for the membrane translocation of folded proteins, including proteins consisting of more than one polypeptide, only one of which contains a signal peptide ("hitchhiker export"). Here, one protein (bait) is expressed as a fusion to a Tat signal peptide, whereas the second protein (prey) is fused to a protein reporter that can confer a phenotype only after export into the bacterial periplasmic space. Since the prey-reporter fusion lacks a signal peptide, it can only be exported as a complex with the bait-signal peptide fusion that is capable of targeting the Tat translocon. Using maltose-binding protein as a reporter, clones expressing interacting proteins can be grown on maltose minimal media or on MacConkey plates. In addition, we introduce the use of the cysteine disulfide oxidase DsbA as a reporter. Export of a signal peptide-prey:bait-DsbA complex into the periplasm allows complementation of dsbA(-) mutants and restores the formation of active alkaline phosphatase, which in turn can be detected by a chromogenic assay.