Motif-Driven Design of Protein-Protein Interfaces

De novo design of protein minibinder agonists of TLR3

Toll-like Receptor 3 (TLR3) is a pattern recognition receptor that initiates antiviral immune responses upon binding double-stranded RNA (dsRNA). Several nucleic acid-based TLR3 agonists have been explored clinically as vaccine adjuvants in cancer and infectious disease, but present substantial manufacturing and formulation challenges. Here, we use computational protein design to create novel miniproteins that bind to human TLR3 with nanomolar affinities. Cryo-EM structures of two minibinders in complex with TLR3 reveal that they bind the target as designed, although one partially unfolds due to steric competition with a nearby N-linked glycan. Multimeric forms of both minibinders induce NF-κB signaling in TLR3-expressing cell lines, demonstrating that they may have therapeutically relevant biological activity. Our work provides a foundation for the development of specific, stable, and easy-to-formulate protein-based agonists of TLRs and other pattern recognition receptors.

Next generation of multispecific antibody engineering

Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that 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, whereas others were generated purely 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 an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

De novo designed protein inhibitors of amyloid aggregation and seeding

Neurodegenerative diseases are characterized by the pathologic accumulation of aggregated proteins. Known as amyloid, these fibrillar aggregates include proteins such as tau and amyloid-β (Aβ) in Alzheimer's disease (AD) and alpha-synuclein (αSyn) in Parkinson's disease (PD). The development and spread of amyloid fibrils within the brain correlates with disease onset and progression, and inhibiting amyloid formation is a possible route toward therapeutic development. Recent advances have enabled the determination of amyloid fibril structures to atomic-level resolution, improving the possibility of structure-based inhibitor design. In this work, we use these amyloid structures to design inhibitors that bind to the ends of fibrils, "capping" them so as to prevent further growth. Using de novo protein design, we develop a library of miniprotein inhibitors of 35 to 48 residues that target the amyloid structures of tau, Aβ, and αSyn. Biophysical characterization of top in silico designed inhibitors shows they form stable folds, have no sequence similarity to naturally occurring proteins, and specifically prevent the aggregation of their targeted amyloid-prone proteins in vitro. The inhibitors also prevent the seeded aggregation and toxicity of fibrils in cells. In vivo evaluation reveals their ability to reduce aggregation and rescue motor deficits in Caenorhabditis elegans models of PD and AD.

Scaffolding protein functional sites using deep learning

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

Design of protein-binding proteins from the target structure alone

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.

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.

Finely 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.

Towards Structure-Guided Development of Pain Therapeutics Targeting Voltage-Gated Sodium Channels

Voltage-gated sodium (Na_V) channels are critical molecular determinants of action potential generation and propagation in excitable cells. Normal Na_V channel function disruption can affect physiological neuronal signaling and lead to increased sensitivity to pain, congenital indifference to pain, uncoordinated movement, seizures, or paralysis. Human genetic studies have identified human Na_V1.7 (hNa_V1.7), hNa_V1.8, and hNa_V1.9 channel subtypes as crucial players in pain signaling. The premise that subtype selective Na_V inhibitors can reduce pain has been reinforced through intensive target validation and therapeutic development efforts. However, an ideal therapeutic has yet to emerge. This review is focused on recent progress, current challenges, and future opportunities to develop Na_V channel targeting small molecules and peptides as non-addictive therapeutics to treat pain.

Protein scaffolds: Antibody alternative for cancer diagnosis and therapy

Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent limitation, and high production cost. These issues have led scientists to explore and develop novel antibody alternatives. Protein scaffolds are small monomeric proteins with stable tertiary structures and mutable residues, which emerged in the 1990s. By combining robust gene engineering and phage display techniques, libraries with sufficient diversity could be established for target binding scaffold selection. Given the properties of small size, high affinity, and excellent specificity and stability, protein scaffolds have been applied in basic research, and preclinical and clinical fields over the past two decades. To date, more than 20 types of protein scaffolds have been developed, with the most frequently used being affibody, adnectin, ANTICALIN®, DARPins, and knottin. In this review, we focus on the protein scaffold applications in cancer therapy and diagnosis in the last 5 years, and discuss the pros and cons, and strategies of optimization and design.

Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent limitation, and high production cost.

Match_Motif: A rapid computational tool to assist in protein-protein interaction design

In order to generate protein assemblies with a desired function, the rational design of protein-protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein-protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.

Designer proteins that competitively inhibit Gαq by targeting its effector site

During signal transduction, the G protein, Gαq, binds and activates phospholipase C-β isozymes. Several diseases have been shown to manifest upon constitutively activating mutation of Gαq, such as uveal melanoma. Therefore, methods are needed to directly inhibit Gαq. Previously, we demonstrated that a peptide derived from a helix-turn-helix (HTH) region of PLC-β3 (residues 852-878) binds Gαq with low micromolar affinity and inhibits Gαq by competing with full-length PLC-β isozymes for binding. Since the HTH peptide is unstructured in the absence of Gαq, we hypothesized that embedding the HTH in a folded protein might stabilize the binding-competent conformation and further improve the potency of inhibition. Using the molecular modeling software Rosetta, we searched the Protein Data Bank for proteins with similar HTH structures near their surface. The candidate proteins were computationally docked against Gαq, and their surfaces were redesigned to stabilize this interaction. We then used yeast surface display to affinity mature the designs. The most potent design bound Gαq/i with high affinity in vitro (KD = 18 nM) and inhibited activation of PLC-β isozymes in HEK293 cells. We anticipate that our genetically encoded inhibitor will help interrogate the role of Gαq in healthy and disease model systems. Our work demonstrates that grafting interaction motifs into folded proteins is a powerful approach for generating inhibitors of protein-protein interactions.

Computational Design of Structured and Functional Peptide Macrocycles

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.

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.

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions into OFF- and ON-switches active in cells.

Identification of a Zika NS2B epitope as a biomarker for severe clinical phenotypes

The identification of specific biomarkers for Zika infection and its clinical complications is fundamental to mitigate the infection spread, which has been associated with a broad range of neurological sequelae. We present the characterization of antibody responses in serum samples from individuals infected with Zika, presenting non-severe (classical) and severe (neurological disease) phenotypes, with high-density peptide arrays comprising the Zika NS1 and NS2B proteins. The data pinpoints one strongly IgG-targeted NS2B epitope in non-severe infections, which is absent in Zika patients, where infection progressed to the severe phenotype. This differential IgG profile between the studied groups was confirmed by multivariate data analysis. Molecular dynamics simulations and circular dichroism have shown that the peptide in solution presents itself in a sub-optimal conformation for antibody recognition, which led us to computationally engineer an artificial protein able to stabilize the NS2B epitope structure. The engineered protein was used to interrogate paired samples from mothers and their babies presenting Zika-associated microcephaly and confirmed the absence of NS2B IgG response in those samples. These findings suggest that the assessment of antibody responses to the herein identified NS2B epitope is a strong candidate biomarker for the diagnosis and prognosis of Zika-associated neurological disease.

Computational design of a synthetic PD-1 agonist

Programmed cell death protein-1 (PD-1) inhibitory antibodies, often referred to as checkpoint inhibitors, have revolutionized the treatment of cancer. However, there is an unmet need for PD-1 agonists to treat autoimmune disorders. Herein, we describe the de novo design of a small, stable PD-1 binding protein and development of a synthetic PD-1 agonist.

Programmed cell death protein-1 (PD-1) expressed on activated T cells inhibits T cell function and proliferation to prevent an excessive immune response, and disease can result if this delicate balance is shifted in either direction. Tumor cells often take advantage of this pathway by overexpressing the PD-1 ligand PD-L1 to evade destruction by the immune system. Alternatively, if there is a decrease in function of the PD-1 pathway, unchecked activation of the immune system and autoimmunity can result. Using a combination of computation and experiment, we designed a hyperstable 40-residue miniprotein, PD-MP1, that specifically binds murine and human PD-1 at the PD-L1 interface with a K_d of ∼100 nM. The apo crystal structure shows that the binder folds as designed with a backbone RMSD of 1.3 Å to the design model. Trimerization of PD-MP1 resulted in a PD-1 agonist that strongly inhibits murine T cell activation. This small, hyperstable PD-1 binding protein was computationally designed with an all-beta interface, and the trimeric agonist could contribute to treatments for autoimmune and inflammatory diseases.

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.

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.

De novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2

We developed a de novo protein design strategy to swiftly engineer decoys for neutralizing pathogens that exploit extracellular host proteins to infect the cell. Our pipeline allowed the design, validation, and optimization of de novo human angiotensin-converting enzyme 2 (hACE2) decoys to neutralize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The best monovalent decoy, CTC-445.2, bound with low nanomolar affinity and high specificity to the receptor-binding domain (RBD) of the spike protein. Cryo-electron microscopy (cryo-EM) showed that the design is accurate and can simultaneously bind to all three RBDs of a single spike protein. Because the decoy replicates the spike protein target interface in hACE2, it is intrinsically resilient to viral mutational escape. A bivalent decoy, CTC-445.2d, showed ~10-fold improvement in binding. CTC-445.2d potently neutralized SARS-CoV-2 infection of cells in vitro, and a single intranasal prophylactic dose of decoy protected Syrian hamsters from a subsequent lethal SARS-CoV-2 challenge.

The advent of de novo proteins for cancer immunotherapy

Engineered proteins are revolutionizing immunotherapy, but advances are still needed to harness their full potential. Traditional protein engineering methods use naturally existing proteins as a starting point, and therefore, are intrinsically limited to small alterations of a protein's natural structure and function. Conversely, computational de novo protein design is free of such limitation, and can produce a virtually infinite number of novel protein sequences, folds, and functions. Recently, we used de novo protein engineering to create Neoleukin-2/15 (Neo-2/15), a protein mimetic of the function of both interleukin-2 (IL-2) and interleukin-15 (IL-15). To our knowledge, Neo-2/15 is the first de novo protein with immunotherapeutic activity, and in murine cancer models, it has demonstrated enhanced therapeutic potency and reduced toxicity compared to IL-2. De novo protein design is already showcasing its tremendous potential for driving the next wave of protein-based therapeutics that are explicitly engineered to treat disease.

Miniproteins as a Powerful Modality in Drug Development

Miniproteins are a diverse group of protein scaffolds characterized by small (1-10 kDa) size, stability, and versatility in drug-like roles. Coming largely from native sources, they have been widely adopted into drug development pipelines. While their structures and capabilities are diverse, the approaches to their utilization share more similarities with each other than with more widely used modalities (e.g., antibodies or small molecules). In this review, we highlight recent advances in miniprotein-based approaches to otherwise poorly addressed clinical needs, including structure-based and functional characterization. We also summarize their unique screening strategies and pharmacology considerations. Through a greater understanding of the unique properties that make them attractive for drug design, miniproteins can be effectively utilized against targets that are intractable by other approaches.

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.

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.

Massively parallel de novo protein design for targeted therapeutics

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Computational and Experimental Evaluation of Designed β-Cap Hairpins Using Molecular Simulations and Kinetic Network Models

Molecular simulation has been used to model the detailed folding properties of peptides, yet prospective computational peptide design by such approaches remains challenging and nontrivial. To test the accuracy of simulation-based hairpin design, we characterized the folding properties of a series of so-called β-cap hairpin peptides designed to mimic a conserved hairpin of LapD, a bacterial intracellular signaling protein, both experimentally by NMR spectroscopy and computationally by implicit-solvent replica-exchange molecular dynamics using three different AMBER force fields (ff96, ff99sb-ildn, and ff99sb-ildn-NMR). A unique challenge presented by these designs is the presence of both a terminal Trp-Trp capping motif and a conserved GWxQ motif in the hairpin turn required for binding to LapG. Consistent with previous studies, we found AMBER ff96 to be the most accurate when used with the OBC GBSA implicit solvent model, despite its known bias toward β-sheet conformations when used in explicit-solvent simulations. To gain microscopic insight into the folding landscape of the hairpin designs, we additionally performed parallel simulations on the Folding@home distributed computing platform using AMBER ff99sb-ildn-NMR with TIP3P explicit solvent. Markov state models (MSMs) built from trajectory data reveal a number of non-native interactions between Trp and other amino acid side chains, creating potential problems in achieving well-folded hairpin structures in solution.

Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer

Many cancers overexpress one or more of the six human pro-survival BCL2 family proteins to evade apoptosis. To determine which BCL2 protein or proteins block apoptosis in different cancers, we computationally designed three-helix bundle protein inhibitors specific for each BCL2 pro-survival protein. Following in vitro optimization, each inhibitor binds its target with high picomolar to low nanomolar affinity and at least 300-fold specificity. Expression of the designed inhibitors in human cancer cell lines revealed unique dependencies on BCL2 proteins for survival which could not be inferred from other BCL2 profiling methods. Our results show that designed inhibitors can be generated for each member of a closely-knit protein family to probe the importance of specific protein-protein interactions in complex biological processes.