Design of protein-binding proteins from the target structure alone

Computational tools to predict context-specific protein complexes

Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

An engineered AAV targeting integrin alpha V beta 6 presents improved myotropism across species

Current adeno-associated virus (AAV) gene therapy using nature-derived AAVs is limited by non-optimal tissue targeting. In the treatment of muscular diseases (MD), high doses are often required but can lead to severe adverse effects. Here, we rationally design an AAV capsid that specifically targets skeletal muscle to lower treatment doses. We computationally integrate binding motifs of human integrin alphaV beta6, a skeletal muscle receptor, into a liver-detargeting capsid. Designed AAVs show higher productivity and superior muscle transduction compared to their parent. One variant, LICA1, demonstrates comparable muscle transduction to other myotropic AAVs with reduced liver targeting. LICA1's myotropic properties are observed across species, including non-human primate. Consequently, LICA1, but not AAV9, effectively delivers therapeutic transgenes and improved muscle functionality in two mouse MD models (male mice) at a low dose (5E12 vg/kg). These results underline the potential of our design method for AAV engineering and LICA1 variant for MD gene therapy.

Construction of homologous branched oligomer megamolecules based on linker-directed protein assembly

Utilizing the building blocks of recombinant proteins and synthetic linkers, we have obtained two distinct octameric megamolecules with diverse branched structures. This approach combines principles from both click chemistry and protein engineering technology, enabling the integration of functional domains within highly ordered protein assemblies for biomedical applications.

Engineered Protein Hydrogels as Biomimetic Cellular Scaffolds

The biochemical and biophysical properties of the extracellular matrix (ECM) play a pivotal role in regulating cellular behaviors such as proliferation, migration, and differentiation. Engineered protein-based hydrogels, with highly tunable multifunctional properties, have the potential to replicate key features of the native ECM. Formed by self-assembly or crosslinking, engineered protein-based hydrogels can induce a range of cell behaviors through bioactive and functional domains incorporated into the polymer backbone. Using recombinant techniques, the amino acid sequence of the protein backbone can be designed with precise control over the chain-length, folded structure, and cell-interaction sites. In this review, the modular design of engineered protein-based hydrogels from both a molecular- and network-level perspective are discussed, and summarize recent progress and case studies to highlight the diverse strategies used to construct biomimetic scaffolds. This review focuses on amino acid sequences that form structural blocks, bioactive blocks, and stimuli-responsive blocks designed into the protein backbone for highly precise and tunable control of scaffold properties. Both physical and chemical methods to stabilize dynamic protein networks with defined structure and bioactivity for cell culture applications are discussed. Finally, a discussion of future directions of engineered protein-based hydrogels as biomimetic cellular scaffolds is concluded.

Sequence-specific targeting of intrinsically disordered protein regions

A general approach to design proteins that bind tightly and specifically to intrinsically disordered regions (IDRs) of proteins and flexible peptides would have wide application in biological research, therapeutics, and diagnosis. However, the lack of defined structures and the high variability in sequence and conformational preferences has complicated such efforts. We sought to develop a method combining biophysical principles with deep learning to readily generate binders for any disordered sequence. Instead of assuming a fixed regular structure for the target, general recognition is achieved by threading the query sequence through diverse extended binding modes in hundreds of templates with varying pocket depths and spacings, followed by RFdiffusion refinement to optimize the binder-target fit. We tested the method by designing binders to 39 highly diverse unstructured targets. Experimental testing of ~36 designs per target yielded binders with affinities better than 100 nM in 34 cases, and in the pM range in four cases. The co-crystal structure of a designed binder in complex with dynorphin A is closely consistent with the design model. All by all binding experiments for 20 designs binding diverse targets show they are highly specific for the intended targets, with no crosstalk even for the closely related dynorphin A and dynorphin B. Our approach thus could provide a general solution to the intrinsically disordered protein and peptide recognition problem.

Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione

The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.

De novo design of mini-binder proteins against IL-2 receptor β chain

IL-2 regulates the immune response by interacting with different IL-2 receptor (IL-2R) subunits. High dose of IL-2 binds to IL-2Rβγc heterodimer, which induce various side effects while activating immune function. Disrupting IL-2 and IL-2R interactions can block IL-2 mediated immune response. Here, we used a computational approach to de novo design mini-binder proteins against IL-2R β chain (IL-2Rβ) to block IL-2 signaling. The hydrophobic region where IL-2 binds to IL-2Rβ was selected and the promising binding mode was broadly explored. Three mini-binders with amino acid numbers ranging from 55 to 65 were obtained and binder 1 showed the best effects in inhibiting CTLL-2 cells proliferation and STAT5 phosphorylation. Molecular dynamics simulation showed that the binding of binder 1 to IL-2Rβ was stable; the free energy of binder1/IL-2Rβ complex was lower, indicating that the affinity of binder 1 to IL-2Rβ was higher than that of IL-2. Free energy decomposition suggested that the ARG35 and ARG131 of IL-2Rβ might be the key to improve the affinity of binder. Our efforts provided new insights in developing of IL-2R blocker, offering a potential strategy for ameliorating the side effects of IL-2 treatment.

The JAK-STAT pathway: from structural biology to cytokine engineering

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway serves as a paradigm for signal transduction from the extracellular environment to the nucleus. It plays a pivotal role in physiological functions, such as hematopoiesis, immune balance, tissue homeostasis, and surveillance against tumors. Dysregulation of this pathway may lead to various disease conditions such as immune deficiencies, autoimmune diseases, hematologic disorders, and cancer. Due to its critical role in maintaining human health and involvement in disease, extensive studies have been conducted on this pathway, ranging from basic research to medical applications. Advances in the structural biology of this pathway have enabled us to gain insights into how the signaling cascade operates at the molecular level, laying the groundwork for therapeutic development targeting this pathway. Various strategies have been developed to restore its normal function, with promising therapeutic potential. Enhanced comprehension of these molecular mechanisms, combined with advances in protein engineering methodologies, has allowed us to engineer cytokines with tailored properties for targeted therapeutic applications, thereby enhancing their efficiency and safety. In this review, we outline the structural basis that governs key nodes in this pathway, offering a comprehensive overview of the signal transduction process. Furthermore, we explore recent advances in cytokine engineering for therapeutic development in this pathway.

AlphaFold two years on: Validation and impact

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.

De novo design of miniprotein antagonists of cytokine storm inducers

Cytokine release syndrome (CRS), commonly known as cytokine storm, is an acute systemic inflammatory response that is a significant global health threat. Interleukin-6 (IL-6) and interleukin-1 (IL-1) are key pro-inflammatory cytokines involved in CRS and are hence critical therapeutic targets. Current antagonists, such as tocilizumab and anakinra, target IL-6R/IL-1R but have limitations due to their long half-life and systemic anti-inflammatory effects, making them less suitable for acute or localized treatments. Here we present the de novo design of small protein antagonists that prevent IL-1 and IL-6 from interacting with their receptors to activate signaling. The designed proteins bind to the IL-6R, GP130 (an IL-6 co-receptor), and IL-1R1 receptor subunits with binding affinities in the picomolar to low-nanomolar range. X-ray crystallography studies reveal that the structures of these antagonists closely match their computational design models. In a human cardiac organoid disease model, the IL-1R antagonists demonstrated protective effects against inflammation and cardiac damage induced by IL-1β. These minibinders show promise for administration via subcutaneous injection or intranasal/inhaled routes to mitigate acute cytokine storm effects.

High-sensitivity in situ capture of endogenous RNA-protein interactions in fixed cells and primary tissues

RNA-binding proteins (RBPs) have pivotal functions in RNA metabolism, but current methods are limited in retrieving RBP-RNA interactions within endogenous biological contexts. Here, we develop INSCRIBE (IN situ Sensitive Capture of RNA-protein Interactions in Biological Environments), circumventing the challenges through in situ RNA labeling by precisely directing a purified APOBEC1-nanobody fusion to the RBP of interest. This method enables highly specific RNA-binding site identification across a diverse range of fixed biological samples such as HEK293T cells and mouse brain tissue and accurately identifies the canonical binding motifs of RBFOX2 (UGCAUG) and TDP-43 (UGUGUG) in native cellular environments. Applicable to any RBP with available primary antibodies, INSCRIBE enables sensitive capture of RBP-RNA interactions from ultra-low input equivalent to ~5 cells. The robust, versatile, and sensitive INSCRIBE workflow is particularly beneficial for precious tissues such as clinical samples, empowering the exploration of genuine RBP-RNA interactions in RNA-related disease contexts.

Accurate de novo design of heterochiral protein-protein interactions

Abiotic D-proteins that selectively bind to natural L-proteins have gained significant biotechnological interest. However, the underlying structural principles governing such heterochiral protein-protein interactions remain largely unknown. In this study, we present the de novo design of D-proteins consisting of 50-65 residues, aiming to target specific surface regions of L-proteins or L-peptides. Our designer D-protein binders exhibit nanomolar affinity toward an artificial L-peptide, as well as two naturally occurring proteins of therapeutic significance: the D5 domain of human tropomyosin receptor kinase A (TrkA) and human interleukin-6 (IL-6). Notably, these D-protein binders demonstrate high enantiomeric specificity and target specificity. In cell-based experiments, designer D-protein binders effectively inhibited the downstream signaling of TrkA and IL-6 with high potency. Moreover, these binders exhibited remarkable thermal stability and resistance to protease degradation. Crystal structure of the designed heterochiral D-protein-L-peptide complex, obtained at a resolution of 2.0 Å, closely resembled the design model, indicating that the computational method employed is highly accurate. Furthermore, the crystal structure provides valuable information regarding the interactions between helical L-peptides and D-proteins, particularly elucidating a novel mode of heterochiral helix-helix interactions. Leveraging the design of D-proteins specifically targeting L-peptides or L-proteins opens up avenues for systematic exploration of the mirror-image protein universe, paving the way for a diverse range of applications.

A pan-variant miniprotein inhibitor protects against SARS-CoV-2 variants

The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has compromised neutralizing antibody responses elicited by prior infection or vaccination and abolished the utility of most monoclonal antibody therapeutics. We previously described a computationally-designed, homotrimeric miniprotein inhibitor, designated TRI2-2, that protects mice against pre-Omicron SARS-CoV-2 variants. Here, we show that TRI2-2 exhibits pan neutralization of variants that evolved during the 4.5 years since the emergence of SARS-CoV-2 and protects mice against BQ.1.1, XBB.1.5 and BA.2.86 challenge when administered post-exposure by an intranasal route. The resistance of TRI2-2 to viral escape and its direct delivery to the upper airways rationalize a path toward clinical advancement.

Preclinical proof of principle for orally delivered Th17 antagonist miniproteins

Interleukin (IL)-23 and IL-17 are well-validated therapeutic targets in autoinflammatory diseases. Antibodies targeting IL-23 and IL-17 have shown clinical efficacy but are limited by high costs, safety risks, lack of sustained efficacy, and poor patient convenience as they require parenteral administration. Here, we present designed miniproteins inhibiting IL-23R and IL-17 with antibody-like, low picomolar affinities at a fraction of the molecular size. The minibinders potently block cell signaling in vitro and are extremely stable, enabling oral administration and low-cost manufacturing. The orally administered IL-23R minibinder shows efficacy better than a clinical anti-IL-23 antibody in mouse colitis and has a favorable pharmacokinetics (PK) and biodistribution profile in rats. This work demonstrates that orally administered de novo-designed minibinders can reach a therapeutic target past the gut epithelial barrier. With high potency, gut stability, and straightforward manufacturability, de novo-designed minibinders are a promising modality for oral biologics.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

De novo design of buttressed loops for sculpting protein functions

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9-14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Efficient Generation of Protein Pockets with PocketGen

Designing protein-binding proteins plays an important role in drug discovery. However, AI-based design of such proteins is challenging due to complex ligand-protein interactions, flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that produces both the residue sequence and atom structure of the protein regions where interactions with ligand molecules occur. PocketGen ensures sequence-structure consistency by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The bilevel graph transformer captures interactions at multiple granularities across atom, residue, and ligand levels. To enhance sequence refinement, PocketGen integrates a structural adapter with the protein language model, ensuring consistency between structure-based and sequence-based predictions. Results show that PocketGen can generate high-fidelity protein pockets with superior binding affinity and structural validity. It is ten times faster than physics-based methods and achieves a 95% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets, along with achieving an amino acid recovery rate exceeding 64%.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Binding and sensing diverse small molecules using shape-complementary pseudocycles

We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies

Many growth factors and cytokines signal by binding to the extracellular domains of their receptors and driving association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affect signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo-designed fibroblast growth factor receptor (FGFR)-binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and mitogen-activated protein kinase (MAPK) pathway activation. The high specificity of the designed agonists reveals distinct roles for two FGFR splice variants in driving arterial endothelium and perivascular cell fates during early vascular development. Our designed modular assemblies should be broadly useful for unraveling the complexities of signaling in key developmental transitions and for developing future therapeutic applications.

De Novo Design of Integrin α5β1 Modulating Proteins for Regenerative Medicine

Integrin α5β1 is crucial for cell attachment and migration in development and tissue regeneration, and α5β1 binding proteins could have considerable utility in regenerative medicine and next-generation therapeutics. We use computational protein design to create de novo α5β1-specific modulating miniprotein binders, called NeoNectins, that bind to and stabilize the open state of α5β1. When immobilized onto titanium surfaces and throughout 3D hydrogels, the NeoNectins outperform native fibronectin and RGD peptide in enhancing cell attachment and spreading, and NeoNectin-grafted titanium implants outperformed fibronectin and RGD-grafted implants in animal models in promoting tissue integration and bone growth. NeoNectins should be broadly applicable for tissue engineering and biomedicine.

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.

Inhibition of ACE2-S Protein Interaction by a Short Functional Peptide with a Boomerang Structure

Considering the high evolutionary rate and great harmfulness of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is imperative to develop new pharmacological antagonists. Human angiotensin-converting enzyme-2 (ACE2) functions as a primary receptor for the spike protein (S protein) of SARS-CoV-2. Thus, a novel functional peptide, KYPAY (K5), with a boomerang structure, was developed to inhibit the interaction between ACE2 and the S protein by attaching to the ACE2 ligand-binding domain (LBD). The inhibition property of K5 was evaluated via molecular simulations, cell experiments, and adsorption kinetics analysis. The molecular simulations showed that K5 had a high affinity for ACE2 but a low affinity for the cell membrane. The umbrella sampling (US) simulations revealed a significant enhancement in the binding potential of this functional peptide to ACE2. The fluorescence microscopy and cytotoxicity experiments showed that K5 effectively prevented the interaction between ACE2 and the S protein without causing any noticeable harm to cells. Further flow cytometry research indicated that K5 successfully hindered the interaction between ACE2 and the S protein, resulting in 78% inhibition at a concentration of 100 μM. This work offers an innovative perspective on the development of functional peptides for the prevention and therapy of SARS-CoV-2.

Structure-based computational design of antibody mimetics: challenges and perspectives

The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

De novo design of alpha-beta repeat proteins

Proteins composed of a single structural unit tandemly repeated multiple times carry out a wide range of functions in biology. There has hence been considerable interest in designing such repeat proteins; previous approaches have employed strict constraints on secondary structure types and relative geometries, and most characterized designs either mimic a known natural topology, adhere closely to a parametric helical bundle architecture, or exploit very short repetitive sequences. Here, we describe Rosetta-based and deep learning hallucination methods for generating novel repeat protein architectures featuring mixed alpha-helix and beta-strand topologies, and 25 new highly stable alpha-beta proteins designed using these methods. We find that incorporation of terminal caps which prevent beta strand mediated intermolecular interactions increases the solubility and monomericity of individual designs as well as overall design success rate.

Proteomic Barcoding Platform for Macromolecular Screening and Delivery

Engineered macromolecules offer compelling means for the therapy of conventionally undruggable interactions in human disease. However, their efficacy is limited by barriers to tissue and intracellular delivery. Inspired by recent advances in molecular barcoding and evolution, we developed BarcodeBabel, a generalized method for the design of libraries of peptide barcodes suitable for high-throughput mass spectrometry proteomics. Combined with PeptideBabel, a Monte Carlo sampling algorithm for the design of peptides with evolvable physicochemical properties and sequence complexity, we developed a barcoded library of cell penetrating peptides (CPPs) with distinct physicochemical features. Using quantitative targeted mass spectrometry, we identified CPPS with improved nuclear and cytoplasmic delivery exceeding hundreds of millions of molecules per human cell while maintaining minimal membrane disruption and negligible toxicity in vitro. These studies provide a proof of concept for peptide barcoding as a homogeneous high-throughput method for macromolecular screening and delivery. BarcodeBabel and PeptideBabel are available open-source from https://github.com/kentsisresearchgroup/.

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

Harnessing iron‑sulfur enzymes for synthetic biology

Reactions catalysed by iron-sulfur (Fe-S) enzymes appear in a variety of biosynthetic pathways that produce valuable natural products. Harnessing these biosynthetic pathways by expression in microbial cell factories grown on an industrial scale would yield enormous economic and environmental benefits. However, Fe-S enzymes often become bottlenecks that limits the productivity of engineered pathways. As a consequence, achieving the production metrics required for industrial application remains a distant goal for Fe-S enzyme-dependent pathways. Here, we identify and review three core challenges in harnessing Fe-S enzyme activity, which all stem from the properties of Fe-S clusters: 1) limited Fe-S cluster supply within the host cell, 2) Fe-S cluster instability, and 3) lack of specialized reducing cofactor proteins often required for Fe-S enzyme activity, such as enzyme-specific flavodoxins and ferredoxins. We highlight successful methods developed for a variety of Fe-S enzymes and electron carriers for overcoming these difficulties. We use heterologous nitrogenase expression as a grand case study demonstrating how each of these challenges can be addressed. We predict that recent breakthroughs in protein structure prediction and design will prove well-suited to addressing each of these challenges. A reliable toolkit for harnessing Fe-S enzymes in engineered metabolic pathways will accelerate the development of industry-ready Fe-S enzyme-dependent biosynthesis pathways.

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Upstream open reading frames: new players in the landscape of cancer gene regulation

The translation of RNA by ribosomes represents a central biological process and one of the most dysregulated processes in cancer. While translation is traditionally thought to occur exclusively in the protein-coding regions of messenger RNAs (mRNAs), recent transcriptome-wide approaches have shown abundant ribosome activity across diverse stretches of RNA transcripts. The most common type of this kind of ribosome activity occurs in gene leader sequences, also known as 5' untranslated regions (UTRs) of the mRNA, that precede the main coding sequence. Translation of these upstream open reading frames (uORFs) is now known to occur in upwards of 25% of all protein-coding genes. With diverse functions from RNA regulation to microprotein generation, uORFs are rapidly igniting a new arena of cancer biology, where they are linked to cancer genetics, cancer signaling, and tumor-immune interactions. This review focuses on the contributions of uORFs and their associated 5'UTR sequences to cancer biology.

Heat-induced structural and chemical changes to a computationally designed miniprotein

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

De novo designed proteins neutralize lethal snake venom toxins

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.

Design of High Affinity Binders to Convex Protein Target Sites

While there has been progress in the de novo design of small globular miniproteins (50-65 residues) to bind to primarily concave regions of a target protein surface, computational design of minibinders to convex binding sites remains an outstanding challenge due to low level of overall shape complementarity. Here, we describe a general approach to generate computationally designed proteins which bind to convex target sites that employ geometrically matching concave scaffolds. We used this approach to design proteins binding to TGFβRII, CTLA-4 and PD-L1 which following experimental optimization have low nanomolar to picomolar affinities and potent biological activity. Co-crystal structures of the TGFβRII and CTLA-4 binders in complex with the receptors are in close agreement with the design models. Our approach provides a general route to generating very high affinity binders to convex protein target sites.

Utility of protein-protein binding surfaces composed of anti-parallel alpha-helices and beta-sheets selected by phage display

Over the past 3 decades, a diverse collection of small protein domains have been used as scaffolds to generate general purpose protein-binding reagents using a variety of protein display and enrichment technologies. To expand the repertoire of scaffolds and protein surfaces that might serve this purpose, we have explored the utility of (i) a pair of anti-parallel alpha-helices in a small highly disulfide-bonded 4-helix bundle, the CC4 domain from reversion-inducing Cysteine-rich Protein with Kazal Motifs and (ii) a concave beta-sheet surface and two adjacent loops in the human FN3 domain, the scaffold for the widely used monobody platform. Using M13 phage display and next generation sequencing, we observe that, in both systems, libraries of ∼30 million variants contain binding proteins with affinities in the low μM range for baits corresponding to the extracellular domains of multiple mammalian proteins. CC4- and FN3-based binding proteins were fused to the N- and/or C-termini of Fc domains and used for immunostaining of transfected cells. Additionally, FN3-based binding proteins were inserted into VP1 of AAV to direct AAV infection to cells expressing a defined surface receptor. Finally, FN3-based binding proteins were inserted into the Pvc13 tail fiber protein of an extracellular contractile injection system particle to direct protein cargo delivery to cells expressing a defined surface receptor. These experiments support the utility of CC4 helices B and C and of FN3 beta-strands C, D, and F together with adjacent loops CD and FG as surfaces for engineering general purpose protein-binding reagents.

Polymorphic Self-Assembly with Procedural Flexibility for Monodisperse Quaternary Protein Structures of DegQ Enzymes

As large molecular tertiary structures, some proteins can act as small robots that find, bind, and chaperone target protein clients, showing the potential to serve as smart building blocks in self-assembly fields. Instead of using such intrinsic functions, most self-assembly methodologies for proteins aim for de novo-designed structures with accurate geometric assemblies, which can limit procedural flexibility. Here, a strategy enabling polymorphic clustering of quaternary proteins, exhibiting simplicity and flexibility of self-assembling paths for proteins in forming monodisperse quaternary cage particles is presented. It is proposed that the enzyme protomer DegQ, previously solved at low resolution, may potentially be usable as a threefold symmetric building block, which can form polyhedral cages incorporated by the chaperone action of DegQ in the presence of protein clients. To obtain highly monodisperse cage particles, soft, and hence, less resistive client proteins, which can program the inherent chaperone activity of DegQ to efficient formations of polymorphic cages, depending on the size of clients are utilized. By reconstructing the atomic resolution cryogenic electron microscopy DegQ structures using obtained 12- and 24-meric clusters, the polymorphic clustering of DegQ enzymes is validated in terms of soft and rigid domains, which will provide effective routes for protein self-assemblies with procedural flexibility.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

Using Protein Design and Directed Evolution to Monomerize a Bright Near-Infrared Fluorescent Protein

The small ultrared fluorescent protein (smURFP) is a bright near-infrared (NIR) fluorescent protein (FP) that forms a dimer and binds its fluorescence chromophore, biliverdin, at its dimer interface. To engineer a monomeric NIR FP based on smURFP potentially more suitable for bioimaging, we employed protein design to extend the protein backbone with a new segment of two helices that shield the original dimer interface while covering the biliverdin binding pocket in place of the second chain in the original dimer. We experimentally characterized 13 designs and obtained a monomeric protein with a weak fluorescence. We enhanced the fluorescence of this designed protein through two rounds of directed evolution and obtained designed monomeric smURFP (DMsmURFP), a bright, stable, and monomeric NIR FP with a molecular weight of 19.6 kDa. We determined the crystal structures of DMsmURFP both in the apo state and in complex with biliverdin, which confirmed the designed structure. The use of DMsmURFP in in vivo imaging of mammalian systems was demonstrated. The backbone design-based strategy used here can also be applied to monomerize other naturally multimeric proteins with intersubunit functional sites.

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.

Automated high-throughput DNA synthesis and assembly

DNA synthesis and assembly primarily revolve around the innovation and refinement of tools that facilitate the creation of specific genes and the manipulation of entire genomes. This multifaceted process encompasses two fundamental steps: the synthesis of lengthy oligonucleotides and the seamless assembly of numerous DNA fragments. With the advent of automated pipetting workstations and integrated experimental equipment, a substantial portion of repetitive tasks in the field of synthetic biology can now be efficiently accomplished through integrated liquid handling workstations. This not only reduces the need for manual labor but also enhances overall efficiency. This review explores the ongoing advancements in the oligonucleotide synthesis platform, automated DNA assembly techniques, and biofoundries. The development of accurate and high-throughput DNA synthesis and assembly technologies presents both challenges and opportunities.

A miniprotein receptor electrochemical biosensor chip based on quantum dots

Recently protein binders have emerged as a promising substitute for antibodies due to their high specificity and low cost. Herein, we demonstrate an electrochemical biosensor chip through the electronic labelling strategy using lead sulfide (PbS) colloidal quantum dots (CQDs) and the unnatural SARS-CoV-2 spike miniprotein receptor LCB. The unnatural receptor can be utilized as a molecular probe for the construction of CQD-based electrochemical biosensor chips, through which the specific binding of LCB and the spike protein is transduced to sensor electrical signals. The biosensor exhibits a good linear response in the concentration range of 10 pg mL-1 to 1 μg mL-1 (13.94 fM to 1.394 nM) with the limit of detection (LOD) being 3.31 pg mL-1 (4.607 fM for the three-electrode system) and 9.58 fg mL-1 (0.013 fM for the HEMT device). Due to the high sensitivity of the electrochemical biosensor, it was also used to study the binding kinetics between the unnatural receptor LCB and spike protein, which has achieved comparable results as those obtained with commercial equipment. To the best of our knowledge, this is the first example of using a computationally designed miniprotein receptor based on electrochemical methods, and it is the first kinetic assay performed with an electrochemical assay alone. The miniprotein receptor electrochemical biosensor based on QDs is desirable for fabricating high-throughput, large-area, wafer-scale biochips.

Advances in IL-7 Research on Tumour Therapy

Interleukin-7 (IL-7) is a versatile cytokine that plays a crucial role in regulating the immune system's homeostasis. It is involved in the development, proliferation, and differentiation of B and T cells, as well as being essential for the differentiation and survival of naïve T cells and the production and maintenance of memory T cells. Given its potent biological functions, IL-7 is considered to have the potential to be widely used in the field of anti-tumour immunotherapy. Notably, IL-7 can improve the tumour microenvironment by promoting the development of Th17 cells, which can in turn promote the recruitment of effector T cells and NK cells. In addition, IL-7 can also down-regulate the expression of tumour growth factor-β and inhibit immunosuppression to promote anti-tumour efficacy, suggesting potential clinical applications for anti-tumour immunotherapy. This review aims to discuss the origin of IL-7 and its receptor IL-7R, its anti-tumour mechanism, and the recent advances in the application of IL-7 in tumour therapy.

Exploring the shared pathogenic strategies of independently evolved effectors across distinct plant viruses

Plants have developed very diverse strategies to defend themselves against viral pathogens, among which plant hormones play pivotal roles. In response, some viruses have also deployed multifunctional viral effectors that effectively hijack key component hubs to counter or evade plant immune surveillance. Although significant progress has been made toward understanding counter-defense strategies that manipulate plant hormone regulatory molecules, these efforts have often been limited to an individual virus or specific host target/pathway. This review provides new insights into broad-spectrum antiviral responses in rice triggered by key components of phytohormone signaling, and highlights the common features of counter-defense strategies employed by distinct rice-infecting RNA viruses. These strategies involve the secretion of multifunctional virulence effectors that target the sophisticated phytohormone system, dampening immune responses by engaging with the same host targets. Additionally, the review provides an in-depth exploration of various viral effectors, emphasizing tertiary structure-based research and shared host targets. Understanding these conserved characteristics in detail may pave the way for molecular drug design, opening new opportunities to enhance broad-spectrum antiviral trials through precise engineering.

Atomically accurate de novo design of single-domain antibodies

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

SpyMask enables combinatorial assembly of bispecific binders

Bispecific antibodies are a successful and expanding therapeutic class. Standard approaches to generate bispecifics are complicated by the need for disulfide reduction/oxidation or specialized formats. Here we present SpyMask, a modular approach to bispecifics using SpyTag/SpyCatcher spontaneous amidation. Two SpyTag-fused antigen-binding modules can be precisely conjugated onto DoubleCatcher, a tandem SpyCatcher where the second SpyCatcher is protease-activatable. We engineer a panel of structurally-distinct DoubleCatchers, from which binders project in different directions. We establish a generalized methodology for one-pot assembly and purification of bispecifics in 96-well plates. A panel of binders recognizing different HER2 epitopes were coupled to DoubleCatcher, revealing unexpected combinations with anti-proliferative or pro-proliferative activity on HER2-addicted cancer cells. Bispecific activity depended sensitively on both binder orientation and DoubleCatcher scaffold geometry. These findings support the need for straightforward assembly in different formats. SpyMask provides a scalable tool to discover synergy in bispecific activity, through modulating receptor organization and geometry.