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Integrin α5ß1 is crucial for cell attachment and migration in development and tissue regeneration, and α5ß1 binding proteins could have considerable utility in regenerative medicine and next-generation therapeutics. We use computational protein design to create de novo α5ß1-specific modulating miniprotein binders, called NeoNectins, that bind to and stabilize the open state of α5ß1. 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.

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Nature has evolved diverse electron transport proteins and multiprotein assemblies essential to the generation and transduction of biological energy. However, substantially modifying or adapting these proteins for user-defined applications or to gain fundamental mechanistic insight can be hindered by their inherent complexity. De novo protein design offers an attractive route to stripping away this confounding complexity, enabling us to probe the fundamental workings of these bioenergetic proteins and systems, while providing robust, modular platforms for constructing completely artificial electron-conducting circuitry. Here, we use a set of de novo designed mono-heme and di-heme soluble and membrane proteins to delineate the contributions of electrostatic micro-environments and dielectric properties of the surrounding protein medium on the inter-heme redox cooperativity that we have previously reported. Experimentally, we find that the two heme sites in both the water-soluble and membrane constructs have broadly equivalent redox potentials in isolation, in agreement with Poisson-Boltzmann Continuum Electrostatics calculations. BioDC, a Python program for the estimation of electron transfer energetics and kinetics within multiheme cytochromes, also predicts equivalent heme sites, and reports that burial within the low dielectric environment of the membrane strengthens heme-heme electrostatic coupling. We conclude that redox cooperativity in our diheme cytochromes is largely driven by heme electrostatic coupling and confirm that this effect is greatly strengthened by burial in the membrane. These results demonstrate that while our de novo proteins present minimalist, new-to-nature constructs, they enable the dissection and microscopic examination of processes fundamental to the function of vital, yet complex, bioenergetic assemblies.

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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.

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De novo protein design expands the protein universe by creating new sequences to accomplish tailor-made enzymes in the future. A promising topology to implement diverse enzyme functions is the ubiquitous TIM-barrel fold. Since the initial de novo design of an idealized four-fold symmetric TIM barrel, the family of de novo TIM barrels is expanding rapidly. Despite this and in contrast to natural TIM barrels, these novel proteins lack cavities and structural elements essential for the incorporation of binding sites or enzymatic functions. In this work, we diversified a de novo TIM barrel by extending multiple ßα-loops using constrained hallucination. Experimentally tested designs were found to be soluble upon expression in Escherichia coli and well-behaved. Biochemical characterization and crystal structures revealed successful extensions with defined α-helical structures. These diversified de novo TIM barrels provide a framework to explore a broad spectrum of functions based on the potential of natural TIM barrels.

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BACKGROUND AIMS: Chimeric antigen receptor T (CAR-T) cells are a remarkably efficacious, highly promising and rapidly evolving strategy in the field of immuno-oncology. The precision of these targeted cellular therapies is driven by the specificity of the antigen recognition element (the "binder") encoded in the CAR. This binder redirects these immune effector cells precisely toward a defined antigen on the surface of cancer cells, leading to T-cell receptor-independent tumor lysis. Currently, for tumor targeting most CAR-T cells are designed using single-chain variable fragments (scFvs) derived from murine or human immunoglobulins. However, there are several emerging alternative binder modalities that are finding increasing utility for improved CAR function beyond scFvs. METHODS: Here we review the most recent developments in the use of non-canonical protein binding domains in CAR design, including nanobodies, DARPins, natural ligands, and de novo-designed protein elements. RESULTS: Overall, we describe how new protein binder formats, with their unique structural properties and mechanisms of action, may possess key advantages over traditional scFv CAR designs. CONCLUSIONS: These alternative binder designs may contribute to enhanced CAR-T therapeutic options and, ultimately, improved outcomes for cancer patients.

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De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of ~50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on ß-sheet-ß-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

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Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI. New protein folds and higher-order assemblies can be designed with considerable experimental success rates, and difficult problems requiring tunable control over protein conformations and precise shape complementarity for molecular recognition are coming into reach. Emerging approaches incorporate engineering principles-tunability, controllability, and modularity-into the design process from the beginning. Exciting frontiers lie in deconstructing cellular functions with de novo proteins and, conversely, constructing synthetic cellular signaling from the ground up. As methods improve, many more challenges are unsolved.

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The emergence of new proteins is a central question in biology. Most tertiary protein folds known to date appear to have an ancient origin, but it is clear from bioinformatic analyses that new proteins continuously emerge in all organismal groups. However, there is a paucity of experimental data on new proteins regarding their structure and biophysical properties. We performed a detailed phylogenetic analysis and identified 48 putative open reading frames in the honeybee-associated bacterium Apilactobacillus kunkeei for which no or few homologs could be identified in closely-related species, suggesting that they could be relatively new on an evolutionary time scale and represent recently evolved proteins. Using circular dichroism-, fluorescence- and nuclear magnetic resonance (NMR) spectroscopy we investigated six of these proteins and show that they are not intrinsically disordered, but populate alpha-helical dominated folded states with relatively low thermodynamic stability (0-3 kcal/mol). The NMR and biophysical data demonstrate that small new proteins readily adopt simple folded conformations suggesting that more complex tertiary structures can be continuously re-invented during evolution by fusion of such simple secondary structure elements. These findings have implications for the general view on protein evolution, where de novo emergence of folded proteins may be a common event.

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Over the past decades, the TIM-barrel fold has served as a model system for the exploration of how changes in protein sequences affect their structural, stability, and functional characteristics, and moreover, how this information can be leveraged to design proteins from the ground up. After numerous attempts to design de novo proteins with this specific fold, sTIM11 was the first validated de novo design of an idealized four-fold symmetric TIM barrel. Subsequent efforts to enhance the stability of this initial design resulted in the development of DeNovoTIMs, a family of de novo TIM barrels with various stabilizing mutations. In this study, we present an investigation into the biophysical and thermodynamic effects upon introducing a varying number of stabilizing mutations per quarter along the sequence of a four-fold symmetric TIM barrel. We compared the base design DeNovoTIM0 without any stabilizing mutations with variants containing mutations in one, two, three, and all four quarters-designated TIM1q, TIM2q, TIM3q, and DeNovoTIM6, respectively. This analysis revealed a stepwise and nonlinear change in the thermodynamic properties that correlated with the number of mutated quarters, suggesting positive nonadditive effects. To shed light on the significance of the location of stabilized quarters, we engineered two variants of TIM2q which contain the same number of mutations but positioned in different quarter locations. Characterization of these TIM2q variants revealed that the mutations exhibit varying effects on the overall protein stability, contingent upon the specific region in which they are introduced. These findings emphasize that the amount and location of stabilized interfaces among the four quarters play a crucial role in shaping the conformational stability of these four-fold symmetric TIM barrels. Analysis of de novo proteins, as described in this study, enhances our understanding of how sequence variations can finely modulate stability in both naturally occurring and computationally designed proteins.

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Relating the macroscopic properties of protein-based materials to their underlying component microstructure is an outstanding challenge. Here, we exploit computational design to specify the size, flexibility, and valency of de novo protein building blocks, as well as the interaction dynamics between them, to investigate how molecular parameters govern the macroscopic viscoelasticity of the resultant protein hydrogels. We construct gel systems from pairs of symmetric protein homo-oligomers, each comprising 2, 5, 24, or 120 individual protein components, that are crosslinked either physically or covalently into idealized step-growth biopolymer networks. Through rheological assessment, we find that the covalent linkage of multifunctional precursors yields hydrogels whose viscoelasticity depends on the crosslink length between the constituent building blocks. In contrast, reversibly crosslinking the homo-oligomeric components with a computationally designed heterodimer results in viscoelastic biomaterials exhibiting fluid-like properties under rest and low shear, but solid-like behavior at higher frequencies. Exploiting the unique genetic encodability of these materials, we demonstrate the assembly of protein networks within living mammalian cells and show via fluorescence recovery after photobleaching (FRAP) that mechanical properties can be tuned intracellularly in a manner similar to formulations formed extracellularly. We anticipate that the ability to modularly construct and systematically program the viscoelastic properties of designer protein-based materials could have broad utility in biomedicine, with applications in tissue engineering, therapeutic delivery, and synthetic biology.

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BACKGROUND: Natural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification. RESULTS: This study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures. CONCLUSION: TPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

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Global proteome changes in microbes affect the survival and overall production of commercially relevant metabolites through different bioprocesses. The existing methods to monitor proteome level changes are destructive in nature. Stable isotope probing (SIP) coupled with Raman spectroscopy is a relatively new approach for proteome analysis. However, applying this approach for monitoring changes in a large culture volume is not cost-effective. In this study, for the first time we are presenting a novel method of combining reverse SIP using 13 C-glucose and Deuterium to monitor the proteome changes through Raman spectroscopy. The findings of the study revealed visible changes (blue shifts) in proteome related peaks that can be used for monitoring proteome dynamics, that is, synthesis of nascent amino acids and its turnover with time in a non-destructive, cost-effective, and label-free manner.

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Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.

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NCYM, a Homininae-specific oncoprotein, is the first de novo gene product experimentally shown to have oncogenic functions. NCYM stabilizes MYCN and ß-catenin via direct binding and inhibition of GSK3ß and promotes cancer progression in various tumors. Thus, the identification of compounds that binds to NCYM and structural characterization of the complex of such compounds with NCYM are required to deepen our understanding of the molecular mechanism of NCYM function and eventually to develop anticancer drugs against NCYM. In this study, the DNA aptamer that specifically binds to NCYM and enhances interaction between NCYM and GSK3ß were identified for the first time using systematic evolution of ligands by exponential enrichment (SELEX). The structural properties of the complex of the aptamer and NCYM were investigated using atomic force microscopy (AFM) in combination with truncation and mutation of DNA sequence, pointing to the regions on the aptamer required for NCYM binding. Further analysis was carried out by small-angle X-ray scattering (SAXS). Structural modeling based on SAXS data revealed that when isolated, NCYM shows high flexibility, though not as a random coil, while the DNA aptamer exists as a dimer in solution. In the complex state, models in which NCYM was bound to a region close to an edge of the aptamer reproduced the SAXS data. Therefore, using a combination of SELEX, AFM, and SAXS, the present study revealed the structural properties of NCYM in its functionally active form, thus providing useful information for the possible future design of novel anti-cancer drugs targeting NCYM.

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There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high-affinity protein binders.

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Recent studies highlight the effectiveness of hybrid Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) vaccines combining wild-type nucleocapsid and Spike proteins. We have further enhanced this strategy by incorporating delta and omicron variants' spike protein mutations. Both delta and omicron mark the shifts in viral transmissibility and severity in unvaccinated and vaccinated patients. So their mutations are highly crucial for future viral variants also. Omicron is particularly adept at immune evasion by mutating spike epitopes. The rapid adaptations of Omicron and sub-variants to spike-based vaccines and simultaneous transmissibility underline the urgency for new vaccines in the continuous battle against SARS-CoV-2. Therefore, we have added three persistent T-cell-stimulating nucleocapsid peptides similar to homologous sequences from seasonal Human Coronaviruses (HuCoV) and an envelope peptide that elicits a strong T-cell immune response. These peptides are clustered in the hybrid spike's cytoplasmic region with non-immunogenic linkers, enabling systematic arrangement. AlphaFold (Artificial intelligence-based model building) analysis suggests omitting the transmembrane domain enhances these cytoplasmic epitopes' folding efficiency which can ensure persistent immunity for CD4+ structural epitopes. Further molecular dynamics simulations validate the compact conformation of the modeled structures and a flexible C-terminus region. Overall, the structures show stability and less conformational fluctuation throughout the simulation. Also, the AlphaFold predicted structural epitopes maintained their folds during simulation to ensure the specificity of CD4+ T-cell response after vaccination. Our proposed approach may provide options for incorporating diverse anti-viral T-cell peptides, similar to HuCoV, into linker regions. This versatility can be promising to address outbreaks and challenges posed by various viruses for effective management in this era of innovative vaccines.

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Producing novel enzymes that are catalytically active in vitro and biologically functional in vivo is a key goal of synthetic biology. Previously, we reported Syn-F4, the first de novo protein that meets both criteria. Syn-F4 hydrolyzed the siderophore ferric enterobactin, and expression of Syn-F4 allowed an inviable strain of Escherichia coli (Δfes) to grow in iron-limited medium. Here, we describe the crystal structure of Syn-F4. Syn-F4 forms a dimeric 4-helix bundle. Each monomer comprises two long α-helices, and the loops of the Syn-F4 dimer are on the same end of the bundle (syn topology). Interestingly, there is a penetrated hole in the central region of the Syn-F4 structure. Extensive mutagenesis experiments in a previous study showed that five residues (Glu26, His74, Arg77, Lys78, and Arg85) were essential for enzymatic activity in vivo. All these residues are located around the hole in the central region of the Syn-F4 structure, suggesting a putative active site with a catalytic dyad (Glu26-His74). The complete inactivity of purified proteins with mutations at the five residues supports the putative active site and reaction mechanism. Molecular dynamics and docking simulations of the ferric enterobactin siderophore binding to the Syn-F4 structure demonstrate the dynamic property of the putative active site. The structure and active site of Syn-F4 are completely different from native enterobactin esterase enzymes, thereby demonstrating that proteins designed de novo can provide life-sustaining catalytic activities using structures and mechanisms dramatically different from those that arose in nature.

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In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction ($\sim\! 40$s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, $\alpha $-helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. CONTACT: guofei@csu.edu.cn, jj.tang@siat.ac.cn.

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Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an "inside-out" approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gαq and its effector PLC-ß isozymes. Several of the designed proteins remain folded above 90°C and bind to Gαq with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gαq , the designed proteins inhibit activation of PLC-ß isozymes and Dbl-family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection.

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The emergence of novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the need to investigate alternative approaches to prevent infection and treat patients with coronavirus disease 2019. Here, we report the preclinical efficacy of NL-CVX1, a de novo decoy that blocks virus entry into cells by binding with nanomolar affinity and high specificity to the receptor-binding domain of the SARS-CoV-2 spike protein. Using a transgenic mouse model of SARS-CoV-2 infection, we showed that a single prophylactic intranasal dose of NL-CVX1 conferred complete protection from severe disease following SARS-CoV-2 infection. Multiple therapeutic administrations of NL-CVX1 also protected mice from succumbing to infection. Finally, we showed that infected mice treated with NL-CVX1 developed both anti-SARS-CoV-2 antibodies and memory T cells and were protected against reinfection a month after treatment. Overall, these observations suggest NL-CVX1 is a promising therapeutic candidate for preventing and treating severe SARS-CoV-2 infections.

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