De novo protein design enables the precise induction of RSV-neutralizing antibodies

Enhanced antibody response to the conformational non-RBD region via DNA prime-protein boost elicits broad cross-neutralization against SARS-CoV-2 variants

Preventing immune escape of SARS-CoV-2 variants is crucial in vaccine development to ensure broad protection against the virus. Conformational epitopes beyond the RBD region are vital components of the spike protein but have received limited attention in the development of broadly protective SARS-CoV-2 vaccines. In this study, we used a DNA prime-protein boost regimen to evaluate the broad cross-neutralization potential of immune response targeting conformational non-RBD region against SARS-CoV-2 viruses in mice. Mice with enhanced antibody responses targeting conformational non-RBD region show better performance in cross-neutralization against the Wuhan-01, Delta, and Omicron subvariants. Via analyzing the distribution of conformational epitopes, and quantifying epitope-specific binding antibodies, we verified a positive correlation between the proportion of binding antibodies against the N-terminal domain (NTD) supersite (a conformational non-RBD epitope) and SARS-CoV-2 neutralization potency. The current work highlights the importance of high ratio of conformational non-RBD-specific binding antibodies in mediating viral cross-neutralization and provides new insight into overcoming the immune escape of SARS-CoV-2 variants.

Unlocking the potential of circular RNA vaccines: a bioinformatics and computational biology perspective

Bioinformatics has significantly advanced RNA-based therapeutics, particularly circular RNAs (circRNAs), which outperform mRNA vaccines, by offering superior stability, sustained expression, and enhanced immunogenicity due to their covalently closed structure. This review highlights how bioinformatics and computational biology optimise circRNA vaccine design, elucidates internal ribosome entry sites (IRES) selection, open reading frame (ORF) optimisation, codon usage, RNA secondary structure prediction, and delivery system development. While circRNA vaccines may not always surpass traditional vaccines in stability, their production efficiency and therapeutic efficacy can be enhanced through computational strategies. The discussion also addresses challenges and future prospects, emphasizing the need for innovative solutions to overcome current limitations and advance circRNA vaccine applications.

Structural Virology: The Key Determinants in Development of Antiviral Therapeutics

Structural virology has emerged as the foundation for the development of effective antiviral therapeutics. It is pivotal in providing crucial insights into the three-dimensional frame of viruses and viral proteins at atomic-level or near-atomic-level resolution. Structure-based assessment of viral components, including capsids, envelope proteins, replication machinery, and host interaction interfaces, is instrumental in unraveling the multiplex mechanisms of viral infection, replication, and pathogenesis. The structural elucidation of viral enzymes, including proteases, polymerases, and integrases, has been essential in combating viruses like HIV-1 and HIV-2, SARS-CoV-2, and influenza. Techniques including X-ray crystallography, Nuclear Magnetic Resonance spectroscopy, Cryo-electron Microscopy, and Cryo-electron Tomography have revolutionized the field of virology and significantly aided in the discovery of antiviral therapeutics. The ubiquity of chronic viral infections, along with the emergence and reemergence of new viral threats necessitate the development of novel antiviral strategies and agents, while the extensive structural diversity of viruses and their high mutation rates further underscore the critical need for structural analysis of viral proteins to aid antiviral development. This review highlights the significance of structure-based investigations for bridging the gap between structure and function, thus facilitating the development of effective antiviral therapeutics, vaccines, and antibodies for tackling emerging viral threats.

Epitope-focused vaccine immunogens design using tailored horseshoe-shaped scaffold

The continuous emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants highlights the need to update coronavirus 2019 disease (COVID-19) vaccine components. Epitope-based vaccine designs targeting conserved and immunorecessive regions of SARS-CoV-2 are critically needed. Here, we report an engineered epitope-focused immunogen design based on a novel horseshoe-shaped natural protein scaffold, named ribonuclease inhibitor 1 (RNH1), that can multiply display of conserved neutralizing epitopes from SARS-CoV-2 S2 stem helix. The designed immunogen RNH1-S1139 demonstrates high binding affinity to S2-specific neutralizing antibodies and elicits robust epitope-targeted antibody responses either through homologous or heterologous vaccination regimens. RNH1-S1139 immune serum has been proven to have similar binding ability against SARS-CoV, SARS-CoV-2 and its variants, providing broad-spectrum protection as a membrane fusion inhibitor. Further studies showed that RNH1 has the potential to serve as a versatile scaffold that displays other helical epitopes from various antigens, including respiratory syncytial virus (RSV) F glycoprotein. Our proposed immunogen engineering strategy via tailored horseshoe-shape nano-scaffold supports the continued development of epitope-focused vaccines as part of a next-generation vaccine design.

Epitope-focused immunogens targeting the hepatitis C virus glycoproteins induce broadly neutralizing antibodies

Hepatitis C virus (HCV) infection causes ~290,000 annual human deaths despite the highly effective antiviral treatment available. Several viral immune evasion mechanisms have hampered the development of an effective vaccine against HCV, among them the remarkable conformational flexibility within neutralization epitopes in the HCV antigens. Here, we report the design of epitope-focused immunogens displaying two distinct HCV cross-neutralization epitopes. We show that these immunogens induce a pronounced, broadly neutralizing antibody response in laboratory and transgenic human antibody mice. Monoclonal human antibodies isolated from immunized human antibody mice specifically recognized the grafted epitopes and neutralized four diverse HCV strains. Our results highlight a promising strategy for developing HCV immunogens and provide an encouraging paradigm for targeting structurally flexible epitopes to improve the induction of neutralizing antibodies.

Atomic molecular dynamics simulation advances of de novo-designed proteins

Proteins are vital biological macromolecules that execute biological functions and form the core of synthetic biological systems. The history of de novo protein has evolved from initial successes in subordinate structural design to more intricate protein creation, challenging the complexities of natural proteins. Recent strides in protein design have leveraged computational methods to craft proteins for functions beyond their natural capabilities. Molecular dynamics (MD) simulations have emerged as a crucial tool for comprehending the structural and dynamic properties of de novo-designed proteins. In this study, we examined the pivotal role of MD simulations in elucidating the sampling methods, force field, water models, stability, and dynamics of de novo-designed proteins, highlighting their potential applications in diverse fields. The synergy between computational modeling and experimental validation continued to play a crucial role in the creation of novel proteins tailored for specific functions and applications.

Advancing sustainable biotechnology through protein engineering

The push for industrial sustainability benefits from the use of enzymes as a replacement for traditional chemistry. Biological catalysts, especially those that have been engineered for increased activity, stability, or novel function, and are often greener than alternative chemical approaches. This Review highlights the role of engineered enzymes (and identifies directions for further engineering efforts) in the application areas of greenhouse gas sequestration, fuel production, bioremediation, and degradation of plastic wastes.

The Cumulative Variations of Respiratory Syncytial Virus Fusion Protein (F) in Ten Consecutive Years in China

BACKGROUND

Variations in the fusion (F) protein of respiratory syncytial virus (RSV) with main antigenic sites I-V and Ø may affect the development of RSV vaccines and therapies.

METHODS

In the study, 30 respiratory specimens positive for RSV were randomly selected from children with acute lower respiratory infections (ALRI) in Beijing every year from 2012 to 2021 for F gene sequencing. Then, 300 F gene sequences and 508 uploaded to GenBank from China were subjected to phylogenetic analysis.

RESULTS

The results indicated the nucleotide identities were 95.4-100% among 446 sequences of RSV A, and 96.3-100% among 362 of RSV B. The most common variant loci were N80K (100.00%) and R213S (97.76%) for site Ø, and V384I/T (98.43%) for site I among sequences of RSV A, and M152I (100.00%), I185V (100.00%), and L172Q/H (94.48%) for site V, and R202Q (99.45%) for site Ø among sequences of RSV B. N276S appears in 95.29% sequences of RSV A, while S276N and N262 I/S appear in 1.38% and 0.55% sequences of RSV B, respectively. No variation was found in all sequences at the binding sites of 14N4 and motavizumab.

CONCLUSIONS

There were cumulative variations of the RSV F gene, especially at some binding sites of antigenic sites.

PB-GPT: An innovative GPT-based model for protein backbone generation

With advanced computational methods, it is now feasible to modify or design proteins for specific functions, a process with significant implications for disease treatment and other medical applications. Protein structures and functions are intrinsically linked to their backbones, making the design of these backbones a pivotal aspect of protein engineering. In this study, we focus on the task of unconditionally generating protein backbones. By means of codebook quantization and compression dictionaries, we convert protein backbone structures into a distinctive coded language and propose a GPT-based protein backbone generation model, PB-GPT. To validate the generalization performance of the model, we trained and evaluated the model on both public datasets and small protein datasets. The results demonstrate that our model has the capability to unconditionally generate elaborate, highly realistic protein backbones with structural patterns resembling those of natural proteins, thus showcasing the significant potential of large language models in protein structure design.

Rational structure-guided design of a blood stage malaria vaccine immunogen presenting a single epitope from PfRH5

There is an urgent need for improved malaria vaccine immunogens. Invasion of erythrocytes by Plasmodium falciparum is essential for its life cycle, preceding symptoms of disease and parasite transmission. Antibodies which target PfRH5 are highly effective at preventing erythrocyte invasion and the most potent growth-inhibitory antibodies bind a single epitope. Here we use structure-guided approaches to design a small synthetic immunogen, RH5-34EM which recapitulates this epitope. Structural biology and biophysics demonstrate that RH5-34EM is correctly folded and binds neutralising monoclonal antibodies with nanomolar affinity. In immunised rats, RH5-34EM induces PfRH5-targeting antibodies that inhibit parasite growth. While PfRH5-specific antibodies were induced at a lower concentration by RH5-34EM than by PfRH5, RH5-34EM induced antibodies that were a thousand-fold more growth-inhibitory as a factor of PfRH5-specific antibody concentration. Finally, we show that priming with RH5-34EM and boosting with PfRH5 achieves the best balance between antibody quality and quantity and induces the most effective growth-inhibitory response. This rationally designed vaccine immunogen is now available for use as part of future malaria vaccines, alone or in combination with other immunogens.

A study on loading multiple epitopes with a single peptide

Epitopes, the basic functional units of antigens, hold great significance in the field of immunology. However, the structure and composition of epitopes and their interactions with antibodies remain unclear, which limits in-depth studies on epitopes and the development of subunit vaccines. In a previous study on the localization of anti-influenza HA monoclonal antibodies (mAbs), three strains with different characteristics reacted with the same peptide. In this study, by conventional immunological assays, computer homology modeling, and molecular docking simulations, we found that (1) the peptide could bind to three strains of mAbs with different reaction characteristics utilizing different combinations of immunodominant groups. (2) By computer molecular docking and simulation methods, the immunodominant groups on the two peptides could be combined into a multi-epitope peptide bound to six strains of mAbs. We established a method for multi-epitope peptide recombination from these immunodominant groups. (3) The immune effect of the recombinant multi-epitope peptide was better than that of a single peptide. Our findings facilitate the understanding of the composition of antigen epitopes and provide a theoretical and experimental basis for developing polyvalent vaccines and understanding immune responses at the molecular level.

A novel Candida albicans Als3, Hwp1 and Met6 derived complex peptide protects mice against hematogenously induced candidiasis

Candida albicans can cause superficial or systemic infections in humans, particularly in immunocompromised individuals. Vaccination strategies targeting specific antigens of C. albicans have shown promise in providing protection against invasive candidiasis. This study aimed to evaluate the immuno-protective capacity of a KLH conjugated complex peptide, 3P-KLH, containing epitopes from C. albicans antigens Als3, Hwp1, and Met6 in a murine model of hematogenously induced candidiasis. Mice immunized with 3P-KLH raised a specific antibody response, and protection against C. albicans infection was assessed. Immunized mice exhibited significantly lower fungal load in their kidneys compared to the control group. Moreover, 37.5 % of immunized mice survived 21 days after the infection, while all control animals died within the first nine days. These findings suggest that the 3P-KLH complex peptide, targeting C. albicans key antigens, elicits a protective immune response and reduces the severity of systemic Candida infection. In addition, the high binding affinity of the selected epitopes with MHC II alleles further supports the potential immunogenicity of this peptide in humans. This research provides insights into the development of novel immunotherapeutic approaches against invasive candidiasis.

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.

Structure-based design of a Plasmodium vivax Duffy-binding protein immunogen focuses the antibody response to functional epitopes

The Duffy-binding protein (DBP) is a promising antigen for a malaria vaccine that would protect against clinical symptoms caused by Plasmodium vivax infection. Region II of DBP (DBP-II) contains the receptor-binding domain that engages host red blood cells, but DBP-II vaccines elicit many non-neutralizing antibodies that bind distal to the receptor-binding surface. Here, we engineered a truncated DBP-II immunogen that focuses the immune response to the receptor-binding surface. This immunogen contains the receptor-binding subdomain S1S2 and lacks the immunodominant subdomain S3. Structure-based computational design of S1S2 identified combinatorial amino acid changes that stabilized the isolated S1S2 without perturbing neutralizing epitopes. This immunogen elicited DBP-II-specific antibodies in immunized mice that were significantly enriched for blocking activity compared to the native DBP-II antigen. This generalizable design process successfully stabilized an integral core fragment of a protein and focused the immune response to desired epitopes to create a promising new antigen for malaria vaccine development.

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

SEMA 2.0: web-platform for B-cell conformational epitopes prediction using artificial intelligence

Prediction of conformational B-cell epitopes is a crucial task in vaccine design and development. In this work, we have developed SEMA 2.0, a user-friendly web platform that enables the research community to tackle the B-cell epitopes prediction problem using state-of-the-art protein language models. SEMA 2.0 offers comprehensive research tools for sequence- and structure-based conformational B-cell epitopes prediction, accurate identification of N-glycosylation sites, and a distinctive module for comparing the structures of antigen B-cell epitopes enhancing our ability to analyze and understand its immunogenic properties. SEMA 2.0 website https://sema.airi.net is free and open to all users and there is no login requirement. Source code is available at https://github.com/AIRI-Institute/SEMAi.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models

Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.

Systematic identification of structure-specific protein-protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

Vaccination induces broadly neutralizing antibody precursors to HIV gp41

A key barrier to the development of vaccines that induce broadly neutralizing antibodies (bnAbs) against human immunodeficiency virus (HIV) and other viruses of high antigenic diversity is the design of priming immunogens that induce rare bnAb-precursor B cells. The high neutralization breadth of the HIV bnAb 10E8 makes elicitation of 10E8-class bnAbs desirable; however, the recessed epitope within gp41 makes envelope trimers poor priming immunogens and requires that 10E8-class bnAbs possess a long heavy chain complementarity determining region 3 (HCDR3) with a specific binding motif. We developed germline-targeting epitope scaffolds with affinity for 10E8-class precursors and engineered nanoparticles for multivalent display. Scaffolds exhibited epitope structural mimicry and bound bnAb-precursor human naive B cells in ex vivo screens, protein nanoparticles induced bnAb-precursor responses in stringent mouse models and rhesus macaques, and mRNA-encoded nanoparticles triggered similar responses in mice. Thus, germline-targeting epitope scaffold nanoparticles can elicit rare bnAb-precursor B cells with predefined binding specificities and HCDR3 features.

Bioluminescent detection of viral surface proteins using branched multivalent protein switches

Fast and reliable virus diagnostics is key to prevent the spread of viruses in populations. A hallmark of viruses is the presence of multivalent surface proteins, a property that can be harnessed to control conformational switching in sensor proteins. Here, we introduce a new sensor platform (dark-LUX) for the detection of viral surface proteins consisting of a general bioluminescent framework that can be post-translationally functionalized with separately expressed binding domains. The platform relies on (1) plug-and-play bioconjugation of different binding proteins via SpyTag/SpyCatcher technology to create branched protein structures, (2) an optimized turn-on bioluminescent switch based on complementation of the split-luciferase NanoBiT upon target binding and (3) straightforward exploration of the protein linker space. The influenza A virus (IAV) surface proteins hemagglutinin (HA) and neuraminidase (NA) were used as relevant multivalent targets to establish proof of principle and optimize relevant parameters such as linker properties, choice of target binding domains and the optimal combination of the competing NanoBiT components SmBiT and DarkBiT. The sensor framework allows rapid conjugation and exchange of various binding domains including scFvs, nanobodies and de novo designed binders for a variety of targets, including the construction of a heterobivalent switch that targets the head and stem region of hemagglutinin. The modularity of the platform thus allows straightforward optimization of binding domains and scaffold properties for existing viral targets, and is well suited to quickly adapt bioluminescent sensor proteins to effectively detect newly evolving viral epitopes.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

Design of complicated all-α protein structures

A wide range of de novo protein structure designs have been achieved, but the complexity of naturally occurring protein structures is still far beyond these designs. Here, to expand the diversity and complexity of de novo designed protein structures, we sought to develop a method for designing 'difficult-to-describe' α-helical protein structures composed of irregularly aligned α-helices like globins. Backbone structure libraries consisting of a myriad of α-helical structures with five or six helices were generated by combining 18 helix-loop-helix motifs and canonical α-helices, and five distinct topologies were selected for de novo design. The designs were found to be monomeric with high thermal stability in solution and fold into the target topologies with atomic accuracy. This study demonstrated that complicated α-helical proteins are created using typical building blocks. The method we developed will enable us to explore the universe of protein structures for designing novel functional proteins.

Is Novelty Predictable?

Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal applications ranging from drug development and plastic degradation to carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion is relevant to machine learning-based design more broadly.

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

Bringing immunofocusing into focus

Immunofocusing is a strategy to create immunogens that redirect humoral immune responses towards a targeted epitope and away from non-desirable epitopes. Immunofocusing methods often aim to develop "universal" vaccines that provide broad protection against highly variant viruses such as influenza virus, human immunodeficiency virus (HIV-1), and most recently, severe acute respiratory syndrome coronavirus (SARS-CoV-2). We use existing examples to illustrate five main immunofocusing strategies-cross-strain boosting, mosaic display, protein dissection, epitope scaffolding, and epitope masking. We also discuss obstacles for immunofocusing like immune imprinting. A thorough understanding, advancement, and application of the methods we outline here will enable the design of high-resolution vaccines that protect against future viral outbreaks.

Virus-Receptor Interactions and Receptor-Mediated Virus Entry into Host Cells

The virus particles described in the previous chapters of this book are vehicles that transmit the viral genome and the infection from cell to cell. To initiate the infective cycle, the viral genome must therefore translocate from the viral particle to the cell cytoplasm. Via distinct proteins or motifs in their outermost shell, the particles of animal viruses or bacteriophages attach initially to specific receptors on the host cell surface. These viral receptors thus mediate penetration of the viral genome inside the cell, where the intracellular infective cycle starts. The presence of these receptors on the cell surface is a principal determinant of virus-host tropism. Viruses can use diverse types of molecules to attach to and enter into cells. In addition, virus-receptor recognition can evolve over the course of an infection, and viral variants with distinct receptor-binding specificities and tropism can appear. The identification of viral receptors and the characterization of virus-receptor interactions have been major research goals in virology. In this chapter, we will describe, from a structural perspective, several virus-receptor interactions and the active role of receptor molecules in virus cell entry.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models

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.

Rapidly determining the 3D structure of proteins by surface-enhanced Raman spectroscopy

Despite great advances in protein structure analysis, label-free and ultrasensitive methods to obtain the natural and dynamic three-dimensional (3D) structures are still urgently needed. Surface-enhanced Raman spectroscopy (SERS) can be a good candidate, whereas the complexity originated from the interactions between the protein and the gradient surface electric field makes it extremely challenging to determine the protein structure. Here, we propose a deciphering strategy for accurate determination of 3D protein structure from experimental SERS spectra in seconds by simply summing SERS spectra of isolated amino acids in electric fields of different strength with their orientations in protein. The 3D protein structure can be reconstructed by comparing the experimental spectra obtained in a well-defined gap-mode SERS configuration with the simulated spectra. The gradient electric field endows SERS with a unique advantage to section biomolecules with atomic precision, which makes SERS a competent tool for monitoring biomolecular events under physiological conditions.

A new age in protein design empowered by deep learning

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Hyper Thermostability and Liquid-Crystal-Like Properties of Designed α-Helical Peptide Nanofibers

Structural and thermodynamic transitions of artificially designed α-helical nanofibers were investigated using eight peptide variants, including four peptides with amide-modified carboxyl termini (CB peptides) and four unmodified peptides (CF peptides). Temperature-dependent circular dichroism spectroscopy and differential scanning calorimetry showed that CB peptides exhibit thermostability up to 50 °C higher than CF peptides. As a result, one of the denaturation temperatures approached nearly 130 °C, which is exceptionally high for a biomacromolecule. Thermodynamic analysis and microscopy observations also showed that CB peptides undergo a thermal transition similar to the phase transition in liquid crystals. In addition, one of the peptides showed a sharp and highly cooperative transition with a small enthalpy change at around 25 °C, which was ascribed to a giga-bundle burst of the molecular assembly. These macroscopic changes in the thermostability and crystallinity of CB peptides may be attributed to an increased amphiphilicity of the molecule in the direction of the helix axis, originating from the microscopic modification of the carboxyl-terminus.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Exploration of novel αβ-protein folds through de novo design

A fundamental question in protein evolution is whether nature has exhaustively sampled nearly all possible protein folds throughout evolution, or whether a large fraction of the possible folds remains unexplored. To address this question, we defined a set of rules for β-sheet topology to predict novel αβ-folds and carried out a systematic de novo protein design exploration of the novel αβ-folds predicted by the rules. The designs for all eight of the predicted novel αβ-folds with a four-stranded β-sheet, including a knot-forming one, folded into structures close to the design models. Further, the rules predicted more than 10,000 novel αβ-folds with five- to eight-stranded β-sheets; this number far exceeds the number of αβ-folds observed in nature so far. This result suggests that a vast number of αβ-folds are possible, but have not emerged or have become extinct due to evolutionary bias.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

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-β 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-β 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.

De novo design of protein structure and function with RFdiffusion

There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Structural basis for respiratory syncytial virus and human metapneumovirus neutralization

Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) continue to be a global burden to infants, the elderly, and immunocompromised individuals. In the past ten years, there has been substantial progress in the development of new vaccine candidates and therapies against these viruses. These advancements were guided by the structural elucidation of the major surface glycoproteins for these viruses, the fusion (F) protein and attachment (G) protein. The identification of immunodominant epitopes on the RSV F and hMPV F proteins has expanded current knowledge on antibody-mediated immune responses, which has led to new approaches for vaccine and therapeutic development through the stabilization of pre-fusion constructs of the F protein and pre-fusion-specific monoclonal antibodies with high potency and efficacy. In this review, we describe structural characteristics of known antigenic sites on the RSV and hMPV proteins, their influence on the immune response, and current progress in vaccine and therapeutic development.

Antisera Produced Using an E. coli-Expressed SARS-CoV-2 RBD and Complemented with a Minimal Dose of Mammalian-Cell-Expressed S1 Subunit of the Spike Protein Exhibits Improved Neutralization

E. coli-expressed proteins could provide a rapid, cost-effective, and safe antigen for subunit vaccines, provided we can produce them in a properly folded form inducing neutralizing antibodies. Here, we use an E. coli-expressed SARS-CoV-2 receptor-binding domain (RBD) of the spike protein as a model to examine whether it yields neutralizing antisera with effects comparable to those generated by the S1 subunit of the spike protein (S1 or S1 subunit, thereafter) expressed in mammalian cells. We immunized 5-week-old Jcl-ICR female mice by injecting RBD (30 µg) and S1 subunit (5 µg) according to four schemes: two injections 8 weeks apart with RBD (RBD/RBD), two injections with S1 (S1/S1), one injection with RBD, and the second one with S1 (RBD/S1), and vice versa (S1/RBD). Ten weeks after the first injection (two weeks after the second injection), all combinations induced a strong immune response with IgG titer > 10⁵ (S1/RBD < S1/S1 < RBD/S1 < RBD/RBD). In addition, the neutralization effect of the antisera ranked as S1/RBD~RBD/S1 (80%) > S1/S1 (56%) > RBD/RBD (42%). These results indicate that two injections with E. coli-expressed RBD, or mammalian-cell-produced spike S1 subunit alone, can provide some protection against SARS-CoV-2, but a mixed injection scheme yields significantly higher protection.

GDockScore: a graph-based protein-protein docking scoring function

Summary

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.

Availability and implementation

The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

In silico evolution of protein binders with deep learning models for structure prediction and sequence design

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. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by 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 tested 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.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

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 Gα q and its effector PLC-β isozymes. Several of the designed proteins remain folded above 90°C and bind to Gα q with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gα q , the designed proteins inhibit activation of PLC-β 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.

statement for broader audience

Engineered proteins that bind to specific target proteins are useful as research reagents, diagnostics, and therapeutics. We used computational protein design to engineer de novo proteins that bind and competitively inhibit the G protein, Gα q , which is an oncogene for uveal melanomas. This computational method is a general approach that should be useful for designing competitive inhibitors against other proteins of interest.

Potent transmission-blocking monoclonal antibodies from naturally exposed individuals target a conserved epitope on Plasmodium falciparum Pfs230

Pfs230 is essential for Plasmodium falciparum transmission to mosquitoes and is the protein targeted by the most advanced malaria-transmission-blocking vaccine candidate. Prior understanding of functional epitopes on Pfs230 is based on two monoclonal antibodies (mAbs) with moderate transmission-reducing activity (TRA), elicited from subunit immunization. Here, we screened the B cell repertoire of two naturally exposed individuals possessing serum TRA and identified five potent mAbs from sixteen Pfs230 domain-1-specific mAbs. Structures of three potent and three low-activity antibodies bound to Pfs230 domain 1 revealed four distinct epitopes. Highly potent mAbs from natural infection recognized a common conformational epitope that is highly conserved across P. falciparum field isolates, while antibodies with negligible TRA derived from natural infection or immunization recognized three distinct sites. Our study provides molecular blueprints describing P. falciparum TRA, informed by contrasting potent and non-functional epitopes elicited by natural exposure and vaccination.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

Bioluminescence Goes Dark: Boosting the Performance of Bioluminescent Sensor Proteins Using Complementation Inhibitors

Bioluminescent sensor proteins have recently gained popularity in both basic research and point-of-care diagnostics. Sensor proteins based on intramolecular complementation of split NanoLuc are particularly attractive because their intrinsic modular design enables for systematic tuning of sensor properties. Here we show how the sensitivity of these sensors can be enhanced by the introduction of catalytically inactive variants of the small SmBiT subunit (DarkBiTs) as intramolecular inhibitors. Starting from previously developed bioluminescent antibody sensor proteins (LUMABS), we developed single component, biomolecular switches with a strongly reduced background signal for the detection of three clinically relevant antibodies, anti-HIV1-p17, cetuximab (CTX), and an RSV neutralizing antibody (101F). These new dark-LUMABS sensors showed 5-13-fold increases in sensitivity which translated into lower limits of detection. The use of DarkBiTs as competitive intramolecular inhibitor domains is not limited to the LUMABS sensor family and might be used to boost the performance of other bioluminescent sensor proteins based on split luciferase complementation.

Computational design of vaccine immunogens

Computational protein engineering has enabled the rational design of customized proteins, which has propelled both sequence-based and structure-based immunogen engineering and delivery. By discerning antigenic determinants of viral pathogens, computational methods have been implemented to successfully engineer representative viral strains able to elicit broadly neutralizing responses or present antigenic sites of viruses for focused immune responses. Combined with improvements in customizable nanoparticle design, immunogens are multivalently displayed to enhance immune responses. These rationally designed immunogens offer unique and powerful approaches to engineer vaccines for pathogens, which have eluded traditional approaches.