An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway.

While natural terpenoid cyclases generate complex terpenoid structures via cationic mechanisms, alternative radical cyclization pathways are underexplored. The metal-catalysed H-atom transfer reaction (M-HAT) offers an attractive means for hydrofunctionalizing olefins, providing access to terpenoid-like structures. Artificial metalloenzymes offer a promising strategy for introducing M-HAT reactivity into a protein scaffold. Here we report our efforts towards engineering an artificial radical cyclase (ARCase), resulting from anchoring a biotinylated [Co(Schiff-base)] cofactor within an engineered chimeric streptavidin. After two rounds of directed evolution, a double mutant catalyses a radical cyclization to afford bicyclic products with a cis-5-6-fused ring structure and up to 97% enantiomeric excess. The involvement of a histidine ligation to the Co cofactor is confirmed by crystallography. A time course experiment reveals a cascade reaction catalysed by the ARCase, combining a radical cyclization with a conjugate reduction. The ARCase exhibits tolerance towards variations in the dienone substrate, highlighting its potential to access terpenoid scaffolds.

Computational design of soluble and functional membrane protein analogues.

De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

Antibody-peptide conjugates deliver covalent inhibitors blocking oncogenic cathepsins.

Cysteine cathepsins are a family of proteases that are relevant therapeutic targets for the treatment of different cancers and other diseases. However, no clinically approved drugs for these proteins exist, as their systemic inhibition can induce deleterious side effects. To address this problem, we developed a modular antibody-based platform for targeted drug delivery by conjugating non-natural peptide inhibitors (NNPIs) to antibodies. NNPIs were functionalized with reactive warheads for covalent inhibition, optimized with deep saturation mutagenesis and conjugated to antibodies to enable cell-type-specific delivery. Our antibody-peptide inhibitor conjugates specifically blocked the activity of cathepsins in different cancer cells, as well as osteoclasts, and showed therapeutic efficacy in vitro and in vivo. Overall, our approach allows for the rapid design of selective cathepsin inhibitors and can be generalized to inhibit a broad class of proteases in cancer and other diseases.

Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation.

Evolutionary changes in the hepatitis B virus (HBV) genome could reflect its adaptation to host-induced selective pressure. Leveraging paired human exome and ultra-deep HBV genome-sequencing data from 567 affected individuals with chronic hepatitis B, we comprehensively searched for the signatures of this evolutionary process by conducting "genome-to-genome" association tests between all human genetic variants and viral mutations. We identified significant associations between an East Asian-specific missense variant in the gene encoding the HBV entry receptor NTCP (rs2296651, NTCP S267F) and mutations within the receptor-binding region of HBV preS1. Through in silico modeling and in vitro preS1-NTCP binding assays, we observed that the associated HBV mutations are in proximity to the NTCP variant when bound and together partially increase binding affinity to NTCP S267F. Furthermore, we identified significant associations between HLA-A variation and viral mutations in HLA-A-restricted T cell epitopes. We used in silico binding prediction tools to evaluate the impact of the associated HBV mutations on HLA presentation and observed that mutations that result in weaker binding affinities to their cognate HLA alleles were enriched. Overall, our results suggest the emergence of HBV escape mutations that might alter the interaction between HBV PreS1 and its cellular receptor NTCP during viral entry into hepatocytes and confirm the role of HLA class I restriction in inducing HBV epitope variations.

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 design of soluble functional analogues of integral membrane proteins.

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

A Synthetic Multivalent Lipopeptide Derived from Pam3CSK4 with Irreversible Influenza Inhibition and Immuno-Stimulating Effects.

The activation of the host adaptive immune system is crucial for eliminating viruses. However, influenza infection often suppresses the innate immune response that precedes adaptive immunity, and the adaptive immune responses are typically delayed. Dendritic cells, serving as professional antigen-presenting cells, have a vital role in initiating the adaptive immune response. In this study, an immuno-stimulating antiviral system (ISAS) is introduced, which is composed of the immuno-stimulating adjuvant lipopeptide Pam3CSK4 that acts as a scaffold onto which it is covalently bound 3 to 4 influenza-inhibiting peptides. The multivalent display of peptides on the scaffold leads to a potent inhibition against H1N1 (EC50  = 20 nM). Importantly, the resulting lipopeptide, Pam3FDA, shows an irreversible inhibition mechanism. The chemical modification of peptides on the scaffold maintains Pam3CSK4's ability to stimulate dendritic cell maturation, thereby rendering Pam3FDA a unique antiviral. This is attributed to its immune activation capability, which also acts in synergy to expedite viral elimination.

Systematic review and meta-analysis of diagnostic test accuracy of ST-segment elevation for acute coronary occlusion

OBJECTIVE: To evaluate the diagnostic sensitivity and specificity of ST-segment elevation on a 12­lead ECG in detecting ACO across any coronary artery, challenging the current STEMI-NSTEMI paradigm. METHODS: Studies from MEDLINE and Scopus (2012-2023) comparing ECG findings with coronary angiograms were systematically reviewed and analyzed following PRISMA-DTA guidelines. QUADAS-2 assessed the risk of bias. STUDY SELECTION: Studies included focused on AMI patients and provided data enabling the construction of contingency tables for sensitivity and specificity calculation, excluding those with non-ACS conditions, outdated STEMI criteria, or a specific focus on bundle branch blocks or other complex diagnoses. Data were extracted systematically and pooled test accuracy estimates were computed using MetaDTA software, employing bivariate analyses for within- and between-study variation. The primary outcomes measured were the sensitivity and specificity of ST-segment elevation in detecting ACO. RESULTS: Three studies with 23,704 participants were included. The pooled sensitivity of ST-segment elevation for detecting ACO was 43.6% (95% CI: 34.7%-52.9%), indicating that over half of ACO cases may not exhibit ST-segment elevation criteria. The specificity was 96.5% (95% CI: 91.2%-98.7%). Additional analysis using the OMI-NOMI strategy showed improved sensitivity (78.1%, 95% CI: 62.7%-88.3%) while maintaining similar specificity (94.4%, 95% CI: 88.6%-97.3%). CONCLUSION: The findings reveal a significant diagnostic gap in the current STEMI-NSTEMI paradigm, with over half of ACO cases potentially lacking ST-segment elevation. The OMI-NOMI strategy could offer an improved diagnostic approach. The high heterogeneity and limited number of studies necessitate cautious interpretation and further research in diverse settings.

Systematic review and meta-analysis of diagnostic test accuracy of ST-segment elevation for acute coronary occlusion.

OBJECTIVE: To evaluate the diagnostic sensitivity and specificity of ST-segment elevation on a 12­lead ECG in detecting ACO across any coronary artery, challenging the current STEMI-NSTEMI paradigm. METHODS: Studies from MEDLINE and Scopus (2012-2023) comparing ECG findings with coronary angiograms were systematically reviewed and analyzed following PRISMA-DTA guidelines. QUADAS-2 assessed the risk of bias. STUDY SELECTION: Studies included focused on AMI patients and provided data enabling the construction of contingency tables for sensitivity and specificity calculation, excluding those with non-ACS conditions, outdated STEMI criteria, or a specific focus on bundle branch blocks or other complex diagnoses. Data were extracted systematically and pooled test accuracy estimates were computed using MetaDTA software, employing bivariate analyses for within- and between-study variation. The primary outcomes measured were the sensitivity and specificity of ST-segment elevation in detecting ACO. RESULTS: Three studies with 23,704 participants were included. The pooled sensitivity of ST-segment elevation for detecting ACO was 43.6% (95% CI: 34.7%-52.9%), indicating that over half of ACO cases may not exhibit ST-segment elevation. The specificity was 96.5% (95% CI: 91.2%-98.7%). Additional analysis using the OMI-NOMI strategy showed improved sensitivity (78.1%, 95% CI: 62.7%-88.3%) while maintaining similar specificity (94.4%, 95% CI: 88.6%-97.3%). CONCLUSION: The findings reveal a significant diagnostic gap in the current STEMI-NSTEMI paradigm, with over half of ACO cases potentially lacking ST-segment elevation. The OMI-NOMI strategy could offer an improved diagnostic approach. The high heterogeneity and limited number of studies necessitate cautious interpretation and further research in diverse settings.

An atlas of protein homo-oligomerization across domains of life.

Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a scalable strategy based on AlphaFold2 to predict homo-oligomeric assemblies across four proteomes spanning the tree of life. Our results suggest that approximately 45% of an archaeal proteome and a bacterial proteome and 20% of two eukaryotic proteomes form homomers. Our predictions accurately capture protein homo-oligomerization, recapitulate megadalton complexes, and unveil hundreds of homo-oligomer types, including three confirmed experimentally by structure determination. Integrating these datasets with omics information suggests that a majority of known protein complexes are symmetric. Finally, these datasets provide a structural context for interpreting disease mutations and reveal coiled-coil regions as major enablers of quaternary structure evolution in human. Our strategy is applicable to any organism and provides a comprehensive view of homo-oligomerization in proteomes.

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.

Protein-based bandpass filters for controlling cellular signaling with chemical inputs.

Biological signal processing is vital for cellular function. Similar to electronic circuits, cells process signals via integrated mechanisms. In electronics, bandpass filters transmit frequencies with defined ranges, but protein-based counterparts for controlled responses are lacking in engineered biological systems. Here, we rationally design protein-based, chemically responsive bandpass filters (CBPs) showing OFF-ON-OFF patterns that respond to chemical concentrations within a specific range and reject concentrations outside that range. Employing structure-based strategies, we designed a heterodimeric construct that dimerizes in response to low concentrations of a small molecule (ON), and dissociates at high concentrations of the same molecule (OFF). The CBPs have a multidomain architecture in which we used known drug receptors, a computationally designed protein binder and small-molecule inhibitors. This modular system allows fine-tuning for optimal performance in terms of bandwidth, response, cutoff and fold changes. The CBPs were used to regulate cell surface receptor signaling pathways to control cellular activities in engineered cells.

Rational design of small-molecule responsive protein switches.

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches.

Rules and mechanisms governing G protein coupling selectivity of GPCRs.

G protein-coupled receptors (GPCRs) convert extracellular stimuli into intracellular signaling by coupling to heterotrimeric G proteins of four classes: Gi/o, Gq, Gs, and G12/13. However, our understanding of the G protein selectivity of GPCRs is incomplete. Here, we quantitatively measure the enzymatic activity of GPCRs in living cells and reveal the G protein selectivity of 124 GPCRs with the exact rank order of their G protein preference. Using this information, we establish a classification of GPCRs by functional selectivity, discover the existence of a G12/13-coupled receptor, G15-coupled receptors, and a variety of subclasses for Gi/o-, Gq-, and Gs-coupled receptors, culminating in development of the predictive algorithm of G protein selectivity. We further identify the structural determinants of G protein selectivity, allowing us to synthesize non-existent GPCRs with de novo G protein selectivity and efficiently identify putative pathogenic variants.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Rational Design of Chemically Controlled Antibodies and Protein Therapeutics.

Protein-based therapeutics, such as monoclonal antibodies and cytokines, are important therapies for various pathophysiological conditions such as oncology, autoimmune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely, cytokine storm syndrome, organ failure, and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small-molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used the Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon the addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into anti-CTLA4, anti-HER2 antibodies, or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon the addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

De novo protein design by inversion of the AlphaFold structure prediction network.

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that 7 out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface's hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus, such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design.

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.

Septic Sternoclavicular Arthritis: An Uncommon Manifestation in the Context of Immunosuppression Following SARS-CoV-2 Infection.

This case report discusses a rare occurrence of septic arthritis in the sternoclavicular joint (SCJ) following SARS-CoV-2 infection-induced immunosuppression in a 94-year-old patient. Despite its rarity, the case underscores the importance of recognizing unusual manifestations of COVID-19, emphasizing the need for healthcare providers to consider COVID-19-induced immunosuppression in differential diagnoses. Swift diagnosis, surgical intervention, and appropriate antibiotics led to a favorable outcome, highlighting the significance of a multidisciplinary approach.

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.