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.

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.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells.

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

A Global Map of Lipid-Binding Proteins and Their Ligandability in Cells.

Lipids play central roles in physiology and disease, where their structural, metabolic, and signaling functions often arise from interactions with proteins. Here, we describe a set of lipid-based chemical proteomic probes and their global interaction map in mammalian cells. These interactions involve hundreds of proteins from diverse functional classes and frequently occur at sites of drug action. We determine the target profiles for several drugs across the lipid-interaction proteome, revealing that its ligandable content extends far beyond traditionally defined categories of druggable proteins. In further support of this finding, we describe a selective ligand for the lipid-binding protein nucleobindin-1 (NUCB1) and show that this compound perturbs the hydrolytic and oxidative metabolism of endocannabinoids in cells. The described chemical proteomic platform thus provides an integrated path to both discover and pharmacologically characterize a wide range of proteins that participate in lipid pathways in cells.

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.

A computationally designed inhibitor of an Epstein-Barr viral Bcl-2 protein induces apoptosis in infected cells.

Because apoptosis of infected cells can limit virus production and spread, some viruses have co-opted prosurvival genes from the host. This includes the Epstein-Barr virus (EBV) gene BHRF1, a homolog of human Bcl-2 proteins that block apoptosis and are associated with cancer. Computational design and experimental optimization were used to generate a novel protein called BINDI that binds BHRF1 with picomolar affinity. BINDI recognizes the hydrophobic cleft of BHRF1 in a manner similar to other Bcl-2 protein interactions but makes many additional contacts to achieve exceptional affinity and specificity. BINDI induces apoptosis in EBV-infected cancer lines, and when delivered with an antibody-targeted intracellular delivery carrier, BINDI suppressed tumor growth and extended survival in a xenograft disease model of EBV-positive human lymphoma. High-specificity-designed proteins that selectively kill target cells may provide an advantage over the toxic compounds used in current generation antibody-drug conjugates.

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.

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.

Tyr-Asp inhibition of glyceraldehyde 3-phosphate dehydrogenase affects plant redox metabolism.

How organisms integrate metabolism with the external environment is a central question in biology. Here, we describe a novel regulatory small molecule, a proteogenic dipeptide Tyr-Asp, which improves plant tolerance to oxidative stress by directly interfering with glucose metabolism. Specifically, Tyr-Asp inhibits the activity of a key glycolytic enzyme, glyceraldehyde 3-phosphate dehydrogenase (GAPC), and redirects glucose toward pentose phosphate pathway (PPP) and NADPH production. In line with the metabolic data, Tyr-Asp supplementation improved the growth performance of both Arabidopsis and tobacco seedlings subjected to oxidative stress conditions. Moreover, inhibition of Arabidopsis phosphoenolpyruvate carboxykinase (PEPCK) activity by a group of branched-chain amino acid-containing dipeptides, but not by Tyr-Asp, points to a multisite regulation of glycolytic/gluconeogenic pathway by dipeptides. In summary, our results open the intriguing possibility that proteogenic dipeptides act as evolutionarily conserved small-molecule regulators at the nexus of stress, protein degradation, and metabolism.

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.

A generic framework for hierarchical de novo protein design.

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

High exposure of global tree diversity to human pressure.

Safeguarding Earth's tree diversity is a conservation priority due to the importance of trees for biodiversity and ecosystem functions and services such as carbon sequestration. Here, we improve the foundation for effective conservation of global tree diversity by analyzing a recently developed database of tree species covering 46,752 species. We quantify range protection and anthropogenic pressures for each species and develop conservation priorities across taxonomic, phylogenetic, and functional diversity dimensions. We also assess the effectiveness of several influential proposed conservation prioritization frameworks to protect the top 17% and top 50% of tree priority areas. We find that an average of 50.2% of a tree species' range occurs in 110-km grid cells without any protected areas (PAs), with 6,377 small-range tree species fully unprotected, and that 83% of tree species experience nonnegligible human pressure across their range on average. Protecting high-priority areas for the top 17% and 50% priority thresholds would increase the average protected proportion of each tree species' range to 65.5% and 82.6%, respectively, leaving many fewer species (2,151 and 2,010) completely unprotected. The priority areas identified for trees match well to the Global 200 Ecoregions framework, revealing that priority areas for trees would in large part also optimize protection for terrestrial biodiversity overall. Based on range estimates for >46,000 tree species, our findings show that a large proportion of tree species receive limited protection by current PAs and are under substantial human pressure. Improved protection of biodiversity overall would also strongly benefit global tree diversity.

The involvement of allosteric effectors and post-translational modifications in the control of plant central carbon metabolism.

Plant metabolism is finely orchestrated to allow the occurrence of complementary and sometimes opposite metabolic pathways. In part this is achieved by the allosteric regulation of enzymes, which has been a cornerstone of plant research for many decades. The completion of the Arabidopsis genome and the development of the associated toolkits for Arabidopsis research moved the focus of many researchers to other fields. This is reflected by the increasing number of high-throughput proteomic studies, mainly focused on post-translational modifications. However, follow-up 'classical' biochemical studies to assess the functions and upstream signaling pathways responsible for such modifications have been scarce. In this work, we review the basic concepts of allosteric regulation of enzymes involved in plant carbon metabolism, comprising photosynthesis and photorespiration, starch and sucrose synthesis, glycolysis and gluconeogenesis, the oxidative pentose phosphate pathway and the tricarboxylic acid cycle. Additionally, we revisit the latest results on the allosteric control of the enzymes involved in these pathways. To conclude, we elaborate on the current methods for studying protein-metabolite interactions, which we consider will become crucial for discoveries in the future.

Dimensions of invasiveness: Links between local abundance, geographic range size, and habitat breadth in Europe's alien and native floras.

Understanding drivers of success for alien species can inform on potential future invasions. Recent conceptual advances highlight that species may achieve invasiveness via performance along at least three distinct dimensions: 1) local abundance, 2) geographic range size, and 3) habitat breadth in naturalized distributions. Associations among these dimensions and the factors that determine success in each have yet to be assessed at large geographic scales. Here, we combine data from over one million vegetation plots covering the extent of Europe and its habitat diversity with databases on species' distributions, traits, and historical origins to provide a comprehensive assessment of invasiveness dimensions for the European alien seed plant flora. Invasiveness dimensions are linked in alien distributions, leading to a continuum from overall poor invaders to super invaders-abundant, widespread aliens that invade diverse habitats. This pattern echoes relationships among analogous dimensions measured for native European species. Success along invasiveness dimensions was associated with details of alien species' introduction histories: earlier introduction dates were positively associated with all three dimensions, and consistent with theory-based expectations, species originating from other continents, particularly acquisitive growth strategists, were among the most successful invaders in Europe. Despite general correlations among invasiveness dimensions, we identified habitats and traits associated with atypical patterns of success in only one or two dimensions-for example, the role of disturbed habitats in facilitating widespread specialists. We conclude that considering invasiveness within a multidimensional framework can provide insights into invasion processes while also informing general understanding of the dynamics of species distributions.

Comparative life-history strategies in three Lebiasinidae (Characiformes) in a Rio Negro tributary, Brazilian Amazon.

Reproductive traits co-evolve and form successful life-history strategies adapted to the biology and environment of a particular taxon, maximizing offspring and species survival chances, therefore studies investigating differences in adaptive traits across different environments can enhance our understanding of the natural selection process and evolution. Herein, we address whether the reproductive strategies of phylogenetically closely related fishes are influenced by habitat predictability, using three species of the Lebiasinidae family as models. The predominance of larger and mature individuals during the flood season, with high waters characterized by smaller immature individuals, suggests a seasonal reproductive strategy for Nannostomus trifasciatus. Copella callolepis, which inhabits both habitats, also showed a single reproductive peak. However, compared to N. trifasciatus, this species displayed late spawning, restricted to the flood season, as indicated by the higher abundance of larger and mature individuals during this period and the presence of smaller (juveniles) and spawned individuals in the following season. The reproductive tactics observed in N. marginatus differed significantly from the single reproductive peak of the other species, as two reproductive peaks were observed: one during the flood season and another during the low water season. In conclusion, our study demonstrates that the environment strongly influences reproductive strategies for lebiasinids. N. marginatus, restricted to small water bodies, exhibited an opportunistic reproductive strategy, whereas the species inhabiting main rivers, N. trifasciatus and C. callolepis, exhibited a more seasonal strategy.

Pharmacotherapeutic Considerations on Telomere Biology: The Positive Effect of Pharmacologically Active Substances on Telomere Length.

Telomeres are part of chromatin structures containing repeated DNA sequences, which function as protective caps at the ends of chromosomes and prevent DNA degradation and recombination, thus ensuring the integrity of the genome. While telomere length (TL) can be genetically inherited, TL shortening has been associated with ageing and multiple xenobiotics and bioactive substances. TL has been characterised as a reliable biomarker for the predisposition to developing chronic pathologies and their progression. This narrative review aims to provide arguments in favour of including TL measurements in a complex prognostic and diagnostic panel of chronic pathologies and the importance of assessing the effect of different pharmacologically active molecules on the biology of telomeres. Medicines used in the management of cardiovascular diseases, diabetes, schizophrenia, hormone replacement therapy at menopause, danazol, melatonin, and probiotics have been studied for their positive protective effects against TL shortening. All these classes of drugs are analysed in the present review, with a particular focus on the molecular mechanisms involved.

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.

Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Bottom-up de novo design of functional proteins with complex structural features.

De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs. We applied the bottom-up strategy to successfully design five folds for four distinct binding motifs, including a bifunctionalized protein with two motifs. Crystal structures confirmed the atomic-level accuracy of the computational designs. These de novo proteins were functional as components of biosensors to monitor antibody responses and as orthogonal ligands to modulate synthetic signaling receptors in engineered mammalian cells. Our work demonstrates the potential of bottom-up approaches to accommodate complex structural motifs, which will be essential to endow de novo proteins with elaborate biochemical functions, such as molecular recognition or catalysis.

RosettaSurf-A surface-centric computational design approach.

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.