Expansive discovery of chemically diverse structured macrocyclic oligoamides

Small macrocycles with four or fewer amino acids are among the most potent natural products known, but there is currently no way to systematically generate such compounds. We describe a computational method for identifying ordered macrocycles composed of alpha, beta, gamma, and 17 other amino acid backbone chemistries, which we used to predict 14.9 million closed cycles composed of >42,000 monomer combinations. We chemically synthesized 18 macrocycles predicted to adopt single low-energy states and determined their x-ray or nuclear magnetic resonance structures; 15 of these were very close to the design models. We illustrate the therapeutic potential of these macrocycle designs by developing selective inhibitors of three protein targets of current interest. By opening up a vast space of readily synthesizable drug-like macrocycles, our results should considerably enhance structure-based drug design.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

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

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Design of amyloidogenic peptide traps

Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1-42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.

Blueprinting extendable nanomaterials with standardized protein blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies, in comparison, has been much more complex, largely owing to the irregular shapes of protein structures1. Here we describe extendable linear, curved and angled protein building blocks, as well as inter-block interactions, that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight 'train track' assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not previously been possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank three-dimensional canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to 'back of an envelope' architectural blueprints.

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

A general method for designing proteins to bind and sense any small molecule of interest would be widely useful. Due to the small number of atoms to interact with, binding to small molecules with high affinity requires highly shape complementary pockets, and transducing binding events into signals is challenging. Here we describe an integrated deep learning and energy based approach for designing high shape complementarity binders to small molecules that are poised for downstream sensing applications. We employ deep learning generated psuedocycles with repeating structural units surrounding central pockets; depending on the geometry of the structural unit and repeat number, these pockets span wide ranges of sizes and shapes. For a small molecule target of interest, we extensively sample high shape complementarity pseudocycles to generate large numbers of customized potential binding pockets; the ligand binding poses and the interacting interfaces are then optimized for high affinity binding. We computationally design binders to four diverse molecules, including for the first time polar flexible molecules such as methotrexate and thyroxine, which are expressed at high levels and have nanomolar affinities straight out of the computer. Co-crystal structures are nearly identical to the design models. Taking advantage of the modular repeating structure of pseudocycles and central location of the binding pockets, we constructed low noise nanopore sensors and chemically induced dimerization systems by splitting the binders into domains which assemble into the original pseudocycle pocket upon target molecule addition.

Sculpting conducting nanopore size and shape through de novo protein design

Transmembrane β-barrels (TMBs) are widely used for single molecule DNA and RNA sequencing and have considerable potential for a broad range of sensing and sequencing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels such as CsgG, which have evolved to carry out functions very different from sensing, and hence provide sub-optimal starting points. In contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to the design of transmembrane β-barrel pores with different diameter and pore geometry. NMR and crystallographic characterization shows that the designs are stably folded with structures close to the design models. We report the first examples of de novo designed TMBs with 10, 12 and 14 stranded β-barrels. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 pS (~0.5 nm pore diameter) to 430 pS (~1.1 nm pore diameter), and can be converted into sensitive small-molecule sensors with high signal to noise ratio. The capability to generate on demand β-barrel pores of defined geometry opens up fundamentally new opportunities for custom engineering of sequencing and sensing technologies.

Directing polymorph specific calcium carbonate formation with de novo protein templates

Biomolecules modulate inorganic crystallization to generate hierarchically structured biominerals, but the atomic structure of the organic-inorganic interfaces that regulate mineralization remain largely unknown. We hypothesized that heterogeneous nucleation of calcium carbonate could be achieved by a structured flat molecular template that pre-organizes calcium ions on its surface. To test this hypothesis, we design helical repeat proteins (DHRs) displaying regularly spaced carboxylate arrays on their surfaces and find that both protein monomers and protein-Ca2+ supramolecular assemblies directly nucleate nano-calcite with non-natural {110} or {202} faces while vaterite, which forms first in the absence of the proteins, is bypassed. These protein-stabilized nanocrystals then assemble by oriented attachment into calcite mesocrystals. We find further that nanocrystal size and polymorph can be tuned by varying the length and surface chemistry of the designed protein templates. Thus, bio-mineralization can be programmed using de novo protein design, providing a route to next-generation hybrid materials.

Precisely patterned nanofibres made from extendable protein multiplexes

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

Accurate computational design of three-dimensional protein crystals

Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain-side-chain interactions across protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein-protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.

De novo design of monomeric helical bundles for pH-controlled membrane lysis

Targeted intracellular delivery via receptor-mediated endocytosis requires the delivered cargo to escape the endosome to prevent lysosomal degradation. This can in principle be achieved by membrane lysis tightly restricted to endosomal membranes upon internalization to avoid general membrane insertion and lysis. Here, we describe the design of small monomeric proteins with buried histidine containing pH-responsive hydrogen bond networks and membrane permeating amphipathic helices. Of the 30 designs that were experimentally tested, all expressed in Escherichia coli, 13 were monomeric with the expected secondary structure, and 4 designs disrupted artificial liposomes in a pH-dependent manner. Mutational analysis showed that the buried histidine hydrogen bond networks mediate pH-responsiveness and control lysis of model membranes within a very narrow range of pH (6.0-5.5) with almost no lysis occurring at neutral pH. These tightly controlled lytic monomers could help mediate endosomal escape in designed targeted delivery platforms.

Hallucination of closed repeat proteins containing central pockets

In pseudocyclic proteins, such as TIM barrels, β barrels, and some helical transmembrane channels, a single subunit is repeated in a cyclic pattern, giving rise to a central cavity that can serve as a pocket for ligand binding or enzymatic activity. Inspired by these proteins, we devised a deep-learning-based approach to broadly exploring the space of closed repeat proteins starting from only a specification of the repeat number and length. Biophysical data for 38 structurally diverse pseudocyclic designs produced in Escherichia coli are consistent with the design models, and the three crystal structures we were able to obtain are very close to the designed structures. Docking studies suggest the diversity of folds and central pockets provide effective starting points for designing small-molecule binders and enzymes.

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

The RGD (Arg-Gly-Asp)-binding integrins αvβ6 and αvβ8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between homologous αvβ6 and αvβ8 and other RGD integrins, stabilize specific conformational states, and have high thermal stability could have considerable therapeutic utility. Existing small molecule and antibody inhibitors do not have all these properties, and hence new approaches are needed. Here we describe a generalized method for computationally designing RGD-containing miniproteins selective for a single RGD integrin heterodimer and conformational state. We design hyperstable, selective αvβ6 and αvβ8 inhibitors that bind with picomolar affinity. CryoEM structures of the designed inhibitor-integrin complexes are very close to the computational design models, and show that the inhibitors stabilize specific conformational states of the αvβ6 and the αvβ8 integrins. In a lung fibrosis mouse model, the αvβ6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

Design of stimulus-responsive two-state hinge proteins

In nature, proteins that switch between two conformations in response to environmental stimuli structurally transduce biochemical information in a manner analogous to how transistors control information flow in computing devices. Designing proteins with two distinct but fully structured conformations is a challenge for protein design as it requires sculpting an energy landscape with two distinct minima. Here we describe the design of "hinge" proteins that populate one designed state in the absence of ligand and a second designed state in the presence of ligand. X-ray crystallography, electron microscopy, double electron-electron resonance spectroscopy, and binding measurements demonstrate that despite the significant structural differences the two states are designed with atomic level accuracy and that the conformational and binding equilibria are closely coupled.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs), but these have poorly understood structure-activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structure-activity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein uses an unusual undertwisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. Our findings show that ice-recrystallization inhibition by the designed proteins increases with the degree of designed undertwisting, thus validating our hypothesis, and opening up avenues for the computational design of IBPs.

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

The RGD (Arg-Gly-Asp)-binding integrins αvβ6 and αvβ8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between the two closely related integrin proteins and other RGD integrins, stabilize specific conformational states, and have sufficient stability enabling tissue restricted administration could have considerable therapeutic utility. Existing small molecules and antibody inhibitors do not have all of these properties, and hence there is a need for new approaches. Here we describe a method for computationally designing hyperstable RGD-containing miniproteins that are highly selective for a single RGD integrin heterodimer and conformational state, and use this strategy to design inhibitors of αvβ6 and αvβ8 with high selectivity. The αvβ6 and αvβ8 inhibitors have picomolar affinities for their targets, and >1000-fold selectivity over other RGD integrins. CryoEM structures are within 0.6-0.7Å root-mean-square deviation (RMSD) to the computational design models; the designed αvβ6 inhibitor and native ligand stabilize the open conformation in contrast to the therapeutic anti-αvβ6 antibody BG00011 that stabilizes the bent-closed conformation and caused on-target toxicity in patients with lung fibrosis, and the αvβ8 inhibitor maintains the constitutively fixed extended-closed αvβ8 conformation. In a mouse model of bleomycin-induced lung fibrosis, the αvβ6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics when delivered via oropharyngeal administration mimicking inhalation, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

Blueprinting expandable nanomaterials with standardized protein building blocks

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures 1 . Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

De novo design of small beta barrel proteins

Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding. Here, we explore the de novo design of small beta barrel topologies using both Rosetta energy-based methods and deep learning approaches to design four small beta barrel folds: Src homology 3 (SH3) and oligonucleotide/oligosaccharide-binding (OB) topologies found in nature and five and six up-and-down-stranded barrels rarely if ever seen in nature. Both approaches yielded successful designs with high thermal stability and experimentally determined structures with less than 2.4 Å rmsd from the designed models. Using deep learning for backbone generation and Rosetta for sequence design yielded higher design success rates and increased structural diversity than Rosetta alone. The ability to design a large and structurally diverse set of small beta barrel proteins greatly increases the protein shape space available for designing binders to protein targets of interest.

Cyclic peptide structure prediction and design using AlphaFold

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 Å. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7-13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 Å), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

Design of Diverse Asymmetric Pockets in De Novo Homo-oligomeric Proteins

A challenge for design of protein-small-molecule recognition is that incorporation of cavities with size, shape, and composition suitable for specific recognition can considerably destabilize protein monomers. This challenge can be overcome through binding pockets formed at homo-oligomeric interfaces between folded monomers. Interfaces surrounding the central homo-oligomer symmetry axes necessarily have the same symmetry and so may not be well suited to binding asymmetric molecules. To enable general recognition of arbitrary asymmetric substrates and small molecules, we developed an approach to designing asymmetric interfaces at off-axis sites on homo-oligomers, analogous to those found in native homo-oligomeric proteins such as glutamine synthetase. We symmetrically dock curved helical repeat proteins such that they form pockets at the asymmetric interface of the oligomer with sizes ranging from several angstroms, appropriate for binding a single ion, to up to more than 20 Å across. Of the 133 proteins tested, 84 had soluble expression in E. coli, 47 had correct oligomeric states in solution, 35 had small-angle X-ray scattering (SAXS) data largely consistent with design models, and 8 had negative-stain electron microscopy (nsEM) 2D class averages showing the structures coming together as designed. Both an X-ray crystal structure and a cryogenic electron microscopy (cryoEM) structure are close to the computational design models. The nature of these proteins as homo-oligomers allows them to be readily built into higher-order structures such as nanocages, and the asymmetric pockets of these structures open rich possibilities for small-molecule binder design free from the constraints associated with monomer destabilization.

Exploration of Structured Symmetric Cyclic Peptides as Ligands for Metal-Organic Frameworks

Despite remarkable advances in the assembly of highly structured coordination polymers and metal-organic frameworks, the rational design of such materials using more conformationally flexible organic ligands such as peptides remains challenging. In an effort to make the design of such materials fully programmable, we first developed a computational design method for generating metal-mediated 3D frameworks using rigid and symmetric peptide macrocycles with metal-coordinating sidechains. We solved the structures of six crystalline networks involving conformationally constrained 6 to 12 residue cyclic peptides with C2, C3, and S2 internal symmetry and three different types of metals (Zn2+, Co2+, or Cu2+) by single-crystal X-ray diffraction, which reveals how the peptide sequences, backbone symmetries, and metal coordination preferences drive the assembly of the resulting structures. In contrast to smaller ligands, these peptides associate through peptide-peptide interactions without full coordination of the metals, contrary to one of the assumptions underlying our computational design method. The cyclic peptides are the largest peptidic ligands reported to form crystalline coordination polymers with transition metals to date, and while more work is required to develop methods for fully programming their crystal structures, the combination of high chemical diversity with synthetic accessibility makes them attractive building blocks for engineering a broader set of new crystalline materials for use in applications such as sensing, asymmetric catalysis, and chiral separation.

Accurate de novo design of membrane-traversing macrocycles

We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1 × 10-6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.

High cubicity of D2O ice inside spherical nanopores of MIL-101(Cr) framework: a neutron diffraction study

Although cubic ice (ice I_c) is considered to be an important phase of water that impacts ice cloud formation in the Earth's upper atmosphere, its properties have not been studied to the same extent as those of hexagonal ice (ice I_h). This is because pristine ice I_c is not formed in simple laboratory conditions. Ice I_c formed in ambient conditions has a stacking disordered array of both hexagonal and cubic-structured hydrogen-bonded water molecules. It is therefore an active area of research to find ways of developing stacking disorder-free pure ice I_c. We demonstrate the evolution of almost pure ice I_c structure within the spherical nanopores of a hydrostable Cr-based metal-organic framework MIL-101(Cr) with an average pore size of 1 nm by low-temperature neutron diffraction study on D₂O. It is observed that at temperatures below 230 K a fraction of liquid D₂O transforms into ice and more than 94% of ice crystals evolved inside the pore are cubic in shape. This is a significantly high fraction of ice I_c formed under simple conditions inside the spherical pores of a Cr-based MOF. It is also observed that upon increasing the temperature, i_ce I_c remains stable until its melting point, without being transformed into ice I_h. This observation is in contrast to our previous observation of ice structure in the 2D cylindrical nanopores of MCM-41, where H₂O ice after creeping out from the cylindrical channel was seen to be dominated by hexagonal shape. In the present study, the D₂O molecules were confined into well-defined spherical nanopores, which hindered the growth of crystals above a certain size, thus minimizing the stacking disordered array. Nanoconfinement of water inside uniform spherical pores is therefore a promising method for the evolution of a significantly large fraction of cubic ice by minimizing the stacking disorder. This finding may open up the possibility of forming ice I_c with 100% cubicity under simple laboratory conditions, which will help in exploring the microphysics of ice cloud formation in the upper atmosphere.

Design of protein-binding proteins from the target structure alone

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1-5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model—despite weaknesses including a noisy data set—can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Reconfigurable asymmetric protein assemblies through implicit negative design

Asymmetric multiprotein complexes that undergo subunit exchange play central roles in biology but present a challenge for design because the components must not only contain interfaces that enable reversible association but also be stable and well behaved in isolation. We use implicit negative design to generate β sheet-mediated heterodimers that can be assembled into a wide variety of complexes. The designs are stable, folded, and soluble in isolation and rapidly assemble upon mixing, and crystal structures are close to the computational models. We construct linearly arranged hetero-oligomers with up to six different components, branched hetero-oligomers, closed C4-symmetric two-component rings, and hetero-oligomers assembled on a cyclic homo-oligomeric central hub and demonstrate that such complexes can readily reconfigure through subunit exchange. Our approach provides a general route to designing asymmetric reconfigurable protein systems.

The nonreceptor tyrosine kinase SRMS inhibits autophagy and promotes tumor growth by phosphorylating the scaffolding protein FKBP51

Nutrient-responsive protein kinases control the balance between anabolic growth and catabolic processes such as autophagy. Aberrant regulation of these kinases is a major cause of human disease. We report here that the vertebrate nonreceptor tyrosine kinase Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristylation sites (SRMS) inhibits autophagy and promotes growth in a nutrient-responsive manner. Under nutrient-replete conditions, SRMS phosphorylates the PHLPP scaffold FK506-binding protein 51 (FKBP51), disrupts the FKBP51-PHLPP complex, and promotes FKBP51 degradation through the ubiquitin-proteasome pathway. This prevents PHLPP-mediated dephosphorylation of AKT, causing sustained AKT activation that promotes growth and inhibits autophagy. SRMS is amplified and overexpressed in human cancers where it drives unrestrained AKT signaling in a kinase-dependent manner. SRMS kinase inhibition activates autophagy, inhibits cancer growth, and can be accomplished using the FDA-approved tyrosine kinase inhibitor ibrutinib. This illuminates SRMS as a targetable vulnerability in human cancers and as a new target for pharmacological induction of autophagy in vertebrates.

This study describes the discovery and characterization of a nutrient-sensitive signaling pathway that drives growth and inhibits autophagy in mammalian cells. This pathway, which involves the non-receptor tyrosine kinase SRMS and the PHLPP scaffold protein FKBP51, promotes tumor growth and is amenable to pharmacological inhibition.

Design of multi-scale protein complexes by hierarchical building block fusion

A systematic and robust approach to generating complex protein nanomaterials would have broad utility. We develop a hierarchical approach to designing multi-component protein assemblies from two classes of modular building blocks: designed helical repeat proteins (DHRs) and helical bundle oligomers (HBs). We first rigidly fuse DHRs to HBs to generate a large library of oligomeric building blocks. We then generate assemblies with cyclic, dihedral, and point group symmetries from these building blocks using architecture guided rigid helical fusion with new software named WORMS. X-ray crystallography and cryo-electron microscopy characterization show that the hierarchical design approach can accurately generate a wide range of assemblies, including a 43 nm diameter icosahedral nanocage. The computational methods and building block sets described here provide a very general route to de novo designed protein nanomaterials.

De novo design of transmembrane β barrels

Transmembrane β-barrel proteins (TMBs) are of great interest for single-molecule analytical technologies because they can spontaneously fold and insert into membranes and form stable pores, but the range of pore properties that can be achieved by repurposing natural TMBs is limited. We leverage the power of de novo computational design coupled with a "hypothesis, design, and test" approach to determine TMB design principles, notably, the importance of negative design to slow β-sheet assembly. We design new eight-stranded TMBs, with no homology to known TMBs, that insert and fold reversibly into synthetic lipid membranes and have nuclear magnetic resonance and x-ray crystal structures very similar to the computational models. These advances should enable the custom design of pores for a wide range of applications.

An enumerative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

Evolution of confined ice nano structures at different levels of pore filling: a synchrotron based X-ray diffraction study

We have thoroughly investigated the crystal structure of ice evolved from super cooled water confined in MCM-41 cylindrical nano pores through a synchrotron-based X-ray diffraction (XRD) technique for two different levels of pore filling. A rigorous analysis of XRD data shows that the nucleation dynamics and the structure of nucleated ice highly depend on the level of pore filling. In the nearly fully hydrated pores, ice crystallites start nucleating inside the pores below 240 K and creep out of the pores to form bulk crystals having crystalline structure of a mixed phase of hexagonal and cubic forms. In the partially hydrated pores, on the other hand, ice crystals cannot creep out of the pore crossing the energy barrier. The crystalline ice particles remaining inside the cylindrical pore show a short range "cubic rich" structure. The "pure cubic" phase has not been identified at either of the pore fillings in these 2.5 nm average size pores. A large fraction of water inside the pores remains in the super cooled liquid phase even at 180 K. This observation is relevant for understanding the ice nucleation through the pore condensation and freezing mechanism, which is a major pathway for the formation of cirrus clouds in the upper atmosphere.

KRASQ61H Preferentially Signals through MAPK in a RAF Dimer-Dependent Manner in Non-Small Cell Lung Cancer

Assembly of RAS molecules into complexes at the cell membrane is critical for RAS signaling. We previously showed that oncogenic KRAS codon 61 mutations increase its affinity for RAF, raising the possibility that KRAS^Q61H, the most common KRAS mutation at codon 61, upregulates RAS signaling through mechanisms at the level of RAS assemblies. We show here that KRAS^Q61H exhibits preferential binding to RAF relative to PI3K in cells, leading to enhanced MAPK signaling in in vitro models and human NSCLC tumors. X-ray crystallography of KRAS^Q61H:GTP revealed that a hyperdynamic switch 2 allows for a more stable interaction with switch 1, suggesting that enhanced RAF activity arises from a combination of absent intrinsic GTP hydrolysis activity and increased affinity for RAF. Disruption of KRAS^Q61H assemblies by the RAS oligomer-disrupting D154Q mutation impaired RAF dimerization and altered MAPK signaling but had little effect on PI3K signaling. However, KRAS^Q61H oligomers but not KRAS^G12D oligomers were disrupted by RAF mutations that disrupt RAF-RAF interactions. KRAS^Q61H cells show enhanced sensitivity to RAF and MEK inhibitors individually, whereas combined treatment elicited synergistic growth inhibition. Furthermore, KRAS^Q61H tumors in mice exhibited high vulnerability to MEK inhibitor, consistent with cooperativity between KRAS^Q61H and RAF oligomerization and dependence on MAPK signaling. These findings support the notion that KRAS^Q61H and functionally similar mutations may serve as predictive biomarkers for targeted therapies against the MAPK pathway. SIGNIFICANCE: These findings show that oncogenic KRAS^Q61H forms a cooperative RAS-RAF ternary complex, which renders RAS-driven tumors vulnerable to MEKi and RAFi, thus establishing a framework for evaluating RAS biomarker-driven targeted therapies.

GTP hydrolysis is modulated by Arg34 in the RASopathy-associated KRASP34R

RAS proteins are commonly mutated in cancerous tumors, but germline RAS mutations are also found in RASopathy syndromes such as Noonan syndrome (NS) and cardiofaciocutaneous (CFC) syndrome. Activating RAS mutations can be subclassified based on their activating mechanisms. Understanding the structural basis for these mechanisms may provide clues for how to manage associated health conditions. We determined high-resolution X-ray structures of the RASopathy mutant KRASP34R seen in NS and CFCS. GTP and GDP-bound KRASP34R crystallized in multiple forms, with each lattice consisting of multiple protein conformations. In all GTP-bound conformations, the switch regions are not compatible with GAP binding, suggesting a structural mechanism for the GAP insensitivity of this RAS mutant. However, GTP-bound conformations are compatible with intrinsic nucleotide hydrolysis, including one that places R34 in a position analogous to the GAP arginine finger or intrinsic arginine finger found in heterotrimeric G proteins, which may support intrinsic GTP hydrolysis. We also note that the affinity between KRASP34R and RAF-RBD is decreased, suggesting another possible mechanism for dampening of RAS signaling. These results may provide a foothold for development of new mutation-specific strategies to address KRASP34R -driven diseases.

Structure and Characterization of a Covalent Inhibitor of Src Kinase

Unregulated Src activity promotes malignant processes in cancer, but no Src-directed targeted therapies are used clinically, possibly because early Src inhibitors produce off-target effects leading to toxicity. Improved selective Src inhibitors may enable Src-directed therapies. Previously, we reported an irreversible Src inhibitor, DGY-06-116, based on the hybridization of dasatinib and a promiscuous covalent kinase probe SM1-71. Here, we report biochemical and biophysical characterization of this compound. An x-ray co-crystal structure of DGY-06-116: Src shows a covalent interaction with the kinase p-loop and occupancy of the back hydrophobic kinase pocket, explaining its high potency, and selectivity. However, a reversible analog also shows similar potency. Kinetic analysis shows a slow inactivation rate compared to other clinically approved covalent kinase inhibitors, consistent with a need for p-loop movement prior to covalent bond formation. Overall, these results suggest that a strong reversible interaction is required to allow sufficient time for the covalent reaction to occur. Further optimization of the covalent linker may improve the kinetics of covalent bond formation.

Correlated paramagnetism and interplay of magnetic and phononic degrees of freedom in 3d-5d coupled La2CuIrO6

Conventional paramagnetism-a state with finite magnetic moment per ion sans long range magnetic ordering, but with lowering temperature the moment each ion picks up a particular direction, breaking spin rotational symmetry, and results into long-range magnetic ordering. However, in systems with competing multiple degrees of freedom this conventional notion may easily break and results into short range correlation much above the global magnetic transition temperature. La₂CuIrO₆ with complex interplay of spins (s  =  1/2) on Cu site and pseudo-spin (j   =  1/2) on Ir site owing to strong spin-orbit coupling provides fertile ground to observe such correlated phenomena. By a comprehensive temperature dependent Raman study, we have shown the presence of such a correlated paramagnetic state in La₂CuIrO₆ much above the long-range magnetic ordering temperature (T _N ). Our observation of strong interactions of phonons, associated with Cu/Ir octahedra, with underlying magnetic degrees of freedom mirrored in the observed Fano asymmetry, which remarkably persists as high as ~3.5T _N clearly signals the existence of correlated paramagnetism hence broken spin rotational symmetry. Our detailed analysis also reveals anomalous changes in the self-energy parameters of the phonon modes, i.e. mode frequencies and linewidth, below T _N , providing a useful gauge for monitoring the strong coupling between phonons and magnetic degrees of freedom.

Structural basis of the atypical activation mechanism of KRASV14I

RAS regulation and signaling are largely accomplished by direct protein-protein interactions, making RAS protein dynamics a critical determinant of RAS function. Here, we report a crystal structure of GDP-bound KRASV14I, a mutated KRAS variant associated with the developmental RASopathy disorder Noonan syndrome (NS), at 1.5-1.6 Å resolution. The structure is notable for revealing a marked extension of switch 1 away from the G-domain and nucleotide-binding site of the KRAS protein. We found that this extension is associated with a loss of the magnesium ion and a tilt in the position of the guanine base because of the additional carbon introduced by the isoleucine substitution. Hydrogen-deuterium exchange MS analysis confirmed that this conformation occurs in solution, but also disclosed a difference in kinetics when compared with KRASA146T, another RAS mutant that displays a nearly identical conformation in previously reported crystal structures. This conformational change contributed to a high rate of guanine nucleotide-exchange factor (GEF)-dependent and -independent nucleotide exchange and to an increase in affinity for SOS Ras/Rac GEF 1 (SOS1), which appears to be the major mode of activation for this RAS variant. These results highlight a mechanistic connection between KRASA146T and KRASV14I that may have implications for the regulation of these variants and for the development of therapeutic strategies to manage KRAS variant-associated disorders.

Crystal structures of Moorella thermoacetica cyanuric acid hydrolase reveal conformational flexibility and asymmetry important for catalysis

An ancient enzyme family responsible for the catabolism of the prebiotic chemical cyanuric acid (1,3,5-triazine-2,4,6-triol) was recently discovered and is undergoing proliferation in the modern world due to industrial synthesis and dissemination of 1,3,5-triazine compounds. Cyanuric acid has a highly stabilized ring system such that bacteria require a unique enzyme with a novel fold and subtle active site construction to open the ring. Each cyanuric acid hydrolase monomer consists of three isostructural domains that coordinate and activate the three-fold symmetric substrate cyanuric acid for ring opening. We have now solved a series of X-ray structures of an engineered, thermostable cyanuric acid ring-opening enzyme at 1.51 ~ 2.25 Å resolution, including various complexes with the substrate, a tight-binding inhibitor, or an analog of the reaction intermediate. These structures reveal asymmetric interactions between the enzyme and bound ligands, a metal ion binding coupled to conformational changes and substrate binding important for enzyme stability, and distinct roles of the isostructural domains of the enzyme. The multiple conformations of the enzyme observed across a series of structures and corroborating biochemical data suggest importance of the structural dynamics in facilitating the substrate entry and the ring-opening reaction, catalyzed by a conserved Ser-Lys dyad.

Tissue-Specific Oncogenic Activity of KRASA146T

KRAS is the most frequently mutated oncogene. The incidence of specific KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specific mutant KRAS proteins. We used a cross-disciplinary approach to compare KRASG12D, a common mutant form, and KRASA146T, a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRASA146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factor-induced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRASG12D and KRASA146T exhibit distinct tissue-specific effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specific phenotypes result from allele-specific signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specific behaviors for KRAS, experimental evidence for allele-specific biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specific KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.See related commentary by Hobbs and Der, p. 696.This article is highlighted in the In This Issue feature, p. 681.

Variability of the EG95 antigen-coding gene of Echinococcus granulosus in animal and human origin: implications for vaccine development

In the present study, the genetic variability of the EG95 protein-coding gene in several animal and human isolates of Echinococcus granulosus was investigated. A total of 24 isolates collected from cattle, buffalo, sheep, goat, dog and man were amplified by Eg95-coding gene-specific primers. From the generated sequence information, a conceptual amino acid sequence was deduced. Phylogenetically, the Eg95 coding gene belongs to the Eg95-1/Eg95-2/Eg95-3/Eg95-4 cluster. Further confirmation on the maximum composite likelihood analysis revealed that the overall transition/transversion bias was 2.913. This finding indicated thatthere is bias towards transitional and transversional substitution. Using artificial neural networks, a B-cell epitope was predicted on primary sequence information. Stretches of amino acid residues varied between animal and human isolates when hydrophobicity was considered. Flexibility also varied between larval and adult stages of the organism. This observation is important to develop vaccines. However, cytotoxic T-lymphocyte epitopes on primary sequence data remained constant in all isolates. In this study, agretope identification started with hydrophobic amino acids. Amino acids with the same physico-chemical properties were present in the middle. The conformational propensity of the Eg95-coding gene of 156 amino acid residues had α-turns and β-turns, and α-amphipathic regions up to 129, 138-156 and 151-155 residues, respectively. The results indicated potential T-cell antigenic sites. The overall Tajima's D value was negative (-2.404165), indicative of negative selection pressure.

KRAS Switch Mutants D33E and A59G Crystallize in the State 1 Conformation

KRAS switch loop movements play a crucial role in regulating RAS signaling, and alteration of these sensitive dynamics is a principal mechanism through which disease-associated RAS mutations lead to aberrant RAS activation. Prior studies suggest that despite a high degree of sequence similarity, the switches in KRAS are more dynamic than those in HRAS. We determined X-ray crystal structures of the rare tumorigenic KRAS mutants KRAS^D33E, in switch 1 (SW1), and KRAS^A59G, in switch 2 (SW2), bound to GDP and found these adopt nearly identical, open SW1 conformations as well as altered SW2 conformations. KRAS^A59G bound to a GTP analogue crystallizes in the same conformation. This open conformation is consistent with the inactive "state 1" previously observed for HRAS bound to GTP. For KRAS^A59G, switch rearrangements may be regulated by increased flexibility in the ⁵⁷DXXGQ⁶¹ motif at codon 59. However, loss of interactions between side chains at codons 33 and 35 in the SW1 ³³DPT³⁵ motif drives changes for KRAS^D33E. The ³³DPT³⁵ motif is conserved for multiple members of the RAS subfamily but is not found in RAB, RHO, ARF, or Gα families, suggesting that dynamics mediated by this motif may be important for determining the selectivity of RAS-effector interactions. Biochemically, the consequence of altered switch dynamics is the same, showing impaired interaction with the guanine exchange factor SOS and loss of GAP-dependent GTPase activity. However, interactions with the RBD of RAF are preserved. Overall, these observations add to a body of evidence suggesting that HRAS and KRAS show meaningful differences in functionality stemming from differential protein dynamics independent of the hypervariable region.

Takinib, a Selective TAK1 Inhibitor, Broadens the Therapeutic Efficacy of TNF-α Inhibition for Cancer and Autoimmune Disease

Tumor necrosis factor alpha (TNF-α) has both positive and negative roles in human disease. In certain cancers, TNF-α is infused locally to promote tumor regression, but dose-limiting inflammatory effects limit broader utility. In autoimmune disease, anti-TNF-α antibodies control inflammation in most patients, but these benefits are offset during chronic treatment. TAK1 acts as a key mediator between survival and cell death in TNF-α-mediated signaling. Here, we describe Takinib, a potent and selective TAK1 inhibitor that induces apoptosis following TNF-α stimulation in cell models of rheumatoid arthritis and metastatic breast cancer. We demonstrate that Takinib is an inhibitor of autophosphorylated and non-phosphorylated TAK1 that binds within the ATP-binding pocket and inhibits by slowing down the rate-limiting step of TAK1 activation. Overall, Takinib is an attractive starting point for the development of inhibitors that sensitize cells to TNF-α-induced cell death, with general implications for cancer and autoimmune disease treatment.

Structure of the Cyanuric Acid Hydrolase TrzD Reveals Product Exit Channel

Cyanuric acid hydrolases are of industrial importance because of their use in aquatic recreational facilities to remove cyanuric acid, a stabilizer for the chlorine. Degradation of excess cyanuric acid is necessary to maintain chlorine disinfection in the waters. Cyanuric acid hydrolase opens the cyanuric acid ring hydrolytically and subsequent decarboxylation produces carbon dioxide and biuret. In the present study, we report the X-ray structure of TrzD, a cyanuric acid hydrolase from Acidovorax citrulli. The crystal structure at 2.19 Å resolution shows a large displacement of the catalytic lysine (Lys163) in domain 2 away from the active site core, whereas the two other active site lysines from the two other domains are not able to move. The lysine displacement is proposed here to open up a channel for product release. Consistent with that, the structure also showed two molecules of the co-product, carbon dioxide, one in the active site and another trapped in the proposed exit channel. Previous data indicated that the domain 2 lysine residue plays a role in activating an adjacent serine residue carrying out nucleophilic attack, opening the cyanuric acid ring, and the mobile lysine guides products through the exit channel.

In Silico Identification of Bioremediation Potential: Carbamazepine and Other Recalcitrant Personal Care Products

Emerging contaminants are principally personal care products not readily removed by conventional wastewater treatment and, with an increasing reliance on water recycling, become disseminated in drinking water supplies. Carbamazepine, a widely used neuroactive pharmaceutical, increasingly escapes wastewater treatment and is found in potable water. In this study, a mechanism is proposed by which carbamazepine resists biodegradation, and a previously unknown microbial biodegradation was predicted computationally. The prediction identified biphenyl dioxygenase from Paraburkholderia xenovorans LB400 as the best candidate enzyme for metabolizing carbamazepine. The rate of degradation described here is 40 times greater than the best reported rates. The metabolites cis-10,11-dihydroxy-10,11-dihydrocarbamazepine and cis-2,3-dihydroxy-2,3-dihydrocarbamazepine were demonstrated with the native organism and a recombinant host. The metabolites are considered nonharmful and mitigate the generation of carcinogenic acridine products known to form when advanced oxidation methods are used in water treatment. Other recalcitrant personal care products were subjected to prediction by the Pathway Prediction System and tested experimentally with P. xenovorans LB400. It was shown to biodegrade structurally diverse compounds. Predictions indicated hydrolase or oxygenase enzymes catalyzed the initial reactions. This study highlights the potential for using the growing body of enzyme-structural and genomic information with computational methods to rapidly identify enzymes and microorganisms that biodegrade emerging contaminants.

Insights from Analysis of Binding Sites of Human Meprins: Screening of Inhibitors by Molecular Dynamics Simulation Study

Human meprin-α and-β are important regulators of angiogenesis, cancer, inflammation, fibrosis, and neurodegenerative diseases and hence important therapeutic targets. Meprins are the only astacin proteases that are expressed in membrane-bound and secreted form. The cleavage specificity of human meprins is similar in certain cases but differs markedly in others. The inhibitor selectivity of human meprins is controlled by the specific residues involved in binding at the active-site cleft of the proteases. Meprins are inhibited by various small molecular inhibitors as well as macromolecular endogenous inhibitors, making them good drug targets. In the current study, molecular dynamics simulation was performed for 10 ns on ten systems consisting of two apoenzymes of meprin -α/β and eight complexes of human meprin-α and -β complexed to four inhibitors with different metal binding moieties and comparable Ki values. These simulation studies helped to elucidate the molecular details of how several parameters influence protein-inhibitor binding affinity. Analysis of the interaction energies of the protein-inhibitor complexes revealed the diverse binding nature of this series of inhibitors. Several structural segments of human meprins exhibited certain conformational changes during the simulation time course. Among the inhibitors studied captopril had a different disposition in the meprin-bound complexes compared to the other three inhibitors, namely Pro- Leu-Gly-hydroxamate, galardin and EDTA. Comparison of the interaction energies for each system helped us to conclude that the hydroxamic acid-based inhibitors are the most potent inhibitors of meprins.

Angiopoietin-2 mediates thrombin-induced monocyte adhesion and endothelial permeability

Essentials Mechanism of thrombin-induced inflammation is not fully understood. Thrombin induced monocyte adhesion and barrier loss require Angiopoietin-2 (Ang-2). Ang-2 mediates vessel leakage and monocyte adhesion through SHP-2/p38MAPK pathway. Calcium dependent SHP2/p38MAPK activation regulates Ang-2 expression through a feedback loop.

SUMMARY

Background Thrombin imparts an inflammatory phenotype to the endothelium by promoting increased monocyte adhesion and vascular permeability. However, the molecular players that govern these events are incompletely understood. Objective The aim of this study was to determine whether Angiopoietin-2 (Ang-2) has a role, if any, in regulating inflammatory signals initiated by thrombin. Methods Assessment of vascular leakage by Miles assay was performed by intra-dermal injection on the foot paw. Surface levels of intercellular adhesion molecule-1 (ICAM-1) were determined by flow cytometry. Overexpression, knockdown and phosphorylation of proteins were determined by Western blotting. Results In time-course experiments, thrombin-stimulated Ang-2 up-regulation, peaked prior to the expression of adhesion molecule ICAM-1 in human umbilical vein-derived endothelial cells (HUVECs). Knockdown of Ang-2 blocked both thrombin-induced monocyte adhesion and ICAM-1 expression. In addition, Ang-2(-/-) mice displayed defective vascular leakage when treated with thrombin. Introducing Ang-2 protein in Ang-2(-/-) mice failed to recover a wild-type phenotype. Mechanistically, Ang-2 appears to regulate the thrombin-activated calcium spike that is required for tyrosine phosphatase SHP2 and p38 MAPK activation. Further, down-regulation of SHP2 attenuated both thrombin-induced Ang-2 expression and monocyte adhesion. Down-regulation of the adaptor protein Gab1, a co-activator of SHP2, as well as overexpression of the Gab1 mutant incapable of interacting with SHP2 (YFGab1), inhibited thrombin-mediated effects, including downstream activation of p38 MAPK, which in turn was required for Ang-2 expression. Conclusions The data establish an essential role of the Gab1/SHP2/p38MAPK signaling pathway and Ang-2 in regulating thrombin-induced monocyte adhesion and vascular leakage.

Gentiana lutea exerts anti-atherosclerotic effects by preventing endothelial inflammation and smooth muscle cell migration

BACKGROUND AND AIMS

Studies suggest that Gentiana lutea (GL), and its component isovitexin, may exhibit anti-atherosclerotic properties. In this study we sought to investigate the protective mechanism of GL aqueous root extract and isovitexin on endothelial inflammation, smooth muscle cell migation, and on the onset and progression of atherosclerosis in streptozotocin (STZ)-induced diabetic rats.

METHODS AND RESULTS

Our results show that both GL extract and isovitexin, block leukocyte adhesion and generation of reactive oxygen species in human umbilical vein endothelial cells (HUVECs) and rat aortic smooth muscle cells (RASMCs), following TNF-alpha and platelet derived growth factor-BB (PDGF-BB) challenges respectively. Both the extract and isovitexin blocked TNF-α induced expression of ICAM-1 and VCAM-1 in HUVECs. PDGF-BB induced migration of RASMCs and phospholipase C-γ activation, were also abrogated by GL extract and isovitexin. Fura-2 based ratiometric measurements demonstrated that, both the extact, and isovitexin, inhibit PDGF-BB mediated intracellular calcium rise in RASMCs. Supplementation of regular diet with 2% GL root powder for STZ rats, reduced total cholesterol in blood. Oil Red O staining demonstrated decreased lipid accumulation in aortic wall of diabetic animals upon treatment with GL. Medial thickness and deposition of collagen in the aortic segment of diabetic rats were also reduced upon supplementation. Immunohistochemistry demonstrated reduced expression of vascular cell adhesion molecule-1 (VCAM-1), inducible nitric oxide synthase (iNOS), and vascular endothelial cadherin (VE-cadherin) in aortic segments of diabetic rats following GL treatment.

CONCLUSIONS

Thus, our results support that GL root extract/powder and isovitexin exhibit anti-atherosclerotic activities.