Binding and sensing diverse small molecules using shape-complementary pseudocycles.

We describe an approach for designing high-affinity small molecule-binding proteins poised for downstream sensing. We use deep learning-generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.

Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site.

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long-lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought-induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model-data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post-drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust-boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought-free conditions. Overall, allocation shifts accounted for 65% [45%-75%] of drought legacy effects in modeled NBE. In summary, drought-induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land-atmosphere CO2 exchanges and should be accounted for in ecosystem models.

Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase.

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

Computational design of non-porous pH-responsive antibody nanoparticles.

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

De novo design of buttressed loops for sculpting protein functions.

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9-14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Inflammatory breast cancer: Epidemiologic data and therapeutic results.

Since the early description more than a century ago, inflammatory breast cancer (IBC) remains an aggressive disease, with a different geographic repartition, with the highest ones incidence reported in the North of Africa (Tunisia, Algeria, Morocco, and Egypt), and the lowest incidence in Western countries (USA, Europe…). In this study, we reviewed the literature using the Surveillance, Epidemiology, and End Results (SEER) database compared to other published series. We observed that in the high incidence areas (North of Africa) when compared to "classical" breast cancer, IBC was associated to younger age (less than 50 years) with rapid evolution of signs and symptoms (in less than 3 up to 6 months), and more aggressive clinical and histopathological-molecular parameters, due to the predominance of triple-negative and HER2+ subtypes in around 60% of cases. An epidemiologic trend was observed in both high and low incidence areas since the eighties are towards reduction of IBC prevalence. Concerning Tunisia, in comparison with the historical series of the 1980s, the incidence decreased in part by applying more stringent diagnostic criteria but also probably due to a slight improvement of the socio-economic level (SEL). This trend was also observed in the US, due to the efforts of collaborative IBC groups from MD Anderson Cancer Center (MDACC), Duke and IBC patient advocacy groups. Therapeutic results are slightly better due to the standardization of a multidisciplinary approach and the use of combined primary chemotherapy and/or targeted therapies (especially in HER2 positive patients), followed by mastectomy plus radiotherapy. The 5-year overall and disease-free survival is at more than 60%, related to an IBC mortality decrease observed in the cohorts of patients treated in the last decade.

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.

Design of amyloidogenic peptide traps.

Segments of proteins with high ß-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in ß-strand and ß-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 ß1-42 (Aß42). The Aß binders block the assembly of Aß fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aß42 species.

Backbone Modification Provides a Long-Acting Inverse Agonist of Pathogenic, Constitutively Active PTH1R Variants.

Parathyroid hormone 1 receptor (PTH1R) plays a key role in mediating calcium homeostasis and bone development, and aberrant PTH1R activity underlies several human diseases. Peptidic PTH1R antagonists and inverse agonists have therapeutic potential in treating these diseases, but their poor pharmacokinetics and pharmacodynamics undermine their in vivo efficacy. Herein, we report the use of a backbone-modification strategy to design a peptidic PTH1R inhibitor that displays prolonged activity as an antagonist of wild-type PTH1R and an inverse agonist of the constitutively active PTH1R-H223R mutant both in vitro and in vivo. This peptide may be of interest for the future development of therapeutic agents that ameliorate PTH1R malfunction.

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.

De novo design of buttressed loops for sculpting protein functions.

In natural proteins, structured loops play central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that generates structured loops buttressed by extensive hydrogen bonding interactions with two neighboring loops and with secondary structure elements. We use this approach to design tandem repeat proteins with buttressed loops ranging from 9 to 14 residues in length. Experimental characterization shows the designs are folded and monodisperse, highly soluble, and thermally stable. Crystal structures are in close agreement with the computational design models, with the loops structured and buttressed by their neighbors as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute quite generally to efforts to design new protein functions.

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.

Computational design of non-porous, pH-responsive antibody nanoparticles.

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and important for targeted delivery of biologics. We describe the design of octahedral non-porous nanoparticles with the three symmetry axes (four-fold, three-fold, and two-fold) occupied by three distinct protein homooligomers: a de novo designed tetramer, an antibody of interest, and a designed trimer programmed to disassemble below a tunable pH transition point. The nanoparticles assemble cooperatively from independently purified components, and a cryo-EM density map reveals that the structure is very close to the computational design model. The designed nanoparticles can package a variety of molecular payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between to 5.9-6.7. To our knowledge, these are the first designed nanoparticles with more than two structural components and with finely tunable environmental sensitivity, and they provide new routes to antibody-directed targeted delivery.

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.

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.

Cyclic Peptide Screening Methods for Preclinical Drug Discovery.

Cyclic peptides are among the most diverse architectures for current drug discovery efforts. Their size, stability, and ease of synthesis provide attractive scaffolds to engage and modulate some of the most challenging targets, including protein-protein interactions and those considered to be "undruggable". With a variety of sophisticated screening technologies to produce libraries of cyclic peptides, including phage display, mRNA display, split intein circular ligation of peptides, and in silico screening, a new era of cyclic peptide drug discovery is at the forefront of modern medicine. In this perspective, we begin by discussing cyclic peptides approved for clinical use in the past two decades. Particular focus is placed around synthetic chemistries to generate de novo libraries of cyclic peptides and novel methods to screen them. The perspective culminates with future prospects for generating cyclic peptides as viable therapeutic options and discusses the advantages and disadvantages currently being faced with bringing them to market.

Generation of Potent and Stable GLP-1 Analogues Via "Serine Ligation".

Peptide and protein bioconjugation technologies have revolutionized our ability to site-specifically or chemoselectively install a variety of functional groups for applications in chemical biology and medicine, including the enhancement of bioavailability. Here, we introduce a site-specific bioconjugation strategy inspired by chemical ligation at serine that relies on a noncanonical amino acid containing a 1-amino-2-hydroxy functional group and a salicylaldehyde ester. More specifically, we harness this technology to generate analogues of glucagon-like peptide-1 that resemble Semaglutide, a long-lasting blockbuster drug currently used in the clinic to regulate glucose levels in the blood. We identify peptides that are more potent than unmodified peptide and equipotent to Semaglutide in a cell-based activation assay, improve the stability in human serum, and increase glucose disposal efficiency in vivo. This approach demonstrates the potential of "serine ligation" for various applications in chemical biology, with a particular focus on generating stabilized peptide therapeutics.

Recent advances in the epidemiology of inflammatory breast cancer.

The survival after the diagnosis of inflammatory breast cancer (IBC) has been steadily improving for the past few decades. This has been due to advances in the knowledge of IBC in a number of fields, including epidemiology, molecular biology, and medical management. In this review we summarize some of the most important recent advances in these fields and suggest possible opportunities for continued improvement.

Zonally contrasting shifts of the tropical rainbelt in response to climate change.

Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.