Modular Strategy Research Articles

A Photochemical Strategy for the Conversion of Nitroarenes into Rigidified Pyrrolidine Analogues.

Bicyclic amines are important motifs for the preparation of bioactive materials. These species have well-defined exit vectors that enable accurate disposition of substituents toward specific areas of chemical space. Of all possible skeletons, the 2-azabicyclo[3.2.0]heptane framework is virtually absent from MedChem libraries due to a paucity of synthetic methods for its preparation. Here, we report a modular synthetic strategy that utilizes nitroarenes as flat and easy-to-functionalize feedstocks for the assembly of these sp3-rich materials. Mechanistically, this approach exploits two concomitant photochemical processes that sequentially ring-expand the nitroarene into an azepine and then fold it into a rigid bicycle pyrroline by means of singlet nitrene-mediated nitrogen insertion and excited-state-4π electrocyclization. A following hydrogenolysis provides, with full diastereocontrol, the desired bicyclic amine derivatives whereby the aromatic substitution pattern has been translated into the one of the three-dimensional heterocycle. These molecules can be considered rigid pyrrolidine analogues with a well-defined orientation of their substituents. Furthermore, unsupervised clustering of an expansive virtual database of saturated N-heterocycles revealed these derivatives as effective isosteres of rigidified piperidines. Overall, this platform enables the conversion of nitroarene feedstocks into complex sp3-rich heterocycles of potential interest to drug development.

Collagen Anchoring Protein-Nucleic Acid Chimeric Probe for In Situ In Vivo Mapping of a Tumor-Specific Protease.

In situ analysis of biomarkers in the tumor microenvironment (TME) is important to reveal their potential roles in tumor progression and early diagnosis of tumors but remains a challenge. In this work, a bottom-up modular assembly strategy was proposed for a multifunctional protein-nucleic chimeric probe (PNCP) for in situ mapping of cancer-specific proteases. PNCP, containing a collagen anchoring module and a target proteolysis-responsive isothermal amplification sensor module, can be anchored in the collagen-rich TME and respond to the target protease in situ and generate amplified signals through rolling cycle amplification of tandem fluorescent RNAs. Taking matrix metalloproteinase 2 (MMP-2), a tumor-associated protease, as the model, the feasibility of PNCP was demonstrated for the in situ detection of MMP-2 activity in 3D tumor spheroids. Moreover, in situ in vivo mapping of MMP-2 activity was also achieved in a metastatic solid tumor model with high sensitivity, providing a useful tool for evaluating tumor metastasis and distinguishing highly aggressive forms of tumors.

π-Extended Helical Multilayer Nanographenes with Layer-Dependent Chiroptical Properties.

Helical nanographenes (NGs) have attracted increasing attention recently because of their intrinsic chirality and exotic chiroptical properties. However, the efficient synthesis of extended helical NGs featuring a multilayer topology is still underdeveloped, and their layer-dependent chiroptical properties remain elusive. In this study, we demonstrate a modular synthetic strategy to construct a series of novel helical NGs (1-3) with a multilayer topology through a consecutive Diels-Alder reaction and regioselective cyclodehydrogenation from the readily accessible phenanthrene-based precursors bearing ethynyl groups. The resultant NGs exhibit bilayer, trilayer, and tetralayer structures with elongated π extension and rigid helical backbones, as unambiguously confirmed by single-crystal X-ray or electron diffraction analysis. We find that the photophysical properties of these helical NGs are notably influenced by the degree of π extension, which varies with the number of layers, leading to obvious redshifted absorption, a fast rising molar extinction coefficient (ε), and markedly boosted fluorescence quantum yield (Φf). Moreover, the embedded [7]helicene subunits in these NGs result in stable chirality, enabling both chiral resolution and exploration of their layer-dependent chiroptical properties. Profiting from the good alignment of electric and magnetic dipole moments determined by the multilayer structure, the resultant NGs exhibit excellent circular dichroism and circularly polarized luminescence response with unprecedented high CPL brightness up to 168 M-1 cm-1, rendering them promising candidates for CPL emitters.

Abstract C118: Design of programmable peptide-guided oncoprotein degraders via generative language models

Abstract Targeted protein degradation of pathogenic proteins represents a powerful new treatment strategy for multiple cancers. Unfortunately, a sizable portion of these proteins are considered “undruggable” by standard small molecule-based approaches, including PROTACs and molecular glues, largely due to their disordered nature, instability, and lack of binding site accessibility. As a more modular strategy, we have developed a genetically-encoded protein architecture by fusing target-specific peptides to E3 ubiquitin ligase domains for selective and potent intracellular degradation of oncoproteins. To enable programmability of our system, we develop a suite of algorithms that enable the design of target-specific peptides via protein language model (pLM) embeddings, without the requirement of 3D structures. First, we train a model that leverages pLM embeddings to efficiently select high-affinity peptides from natural protein interaction interfaces. Next, we develop a high-accuracy discriminator, based on the contrastive language-image pretraining (CLIP) architecture underlying OpenAI's DALL-E model, to prioritize and screen peptides with selectivity to a specified target oncoprotein. As input to the discriminator, we create a Gaussian diffusion generator to sample a pLM latent space, fine-tuned on experimentally-valid peptide sequences. Finally, to enable de novo design of binding peptides, we train an instance of GPT-2 with protein interacting sequences to enable peptide generation conditioned on target oncoprotein sequences. Our models demonstrate low perplexities across both existing and generated peptide sequences, highlighting their robust generative capability. By experimentally fusing model-derived peptides to E3 ubiquitin ligase domains, we reliably identify candidates exhibiting robust and selective endogenous degradation of diverse, "undruggable" oncoproteins in cancer cell models, including tumorigenic regulators such as β-catenin and TRIM8, as well as oncogenic fusion proteins, such as EWS-FLI1, PAX3-FOXO1, and DNAJB1-PRKACA. We further show that our peptide-guided degraders have negligible off-target effects via whole-cell proteomics and demonstrate their modulation of transcriptional and apoptotic pathways, motivating further translation of our therapeutic platform. Together, our work establishes a CRISPR-analogous system for programmable protein degradation applications across the oncoproteome. Citation Format: Suhaas Bhat, Garyk Brixi, Kalyan Palepu, Lauren Hong, Vivian Yudistyra, Tianlai Chen, Sophia Vincoff, Lin Zhao, Pranam Chatterjee. Design of programmable peptide-guided oncoprotein degraders via generative language models [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C118.

Palladium‐Catalyzed anti‐Markovnikov Halosulfonamidation of Unactivated Alkene†

Comprehensive SummaryPalladium‐catalyzed regioselective amination of unactivated alkene remains a challenge and is of great interest. Herein, a three‐component coupling of unactivated alkene, sulfonamide, and N‐halo compounds accessing vicinal haloamine has been conceived. This aminohalogenation represents a modular and regioselective strategy. The electrophilic halogenating agent enables regioselective anti‐Markovnikov aminopalladation and facilitates subsequent halogenation events. And this protocol is characterized by gram‐scale syntheses and late‐stage functionalizations. Of note, the recovered byproduct phthalimide allows for reusing by conversion to the starting material.

Light-Activated Assembly of DNA Origami into Dissipative Fibrils.

Hierarchical DNA nanostructures offer programmable functions at scale, but making these structures dynamic, while keeping individual components intact, is challenging. Here we show that the DNA A-motif-protonated, self-complementary poly(adenine) sequences-can propagate DNA origami into one-dimensional, micron-length fibrils. When coupled to a small molecule pH regulator, visible light can activate the hierarchical assembly of our DNA origami into dissipative fibrils. This system is recyclable and does not require DNA modification. By employing a modular and waste-free strategy to assemble and disassemble hierarchical structures built from DNA origami, we offer a facile and accessible route to developing well-defined, dynamic, and large DNA assemblies with temporal control. As a general tool, we envision that coupling the A-motif to cycles of dissipative protonation will allow the transient construction of diverse DNA nanostructures, finding broad applications in dynamic and non-equilibrium nanotechnology.

Exploring the three-dimensional space with modular concrete shells: Form-finding, design and structural analysis

Thin concrete shells can span large distances thanks to their structural efficiency, following the optimum force flow. Despite the efficient use of material of well-designed shells, their construction suffers from labor-intensive and time-consuming fabrication techniques. This research therefore proposes a design method for the facilitated manufacturing of freeform shell structures. The manufacturing process has been considered from the design phase on, by generating an extensive range of different shell configurations based on a limited set of form-found modules and thus formworks. The challenge was to define which curved module geometries, after assembly, lead to the largest variety of freeform surfaces. More than sixty configurations resulted from a set of four modules only. This paper will discuss the form-finding process as well as the geometrical arrangement using tessellation patterns for the achievement of those modular configurations, going from domed to undulating shell shapes. In order to evaluate the structural efficiency of the shapes, twenty modular configurations were selected and analyzed under self-weight, using finite element simulations. To conclude, a prototype of one modular base element has been built, using a reusable casting wax in combination with a grid shell box, as the main parts of the formwork. The results demonstrate the structural feasibility and limitations of the modular shell design strategy. The modular approach contributes to circularity by encouraging reuse and allowing various combinations with a repeating set of modules, striving as such towards resource-effective innovative shell structures.

Evolutionary design of explainable algorithms for biomedical image segmentation

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.

Modular design of curved beam-based recyclable architected materials

Advances in manufacturing technologies have enabled architected materials with unprecedented properties. These materials are typically irreversibly designed and fabricated with characteristic geometries and specific mechanical properties, thus rendering them suitable for pre-specified requests. However, these materials cannot be recycled or reconstructed into different shapes and functionalities to economically adapt to various environments. Hence, we present a modular design strategy to create a category of recyclable architected materials comprising elastic initially curved beams and rigid cylindrical magnets. Based on numerical analyses and physical prototypes, we introduce an arc-serpentine curved beam (ASCB) and systematically investigate its mechanical properties. Subsequently, we develop two sets of hierarchical modules for the ASCB, thus expanding the constructable shape of architected materials from regular cuboids to complex curved surfaces. Furthermore, we demonstrate that the magnets attached to the centers of specific serpentine patterns of the modules allows the effective in-situ recycling of the designed materials, including sheet materials for non-damage storage, bulk materials for tunable stiffness, and protective package boxes for reshaping into decorative lampshades. We expect our approach to improve the flexibility of architected materials for multifunctional implementation in resource-limited scenarios.

Modular Customization and Regulation of Metal-Organic Frameworks for Efficient Membrane Separations.

Metal-organic frameworks (MOFs) are considered ideal membrane candidates for energy-efficient separations. However, the MOF membrane amount to date is only a drop in the bucket compared to the material collections. The fabrication of an arbitrary MOF membrane exhibiting inherent separation capacity of the material remains a long-standing challenge. Herein, we report a MOF modular customization strategy by employing four MOFs with diverse structures and physicochemical properties and achieving innovative defect-free membranes for efficient separation validation. Each membrane fully displays the separation potential according to the MOF pore/channel microenvironment, and consequently, an intriguing H2 /CO2 separation performance sequence is achieved (separation factor of 1656-5.4, H2 permeance of 964-2745 gas permeation unit). Taking advantage of this strategy, separation performance can be manipulated by a non-destructive modification separately towards the MOF module. This work establishes a universal full-chain demonstration for membrane fabrication-separation validation-microstructure modification and opens an avenue for exclusive customization of membranes for important separations.

Insulin-like Growth Factor 2 (IGF2)-Fused Lysosomal Targeting Chimeras for Degradation of Extracellular and Membrane Proteins.

Targeted degradation of the cell-surface and extracellular proteins via the endogenous lysosomal degradation pathways, such as lysosome-targeting chimeras (LYTACs), has recently emerged as an attractive tool to expand the scope of extracellular chemical biology. Herein, we report a series of recombinant proteins genetically fused to insulin-like growth factor 2 (IGF2), which we termed iLYTACs, that can be conveniently obtained in high yield by standard cloning and bacterial expression in a matter of days. We showed that both type-I iLYTACs, in which IGF2 was fused to a suitable affibody or nanobody capable of binding to a specific protein target, and type-II iLYTAC (or IGF2-Z), in which IGF2 was fused to the IgG-binding Z domain that served as a universal antibody-binding adaptor, could be used for effective lysosomal targeting and degradation of various extracellular and membrane-bound proteins-of-interest. These heterobifunctional iLYTACs are fully genetically encoded and can be produced on a large scale from conventional E. coli expression systems without any form of chemical modification. In the current study, we showed that iLYTACs successfully facilitated the cell uptake, lysosomal localization, and efficient lysosomal degradation of various disease-relevant protein targets from different mammalian cell lines, including EGFR, PD-L1, CD20, and α-synuclein. The antitumor properties of iLYTACs were further validated in a mouse xenograft model. Overall, iLYTACs represent a general and modular strategy for convenient and selective targeted protein degradation, thus expanding the potential applications of current LYTACs and related techniques.

Pushing Up the Size Limit of Boron‐doped peri‐Acenes: Modular Synthesis and Characterizations

AbstractHeteroatom‐doped peri‐acenes (PAs) have recently attracted considerable attention considering their fascinating physical properties and chemical stability. However, the precise sole addition of boron atoms along the zigzag edges of PAs remains challenging, primarily due to the limited synthetic approach. Herein, we present a novel one‐pot modular synthetic strategy toward unprecedented boron‐doped PAs (B‐PAs), including B‐[4,2]PA (1 a‐2), B‐[4,3]PA (1 b‐2) and B‐[7,2]PA (1 c‐3) derivatives, through efficient intramolecular electrophilic borylation. Their chemical structures are unequivocally confirmed with a combination of mass spectrometry, NMR, and single‐crystal X‐ray diffraction analysis. Notably, 1 b‐2 exhibits an almost planar geometry, whereas 1 a‐2 displays a distinctive bowl‐like distortion. Furthermore, the optoelectronic properties of this series of B‐PAs are thoroughly investigated by UV/Vis absorption and fluorescence spectroscopy combined with DFT calculation. Compared with their parent all‐carbon analogs, the obtained B‐PAs exhibit high stability, wide energy gaps, and high photoluminescence quantum yields of up to 84 %. This study reveals the exceptional ability of boron doping to finely tune the physicochemical properties of PAs, showcasing their potential applications in optoelectronics.

Pushing Up the Size Limit of Boron-doped peri-Acenes: Modular Synthesis and Characterizations.

Heteroatom-doped peri-acenes (PAs) have recently attracted considerable attention considering their fascinating physical properties and chemical stability. However, the precise sole addition of boron atoms along the zigzag edges of PAs remains challenging, primarily due to the limited synthetic approach. Herein, we present a novel one-pot modular synthetic strategy toward unprecedented boron-doped PAs (B-PAs), including B-[4,2]PA (1 a-2), B-[4,3]PA (1 b-2) and B-[7,2]PA (1 c-3) derivatives, through efficient intramolecular electrophilic borylation. Their chemical structures are unequivocally confirmed with a combination of mass spectrometry, NMR, and single-crystal X-ray diffraction analysis. Notably, 1 b-2 exhibits an almost planar geometry, whereas 1 a-2 displays a distinctive bowl-like distortion. Furthermore, the optoelectronic properties of this series of B-PAs are thoroughly investigated by UV/Vis absorption and fluorescence spectroscopy combined with DFT calculation. Compared with their parent all-carbon analogs, the obtained B-PAs exhibit high stability, wide energy gaps, and high photoluminescence quantum yields of up to 84 %. This study reveals the exceptional ability of boron doping to finely tune the physicochemical properties of PAs, showcasing their potential applications in optoelectronics.

High-Level Production of Lacto-N-neotetraose in Escherichia coli by Stepwise Optimization of the Biosynthetic Pathway.

Lacto-N-neotetraose (LNnT), an abundant human milk oligosaccharide (HMO), has been approved as a novel functional additive for infant formulas. Therefore, LNnT biosynthesis has attracted extensive attention. Here, a high LNnT-producing, low lacto-N-triose II (LNT II)-residue Escherichia coli strain was constructed. First, an initial LNnT-producing chassis strain was constructed by blocking lactose, UDP-N-acetylglucosamine, and UDP-galactose competitive consumption pathways and introducing β-1,3-N-acetylglucosaminyltransferase LgtA and β-1,4-galactosyltransferase LgtB. Subsequently, the supply of LNnT precursors was increased by enhancing UDP-N-acetylglucosamine and UDP-galactose synthesis, inactivating LNT II extracellular transporter SetA, and improving UTP synthesis. Then, modular engineering strategy was used to optimize LNnT biosynthetic pathway fluxes. Moreover, pathway fluxes were fine-tuned by modulating translation initiation strength of essential genes lgtB, prs, and lacY. Finally, LNnT production reached 6.70 g/L in a shake flask and 19.40 g/L in a 3 L bioreactor with 0.47 g/(L h) productivity, with 1.79 g/L LNT II residue, highest productivity level, and lowest LNT II residue thus far.

Online Meta-Learning for Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.

Engineering cell-electrode interfacial electron transfer to boost power generation of electroactive biofilm

Electroactive biofilm plays a crucial rule in the electron transfer efficiency of bio-electrochemical systems (BES). However, the slow rate of interfacial electron transfer (IET) restricts practical applications of various BES. Here, a modular engineering strategy was developed to enhance IET rate. Firstly, to accelerate the low transmembrane electron transfer rate caused by insulative cell membrane of Shewanella oneidensis, pili-based artificial conductive nanowires and outer-membrane c-cytochrome OmcF from Geobacter sulfurreducens were heterologously expressed to construct transmembrane electron conduits. Secondly, to improve the low electron transfer rate from S. oneidensis to anode owing to the poor electron collection ability of the anode, N-doped carbon nanotubes and polyriboflavin were used to increase the surface area and active sites of the anode. Thirdly, to further reduce the resistance due to the low conductivity of biofilm, the polydopamine was coated in situ to construct high-speed conductive networks, obtaining an unprecedented power density of 5233.7 ± 364.7 mW/m2, ∼83.1-fold higher than that of the wild-type strain (62.2 ± 5.5 mW/m2), and the maximum coulomb efficiency of ESR1 @PDA was 85.4%, which, to the best of our knowledge, is one of the highest output power densities and coulomb efficiencies that have ever reported in the genetically engineered Shewanella strains. This study demonstrated an integrated biotic-electrode modular engineering strategy to boost power generation of electroactive biofilm via synthetic biology and material engineering.

Strong Protein Adhesives through Lanthanide-enhanced Structure Folding and Stack Density.

Generating strong adhesion by engineered proteins has the potential for high technical applications. Current studies of adhesive proteins are primarily limited to marine organisms, e.g., mussel adhesive proteins. Here, we present a modular engineering strategy to generate a type of exotic protein adhesives with super strong adhesion behaviors. In the protein complexes, the lanmodulin (LanM) underwent α-helical conformational transition induced by lanthanides, thereby enhancing the stacking density and molecular interactions of adhesive protein. The resulting adhesives exhibited outstanding lap-shear strength of ~31.7 MPa, surpassing many supramolecular and polymer adhesives. The extreme temperature (-196 to 200°C) resistance capacity and underwater adhesion performance can significantly broaden their practical application scenarios. Ex vivo and in vivo experiments further demonstrated the persistent adhesion performance for surgical sealing and healing applications.

Optimizing vestibular neuritis management with modular strategies.

This study proposes a "modular management" approach for vestibular neuritis (VN) to reduce chronicization and improve patient prognosis. The approach involves multi-factor grading and hierarchical intervention and was found to be more effective than traditional treatment strategies. This retrospective analysis compared two groups of VN patients from two medical institutions. The intervention group of 52 patients received "modular management," while the control group of 51 patients did not receive this kind of management. Analyzed the early treatment strategies, 6-month prognosis, and other indicators of the two groups of patients, compared and analyzed their overall prognosis, and identified the risk factors affecting the chronicization. The modular management group had lower dizziness severity, better balance, lower anxiety, and higher video head impulse testing (v-HIT) gain after 6 months of onset. Analysis of factors related to persistent postural-perceptual dizziness (PPPD) in patients with VN showed positive correlations between the time from onset to diagnosis and PPPD, and Vertigo Symptom Scale (VSS), Dizziness Handicap Inventory (DHI), anxiety, and depression. Normalized vestibular rehabilitation was negatively correlated with PPPD, while gender, age, and early steroid use had no significant correlation. The multi-factor logistic regression model correctly classified 93.20% of the study subjects with a sensitivity of 87.50% and specificity of 94.90%. The proposed "modular management" scheme for VN is a comprehensive and dynamic approach that includes health education, assessment, rehabilitation, therapy, evaluation, and prevention. It can significantly improve patient prognosis and reduce chronicization by shifting from simple acute treatment to continuous management.

Modular synthetic strategy for N/C-terminal protected amyloidogenic peptides.

N/C-terminal protected amyloidogenic peptides are valuable biomaterials. Optimization of the protective structures at both termini is, however, synthetically laborious because a linear sequence of solid-phase peptide synthesis protocol (on-resin peptide assembly/peptide removal from resin/high-performance liquid chromatography purification) is required for the peptides each time the protective group is modified. In this study, we demonstrate a modular synthetic strategy for the purpose of rapidly deriving the N/C-terminal structures of amyloidogenic peptides. The precursor sequences that can be easily synthesized due to a non-amyloidogenic property were stocked as the synthetic intermediates. Condensation of the intermediates with N/C-terminal units in a liquid phase followed by high-performance liquid chromatography purification gave the desired peptides P1-P8. The amyloidogenic peptides that have various N/C-terminal protective structures were therefore synthesized in a labor-effective manner. This method is suggested to be useful for synthesizing amyloidogenic peptides possessing divergent protective structures at the N/C-terminus.

Strong Protein Adhesives through Lanthanide‐enhanced Structure Folding and Stack Density

Generating strong adhesion by engineered proteins has the potential for high technical applications. Current studies of adhesive proteins are primarily limited to marine organisms, e.g., mussel adhesive proteins. Here, we present a modular engineering strategy to generate a type of exotic protein adhesives with super strong adhesion behaviors. In the protein complexes, the lanmodulin (LanM) underwent α‐helical conformational transition induced by lanthanides, thereby enhancing the stacking density and molecular interactions of adhesive protein. The resulting adhesives exhibited outstanding lap‐shear strength of ~31.7 MPa, surpassing many supramolecular and polymer adhesives. The extreme temperature (‐196 to 200°C) resistance capacity and underwater adhesion performance can significantly broaden their practical application scenarios. Ex vivo and in vivo experiments further demonstrated the persistent adhesion performance for surgical sealing and healing applications.