Universal Building Block Research Articles

SYNTHESIS OF N-BENZYLPIPERIDINE-4-ON DERIVATIVES (Review)

In the field of organic synthetic pharmaceuticals, methods for the discovery, creation, and synthesis of universal molecules with pharmacological activity are rapidly advancing worldwide. Optimal synthesis methods enable the production of analytically pure biologically active compounds with the highest possible yields and minimal waste, which helps conserve resources, time, and money. The diversity of biologically active drug groups increases the ability to meet the medical needs of the population. The search for optimal synthesis methods drives the development of new technologies and techniques, which, in turn, contributes to scientific discoveries in the fields of Medicine and Chemistry. The purpose of this review is to examine the literature on methods for obtaining biologically active derivatives of N-benzylpiperidin-4-one published in scientific journals. Research Object: Derivatives of N-benzylpiperidin-4-one. Research results: The study of the reactivity of Nbenzylpiperidin-4-one derivatives confirms their versatility in various chemical reactions, highlighting their utility as chemical compounds. The results compiled in scientific literature demonstrate the potential of N-benzylpiperidin-4-one derivatives in the development of new drugs with various pharmacological properties. Conclusion: The properties of the N-benzylpiperidin-4-one molecule as a universal chemical building block indicate its wide application in medicine and pharmacology, enhancing its significance in further scientific research and the synthesis of new compounds.

A synthetic cell-free pathway for biocatalytic upgrading of one-carbon substrates.

Biotechnological processes hold tremendous potential for the efficient and sustainable conversion of one-carbon (C1) substrates into complex multi-carbon products. However, the development of robust and versatile biocatalytic systems for this purpose remains a significant challenge. In this study, we report a hybrid electrochemical-biochemical cell-free system for the conversion of C1 substrates into the universal biological building block acetyl-CoA. The synthetic reductive formate pathway (ReForm) consists of five core enzymes catalyzing non-natural reactions that were established through a cell-free enzyme engineering platform. We demonstrate that ReForm works in a plug-and-play manner to accept diverse C1 substrates including CO2 equivalents. We anticipate that ReForm will facilitate efforts to build and improve synthetic C1 utilization pathways for a formate-based bioeconomy.

Mimicking Mytilus edulis foot protein: A versatile strategy for robust biomedical coatings

Universal coatings with versatile surface adhesion, good mechanochemical robustness, and the capacity for secondary modification are of great scientific interest. However, incorporating these advantages into a system is still a great challenge. Here, we report a series of catechol-decorated polyallylamines (CPAs), denoted as pseudo-Mytilus edulis foot protein 5 (pseudo-Mefp-5), that mimic not only the catechol and amine groups but also the backbone of Mefp-5. CPAs can fabricate highly adhesive, robust, multifunctional polyCPA (PCPA) coatings based on synergetic catechol-polyamine chemistry as universal building blocks. Due to the interpenetrating entangled network architectures, these coatings exhibit high chemical robustness against harsh conditions (HCl, pH 1; NaOH, pH 14; H2O2, 30%), good mechanical robustness, and wear resistance. In addition, PCPA coatings provide abundant grafting sites, enabling the fabrication of various functional surfaces through secondary modification. Furthermore, the versatility, multifaceted robustness, and scalability of PCPA coatings indicate their great potential for surface engineering, especially for withstanding harsh conditions in multipurpose biomedical applications.

Elucidating the role of diverse mineralisation paradigms on bone biomechanics - a coarse-grained molecular dynamics investigation.

Bone as a hierarchical composite structure plays a myriad of roles in vertebrate skeletons including providing the structural stability of the body. Despite this critical role, the mechanical behaviour at the sub-micron levels of bone's hierarchy remains poorly understood. At this scale, bone is composed of Mineralised Collagen Fibrils (MCF) embedded within an extra-fibrillar matrix that consists of hydroxyapatite minerals and non-collagenous proteins. Recent experimental studies hint at the significance of the extra-fibrillar matrix in providing the bone with the stiffness and ductility needed to serve its structural roles. However, due to limited resolution of experimental tools, it is not clear how the arrangement of minerals, and in particular their relative distribution between the intra- and extra-fibrillar space contribute to bone's remarkable mechanical properties. In this study, a Coarse Grained Molecular Dynamics (CGMD) framework was developed to study the mechanical properties of MCFs embedded within an extra-fibrillar mineral matrix and the precise roles extra- and intra-fibrillar mineralisation on the load-deformation response was investigated. It was found that the presence of extra-fibrillar mineral resulted in the development of substantial residual stress in the system, by limiting MCF shortening that took place during intra-fibrillar mineralisation, resulting in substantial compressive residual stresses in the extra-fibrillar mineral phase. The simulation results also revealed the crucial role of extra-fibrillar mineralisation in determining the elastic response of the Extrafibrillar mineralised MCF (EFM-MCF) system up to the yield point, while the fibrillar collagen affected the post-yield behaviour. When physiological levels of mineralisation were considered, the mechanical response of the EFM-MCF systems was characterised by high ductility and toughness, with micro-cracks being distributed across the extra-fibrillar matrix, and MCFs effectively bridging these cracks leading to an excellent combination of strength and toughness. Together, these results provide novel insight into the deformation mechanisms of an EFM-MCF system and highlight that this universal building block, which forms the basis for lamellar bone, can provide an excellent balance of stiffness, strength and toughness, achieving mechanical properties that are far beyond the capabilities of the individual constituents acting alone.

Multiscale modeling reveals the ion-mediated phase separation of nucleosome core particles

Due to the vast length scale inside the cell nucleus, multiscale models are required to understand chromatin folding, structure, and dynamics and how they regulate genomic activities such as DNA transcription, replication, and repair. We study the interactions and structure of condensed phases formed by the universal building block of chromatin, the nucleosome core particle (NCP), using bottom-up multiscale coarse-grained (CG) simulations with a model extracted from all-atom MD simulations. In the presence of the multivalent cations Mg(H2O)62+ or CoHex3+, we analyze the internal structures of the NCP aggregates and the contributions of histone tails and ions to the aggregation patterns. We then derive a "super" coarse-grained (SCG) NCP model to study the macroscopic scale phase separation of NCPs. The SCG simulations show the formation of NCP aggregates with Mg(H2O)62+ concentration-dependent densities and sizes. Variation of the CoHex3+ concentrations results in highly ordered lamellocolumnar and hexagonal columnar phases in agreement with experimental data. The results give detailed insights into nucleosome interactions and for understanding chromatin folding in the cell nucleus.

Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins

We report a flexible language-model-based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict the secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural materials, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform eight distinct tasks, with available datasets, it can be extended to solve additional problems. In a broader sense, this study illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties, via a synergizing learning capacity, to express a set of potentialities embedded in the knowledge used in training via the interplay of universality and diversity. Significance statement: Predicting the properties of materials based on a flexible description of their structure, environment, or process, is a long-standing challenge in multiscale modeling. Our MaterioFormer language model, trained to solve forward and inverse tasks, incorporates a deep learning capacity through attention and graph strategies to yield a multimodal approach to model and design materials. Since our model is prompt-based and information is encoded consistently via byte-level utf8 tokenization, it can process diverse modalities of information, such as sequence data, description of tasks, and numbers, and offers a flexible workflow that integrates human intelligence and artificial intelligence. Autoregressive training, using pre-training against a large unlabeled dataset, allows for straightforward adjustment of specific objectives.

Protein kinase ATR inhibits E3 ubiquitin ligase CRL4PRL1 to stabilize ribonucleotide reductase in response to replication stress

The protein kinase ATR is essential for replication stress responses in all eukaryotes. Ribonucleotide reductase (RNR) catalyzes the formation of deoxyribonucleotide (dNTP), the universal building block for DNA replication and repair. However, the relationship between ATR and RNR is not well understood. Here, we show that ATR promotes the protein stability of RNR in Arabidopsis. Through an activation tagging-based genetic screen, we found that overexpression of TSO2, a small subunit of RNR, partially suppresses the hypersensitivity of the atr mutant to replication stress. Biochemically, TSO2 interacts with PRL1, a central subunit of the Cullin4-based E3 ubiquitin ligase CRL4PRL1, which polyubiquitinates TSO2 and promotes its degradation. ATR inhibits CRL4PRL1 to attenuate TSO2 degradation. Our work provides an important insight into the replication stress responses and a post-translational regulatory mechanism for RNR. Given the evolutionary conservation of the proteins involved, the ATR-PRL1-RNR module may act across eukaryotes.

Logarithmic correction to black hole entropy in universal low-energy string theory models

We calculate the logarithmic correction to the entropy of asymptotically flat and AdS black holes (rotating, non-rotating, charged, and uncharged) embedded in Einstein-Maxwell-dilaton (EMD) theories with U(1)-charged. The leading quantum gravitational corrections are achieved in both extremal and non-extremal limits of black hole temperature by designing a common Euclidean gravity setup that evaluates the “logarithmic term” from one-loop effective actions via heat kernel method-based calculations. EMD theories are universal building blocks of compactified string theory or supergravity models in 4D. For a concrete example, we generalize the entire setup and calculate logarithmic corrections for black holes in U(1)2-charged EMD models intersecting with mathcal{N} = 4 ungauged and gauged bosonic supergravity. In contrast to flat backgrounds, all the AdS4 results are found to be non-topological, providing a wider “infrared window” into the microscopic degrees of freedom of black holes in string theory.

The Multifaceted MEP Pathway: Towards New Therapeutic Perspectives.

Isoprenoids, a diverse class of natural products, are present in all living organisms. Their two universal building blocks are synthesized via two independent pathways: the mevalonate pathway and the 2-C-methyl-ᴅ-erythritol 4-phosphate (MEP) pathway. The presence of the latter in pathogenic bacteria and its absence in humans make all its enzymes suitable targets for the development of novel antibacterial drugs. (E)-4-Hydroxy-3-methyl-but-2-enyl diphosphate (HMBPP), the last intermediate of this pathway, is a natural ligand for the human Vγ9Vδ2 T cells and the most potent natural phosphoantigen known to date. Moreover, 5-hydroxypentane-2,3-dione, a metabolite produced by Escherichia coli 1-deoxy-ᴅ-xylulose 5-phosphate synthase (DXS), the first enzyme of the MEP pathway, structurally resembles (S)-4,5-dihydroxy-2,3-pentanedione, a signal molecule implied in bacterial cell communication. In this review, we shed light on the diversity of potential uses of the MEP pathway in antibacterial therapies, starting with an overview of the antibacterials developed for each of its enzymes. Then, we provide insight into HMBPP, its synthetic analogs, and their prodrugs. Finally, we discuss the potential contribution of the MEP pathway to quorum sensing mechanisms. The MEP pathway, providing simultaneously antibacterial drug targets and potent immunostimulants, coupled with its potential role in bacterial cell-cell communication, opens new therapeutic perspectives.

A coarse-grained molecular dynamics investigation of the role of mineral arrangement on the mechanical properties of mineralized collagen fibrils.

Mineralized collagen fibrils (MCFs) comprise collagen molecules and hydroxyapatite (HAp) crystals and are considered universal building blocks of bone tissue, across different bone types and species. In this study, we developed a coarse-grained molecular dynamics (CGMD) framework to investigate the role of mineral arrangement on the load-deformation behaviour of MCFs. Despite the common belief that the collagen molecules are responsible for flexibility and HAp minerals are responsible for stiffness, our results showed that the mineral phase was responsible for limiting collagen sliding in the large deformation regime, which helped the collagen molecules themselves undergo high tensile loading, providing a substantial contribution to the ultimate tensile strength of MCFs. This study also highlights different roles for the mineralized and non-mineralized protofibrils within the MCF, with the mineralized groups being primarily responsible for load carrying due to the presence of the mineral phase, while the non-mineralized groups are responsible for crack deflection. These results provide novel insight into the load-deformation behaviour of MCFs and highlight the intricate role that both collagen and mineral components have in dictating higher scale bone biomechanics.

Time-Optimal Current Control of Synchronous Motor Drives

We are concerned with the problem of fast and accurate tracking of currents in the general synchronous drive. The problem becomes complicated with decreasing available voltage, which is common in high-speed and field weakening regimes. The existing time-optimal controllers rely on a simplified model, ignoring stator resistance and differences in inductances. We derive a solution for the general model considering all parameters and show how the parameters affect the current trajectory. One simplifying assumption had to be made, but we show in simulation that it has a negligible impact on accuracy. The simplification allowed for the design of a feed-forward controller that has a low computational cost and can be easily implemented in realtime. We provide experimental validation of the controller on the developed IPMSM drive prototype of the rated power of 4.5 kW using conventional industrial DSP. The controller is compared to conventional PI and deadbeat solutions, demonstrating that the time-optimal controller can reach the required setpoint four times faster than the competitors at the field weakening regime of the drive. The proposed feed-forward control can be seen as a universal building block that can be combined with existing feedback controllers and observers and thus incorporated into existing control solutions.

End-functionalized diblock copolymers by mix and match of poly(2-oxazoline) and polyester building blocks

2-(4-(Iodomethyl)benzyl)isoindoline-1,3-dione was utilized as a protected amine-functionalized initiator for the cationic ring opening-polymerization (CROP) of 2-ethyl-2-oxazoline. As kinetic studies indicated a well-controlled polymerization, the CROP was terminated with sodium azide. Subsequent deprotection of the α-end group enabled successive attachment of a targeting ligand, e.g., retinoic acid or a proteogenic amino acid as a peptide model at the α-chain end. The heterotelechelic PEtOx were coupled to cyclooctyne end-functionalized polylactide, which was obtained by initiating the ring opening polymerization of l-lactide via a cyclooctyne based alcohol, utilizing strain-promoted azide-alkyne cycloaddidtion. In-depth characterization via mass spectrometry, 1H NMR spectroscopy, size exclusion chromatography, and infrared spectroscopy investigations verified the success of all synthetic steps and the preservation of retinoyl and leucine moieties also in the end-functionalized PEtOx-b-PLA block copolymers. Circumventing potential degradation of the polyester block during ligand attachment, the heterotelechelic PEtOx represents a universal building block useful for modular approaches towards tailored materials for nanomedicine.

Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach

Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine.

Synapse and Tunable Leaky-Integrate Neuron Functions Enabled by Oxide Trapping Dynamics in a Single Logic Transistor

We show that a high-k gated n-MOSFET can embody both the memory plasticity of a synapse and leaky-integration of a neuron, by virtue of the rich temporal dynamics of charge capture/emission by gate-oxide defects. In addition, a tunable leaky-integrate function is demonstrated. The lower limit of energy per input spike is on the order of fJ, which provides room for low-power trigger circuit design and scalability. This work points to the prospect of a gate-engineered logic transistor serving as the universal building block of hardware spiking neural networks, thereby accelerating the realization of compact, energy efficient neuromorphic computers given the relative maturity of the transistor technology.

Universal 4-qualifiable fluorene-based building blocks for potential optoelectronic applications

In the design of conjugated molecules, modular production enables materials to easily realize structure modification and precisely tune their photoelectrical property. Construction of a novel and universal building block is crucial to design and manufacture high performance and stable conjugated molecules for optoelectronic application. Herein, we originally demonstrated a universal 4-qualifiable fluorene-based building block, which is a fundamental molecular segment to functionalize and obtain novel conjugated materials. Compared to the traditional modification at 9-site, additional 4-position functionalization provided an exciting blueprint to not only tune electronic structure and excited state via p-n molecular design engineering and space charge-transfer strategy, but also allow for optimizing intermolecular arrangement and obtaining solution-processing ability. The introduction of the 4-site substituent in fluorene based semiconductors may endow materials with unique properties. Finally, we successfully prepared two stable deep-blue light-emitting conjugated polymer, PODOPF and PODOF, by utilizing the 4-substituent fluorene based building block. It is believable that the performance, stability and processibility of reported outstanding fluorene-based conjugated molecules can be further optimized based on this universal building block.

Future‐Oriented Advanced Diarylethene Photoswitches: From Molecular Design to Spontaneous Assembly Systems

Diarylethene (DAE) photoswitch is a new and promising family of photochromic molecules and has shown superior performance as a smart trigger in stimulus-responsive materials. During the past few decades, the DAE family has achieved a leap from simple molecules to functional molecules and developed toward validity as a universal switching building block. In recent years, the introduction of DAE into an assembly system has been an attractive strategy that enables the photochromic behavior of the building blocks to be manifested at the level of the entire system, beyond the DAE unit itself. This assembly-based strategy will bring many unexpected results that promote the design and manufacture of a new generation of advanced materials. Here, recent advances in the design and fabrication of diarylethene as a trigger in materials science, chemistry, and biomedicine are reviewed.

Putting DNA to Work as Generic Polymeric Materials

AbstractDNA is a true polymer that stores the genetic information of an organism. With its amazing biological and polymeric characteristics, DNA has been regarded as a universal building block for the construction of diverse materials for real‐world applications. Through various approaches including ligation, polymerization, chemical crosslinking, and physical crosslinking, both pure and hybrid DNA gels have been developed as generic materials. This Review discusses recent advances in the construction of DNA‐based networks without considering any of DNA′s genetic properties. In addition, we highlight the biomedical and non‐biomedical applications of DNA as generic materials. Owing to the superb molecular recognition, self‐assembly, and responsiveness of DNA, a mushrooming number of DNA materials with various properties have been developed for general utilization.

Putting DNA to Work as Generic Polymeric Materials.

DNA is a true polymer that stores the genetic information of an organism. With its amazing biological and polymeric characteristics, DNA has been regarded as a universal building block for the construction of diverse materials for real-world applications. Through various approaches including ligation, polymerization, chemical crosslinking, and physical crosslinking, both pure and hybrid DNA gels have been developed as generic materials. This Review discusses recent advances in the construction of DNA-based networks without considering any of DNA's genetic properties. In addition, we highlight the biomedical and non-biomedical applications of DNA as generic materials. Owing to the superb molecular recognition, self-assembly, and responsiveness of DNA, a mushrooming number of DNA materials with various properties have been developed for general utilization.

A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices.

Principles of low dissipation computing from a stochastic circuit model

We introduce a thermodynamically consistent, minimal stochastic model for complementary logic gates built with field-effect transistors. We characterize the performance of such gates with tools from information theory and study the interplay between accuracy, speed, and dissipation of computations. With a few universal building blocks, such as the NOT and NAND gates, we are able to model arbitrary combinatorial and sequential logic circuits, which are modularized to implement computing tasks. We find generically that high accuracy can be achieved provided sufficient energy consumption and time to perform the computation. However, for low-energy computing, accuracy and speed are coupled in a way that depends on the device architecture and task. Our work bridges the gap between the engineering of low dissipation digital devices and theoretical developments in stochastic thermodynamics, and provides a platform to study design principles for low dissipation digital devices.