Challenging Target Research Articles

Revisiting Pyrimidine-Embedded Molecular Frameworks to Probe the Unexplored Chemical Space for Protein-Protein Interactions.

ConspectusProtein-protein interactions (PPIs) are essential in numerous biological processes and diseases, making them attractive yet challenging drug targets. While many advances have been made in traditional drug discovery, targeting PPIs has been difficult due to a lack of specialized chemical libraries designed to modulate these interactions. Current libraries mainly focus on conventional target proteins like enzymes or receptors as substrate analogs rather than small-molecule modulators targeting PPIs. These traditional drug targets behave differently from PPIs. Conventional druggable targets have relatively small surfaces and binding pockets that have allowed them to be targeted with current libraries, but PPIs behave differently than these traditional drug targets. As a result, there is an urgent need for an innovative approach to expand the druggable space.To address this, we developed a privileged substructure-based diversity-oriented synthesis (pDOS) strategy, aimed at creating maximal skeletal diversity to explore broader biochemical space. Pyrimidine serves as the privileged substructure in our approach, which employs several strategies: (i) silver-catalyzed or iodine-mediated tandem cyclizations to generate pyrimidine-embedded polyheterocycles; (ii) diverse pairing strategies to produce pyrimidodiazepine-containing polyheterocyclic skeletons with enhanced scaffold saturation; (iii) skeletal transformation to develop pyrimidine-fused medium-sized azacycles via chemoselective cleavages or migrations of N-N or C-N bond; (iv) design of small-molecule peptidomimetics that systematically mimic three pivotal protein secondary structures using pyrimidodiazepine-based scaffolds; and (v) identification of pyrimidodiazepine-based small-molecules that allosterically inhibits the interaction between human ACE2 and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein to block viral entry into host cells.Through these approaches, we generated 39 distinct pyrimidine-embedded frameworks, demonstrating significant molecular diversity validated by chemoinformatic analyses such as Tanimoto similarity and principal moment of inertia (PMI) analysis. This molecular diversity extends pyrimidine structures beyond traditional linear or bicyclic forms, creating polyheterocycles with enhanced 3D structural diversity. These novel frameworks overcome the limitation of simpler privileged scaffolds, offering promising tools for modulating PPIs.Our pDOS approach highlights how privileged structure-embedded polyheterocycles, particularly those based on pyrimidine, can effectively target previously undruggable PPIs. This strategy provides a new direction for drug discovery, allowing for the development of small molecules that operate beyond traditional drug-like rules. In addition to expanding the chemical space for PPI modulation, our pDOS strategy enables the creation of scaffolds that are particularly suited for targeting complex and dynamic protein interfaces. This innovation could significantly impact therapeutic development, offering solutions for previously intractable drug targets. By expanding the scope of pyrimidine-based scaffolds, we have opened up new possibilities for targeting PPIs and advancing chemical biology.This perspective demonstrates the potential outlines of our pDOS strategy in creating structurally diverse frameworks, offering a platform for the discovery of PPI modulators and facilitating the exploration of untapped biochemical spaces in drug development, potentially transforming the way we approach these complex biological interactions.

Selection of Nucleotide-Encoded Mass Libraries of Macrocyclic Peptides for Inaccessible Drug Targets.

Technological advances and breakthrough developments in the pharmaceutical field are knocking at the door of the "undruggable" fortress with increasing insistence. Notably, the 21st century has seen the emergence of macrocyclic compounds, among which cyclic peptides are of particular interest. This new class of potential drug candidates occupies the vast chemical space between classic small-molecule drugs and larger protein-based therapeutics, such as antibodies. As research advances toward clinical targets that have long been considered inaccessible, macrocyclic peptides are well-suited to tackle these challenges in a post-rule of 5 pharmaceutical landscape. Facilitating their discovery is an arsenal of high-throughput screening methods that exploit massive randomized libraries of genetically encoded compounds. These techniques benefit from the incorporation of non-natural moieties, such as non- proteinogenic amino acids or stabilizing hydrocarbon staples. Exploiting these features for the strategic architectural design of macrocyclic peptides has the potential to tackle challenging targets such as protein-protein interactions, which have long resisted research efforts. This Review summarizes the basic principles and recent developments of the main high-throughput techniques for the discovery of macrocyclic peptides and focuses on their specific deployment for targeting undruggable space. A particular focus is placed on the development of new design guidelines and principles for the cyclization and structural stabilization of cyclic peptides and the resulting success stories achieved against well-known inaccessible drug targets.

Structure‐Based Design of Bicyclic Helical Peptides That Target the Oncogene β‐Catenin

AbstractThe inhibition of intracellular protein‐protein interactions is challenging, in particular, when involved interfaces lack pronounced cavities. The transcriptional co‐activator protein and oncogene β‐catenin is a prime example of such a challenging target. Despite extensive targeting efforts, available high‐affinity binders comprise only large molecular weight inhibitors. This hampers the further development of therapeutically useful compounds. Herein, we report the design of a considerably smaller peptidomimetic scaffold derived from the α‐helical β‐catenin‐binding motif of Axin. Sequence maturation and bicyclization provided a stitched peptide with an unprecedented crosslink architecture. The binding mode and site were confirmed by a crystal structure. Further derivatization yielded a β‐catenin inhibitor with single‐digit micromolar activity in a cell‐based assay. This study sheds light on how to design helix mimetics with reduced molecular weight thereby improving their biological activity.

Unlocking nature’s brilliance: using Antarctic extremophile Shewanella baltica to biosynthesize lanthanide-containing nanoparticles with optical up-conversion

Both lanthanide-containing and fluorine-containing nanomaterials present challenging targets for microbial biosynthesis because these elements are toxic to most bacteria. Here, we overcome these challenges by using an Antarctic Shewanella baltica strain that tolerates these elements and report the first biosynthesis of lanthanide-doped fluoride nanoparticles (NPs) from them. NaYF4 NPs doped with Er3+/Yb3+ are prototypical lanthanide-based upconverting nanoparticles (UCNPs) with upconverted luminescence at visible wavelengths under infrared excitation. However, their synthesis employs high precursor concentrations, organic solvents, and elevated temperatures. Microbial biosynthesis offers a greener alternative but has not been explored for these materials. Here, we harness an extremophile S. baltica strain to biosynthesize UCNPs at room temperature, based upon its high tolerance for fluoride and lanthanide ions and the observation that tolerance of lanthanides increased in the presence of fluoride. Our biosynthesis produces electron-dense nanostructures composed of Na, Y, F, Yb, and Er in the bacterial periplasm, adhered to the outer cell membrane, and dispersed extracellularly, which exhibited up-converted emission under 980 nm excitation. This suggests that extracellular or periplasmic mineralization of lanthanides as fluorides protects the bacteria from lanthanide toxicity. Subsequent heating both enhanced upconverted emission from UCNPs and allowed observation of their crystallinity in transmission electron microscopy (TEM). This work establishes the first biosynthesis of NaYF4:Yb: Er UCNPs, advancing both nanotechnology and biotechnology.Graphical

A Study of Pradhan Mantri Surya Ghar Yojna and Its Impact on Household Electricity Costs and Decreasing Dependency on Conventional Energy Sources

According to research, the Pradhan Mantri Surya Ghar Yojna has a significant impact on sustainable energy practices by lowering household electricity costs through rooftop solar installations and reducing India's dependency on foreign energy sources. The Yojna lessens the environmental effect of traditional energy usage by enabling homes to switch to greener options by promoting access to renewable energy. The program's subsidies and incentives increase access to solar energy, promoting its widespread adoption by both urban and rural families. In addition to lowering household power costs, its widespread adoption helps India meet its challenging targets for lowering carbon emissions and fostering environmental sustainability. Through the creation of chances for local manufacturers, skilled workers, and industries to engage in the solar energy value chain, the project also promotes socioeconomic development. The initiative supports India's larger sustainable energy aims by offering a framework for rooftop solar panel installations, guaranteeing long-term financial gains and promoting energy independence. There are difficulties, mostly associated with the initial high cost of installing solar panels and the difficulties in obtaining funding or subsidies, but they are gradually being resolved by means of incentives and policy changes from the government. Overall, by making solar energy more accessible, cheap, and appealing to Indian homes, the Pradhan Mantri Surya Ghar Yojna has played a significant role in defining sustainable energy habits and supporting the nation's wider economic and climatic goals.

Intense and Stable Blue Light Emission From CsPbBr3/Cs4PbBr6 Heterostructures Embedded in Transparent Nanoporous Films

AbstractLead halide perovskite nanocrystals are attractive for light emitting devices both as electroluminescent and color‐converting materials since they combine intense and narrow emissions with good charge injection and transport properties. However, while most perovskite nanocrystals shine at green and red wavelengths, the observation of intense and stable blue emission still remains a challenging target. In this work, a method is reported to attain intense and enduring blue emission (470–480 nm), with a photoluminescence quantum yield (PLQY) of 40%, originating from very small CsPbBr3 nanocrystals (diameter < 3 nm) formed by controllably exposing Cs4PbBr6 to humidity. This process is mediated by the void network of a mesoporous transparent scaffold in which the zero‐dimensional Cs4PbBr6 lattice is embedded, which allows the fine control over water adsorption and condensation that determines the optimization of the synthetic procedure and, eventually, the nanocrystal size. The approach provides a means to attain highly efficient transparent and stable blue light‐emitting films that complete the palette offered by perovskite nanocrystals for lighting and display applications.

A Systematic Review of Ship Wake Detection Methods in Satellite Imagery

The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field.

Influences of Variability in Attenuation Compensation on the Estimation of Backscatter Coefficient of Median Nerves in Vivo.

Peripheral nerves remain a challenging target for medical imaging, given their size, anatomical complexity, and structural heterogeneity. Quantitative ultrasound (QUS) applies a set of techniques to estimate tissue acoustic parameters independent of the imaging platform. Many useful medical and laboratory applications for QUS have been reported, but challenges remain for deployment in vivo, especially for heterogeneous tissues. Several phenomena introduce variability in attenuation estimates, which may influence the estimation of other QUS parameters. For example, estimating the backscatter coefficient (BSC) requires compensation for the attenuation of overlying tissues between the transducer and the underlying tissue of interest. The purpose of this study is to extend prior studies by investigating the efficacy of several analytical methods of estimating attenuation compensation on QUS outcomes in the human median nerve. Median nerves were imaged at the volar wrist in vivo and beam-formed radiofrequency (RF) data were acquired. Six analytical approaches for attenuation compensation were compared: 1-2) attenuation estimated by applying spectral difference method (SDM) and spectral log difference method (SLDM) independently to regions of interest (ROIs) overlying the nerve and to the nerve ROI itself; 3-4) attenuation estimation by applying SDM and SLDM to ROIs overlying the nerve, and transferring these properties to the nerve ROI; and 5-6) methods that apply previously published values of tissue attenuation to the measured thickness of each overlying tissue. Mean between-subject estimates of BSC-related outcomes as well as within-subject variability of these outcomes were compared among the 6 methods. Compensating for attenuation using SLDM and values from the literature reduced variability in BSC-based outcomes, compared to SDM. Variability in attenuation coefficients contributes substantially to variability in backscatter measurements. This work has implications for the application of QUS to in vivo diagnostic assessments in peripheral nerves and possibly other heterogeneous tissues.

Rhodium(III)-Catalyzed Atroposelective Indolization to Access Planar-Chiral Macrocycles.

Macrocycles incorporating conformationally defined indoles are widely found in bioactive natural products. However, the catalytic enantioselective synthesis of planar-chiral indoles via indolization involving macrocyclization remains elusive. Herein, we present the first rhodium(III)-catalyzed atroposelective macrocyclization, which involves the C-H activation of aniline, and a subsequent oxidation [3 + 2] annulation reaction with an intramolecular alkyne. This protocol achieves the construction of indoles, macrocyclization, and planar chirality control in a single step. Importantly, this strategy produces macrocyclic atropisomers bearing full-carbon ansa chains, which represent challenging targets in organic synthesis. Thermodynamic experiments revealed that the rotational barrier of the full-carbon ansa chain-linked macrocyclic atropisomer was lower than that of the atropisomer bearing an oxa-ansa chain. The reaction mechanism was elucidated by computational studies, which revealed that the C-H activation and intramolecular alkyne insertion steps collectively determined the enantioselectivity.

Physical layer security for confidential transmissions in frequency hopping-based downlink NOMA networks

Facing the exponential number of Internet of Things (IoT) devices and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets in terms of providing massive access and ensuring higher spectral efficiency. In this vein, Non-Orthogonal Multiple Access (NOMA) has been widely recognized as one of the advantageous techniques to handle the proliferation of the IoT. Nevertheless, from a security standpoint, enabling a user to decode the signals of the other users, while using Successive Interference Cancellation (SIC), raises serious concerns regarding confidentiality and vulnerability to malicious attacks. Meanwhile, conventional security paradigms, such as upper-layer encryption and sophisticated authentication mechanisms, require high computational complexity and additional processing, which impose an overwhelming burden on energy-efficient IoT devices. Alternatively, Physical layer Security (PLS) has sparked a significant interest as a promising complement to cryptographic techniques. The key idea of PLS is to avail wireless communication properties to secure communications without adding complex encryption mechanisms at higher layers. In this paper, we propose a PLS approach based on a network coding technique to prevent eavesdroppers from decoding users’ information transmitted through a downlink-based NOMA system. This results in correlating the packets to be transmitted with each other, making the interception of a single packet useless. We demonstrate that the eavesdropper’s decoding complexity increases exponentially with the sequence length, making the task intractable for relatively long ones.

Development of DuoMYC: a synthetic cell penetrant miniprotein that efficiently inhibits the oncogenic transcription factor MYC.

The master regulator transcription factor MYC is implicated in numerous human cancers, and its targeting is a long-standing challenge in drug development. MYC is a typical 'undruggable' target, with no binding pockets on its DNA binding domain and extensive intrinsically disordered regions. Rather than trying to target MYC directly with classical modalities, here we engineer synthetic miniproteins that can bind to MYC's target DNA, the enhancer box (E-Box), and potently inhibit MYC-driven transcription. We crafted the miniproteins via structure-based design and a combination of solid phase peptide synthesis and site-specific crosslinking. Our lead variant, DuoMYC, binds to E-Box DNA with high affinity (KD ~ 0.1 µM) and is able to enter cells and inhibit MYC-driven transcription with submicromolar potency (IC50 = 464 nM) as shown by reporter gene assay and confirmed by RNA sequencing. Notably, DuoMYC surpasses the efficacy of several other recently developed MYC inhibitors. Our results highlight the potential of engineered synthetic protein therapeutics for addressing challenging intracellular targets.

Development of DuoMYC: a synthetic cell penetrant miniprotein that efficiently inhibits the oncogenic transcription factor MYC

The master regulator transcription factor MYC is implicated in numerous human cancers, and its targeting is a long‐standing challenge in drug development. MYC is a typical ‘undruggable’ target, with no binding pockets on its DNA binding domain and extensive intrinsically disordered regions. Rather than trying to target MYC directly with classical modalities, here we engineer synthetic miniproteins that can bind to MYC’s target DNA, the enhancer box (E‐Box), and potently inhibit MYC‐driven transcription. We crafted the miniproteins via structure‐based design and a combination of solid phase peptide synthesis and site‐specific crosslinking. Our lead variant, DuoMYC, binds to E‐Box DNA with high affinity (KD ~ 0.1 µM) and is able to enter cells and inhibit MYC‐driven transcription with submicromolar potency (IC50 = 464 nM) as shown by reporter gene assay and confirmed by RNA sequencing. Notably, DuoMYC surpasses the efficacy of several other recently developed MYC inhibitors. Our results highlight the potential of engineered synthetic protein therapeutics for addressing challenging intracellular targets.

Scaling laws for nanoparticles – Online shape heterogeneity analysis by size exclusion chromatography coupled with multi-angle light scattering

Nanoparticle-based drug delivery systems are rising technologies to access challenging therapeutic targets. Following commercial success of lipid-based nanoparticles (LBNP), accruing understandings of nanoparticle structures and critical quality attributes through advanced analytics are beneficial to future clinical development and generalization of this delivery platform. The morphological attributes of nanoparticles, such as shape, can affect uptake, cell-interaction, drug release, circulation, and flow. Gaining an understanding of these structure-activity relationships in early-stage formulation development is important because mix morphologies can affect quality and potency but often exist before process control strategies are fully implemented. In this study, we used shape heterogeneous nanoparticle mixtures, containing various populations of liposomes and lipodisks, as a model system and developed an online semi-quantitative method to characterize the nanoparticle shape heterogeneity by size exclusion chromatography (SEC) coupled with multi-angle light scattering (MALS). The liposomes and lipodisks were separated in SEC when their sizes were ∼3 fold different. When the particles of different shapes were in similar sizes, size-based separation was not always feasible. Instead, light scattering data distinguished liposomes and lipodisks by the scaling law linking radius of gyration and molecular weight of the nanoparticles, enabling morphological identification. A semi-quantitative model was built based on the exponential correlation between the scaling law exponents and the ratios of liposomes and lipodisks. The model was applied to test 6 random formulations made with different compositions and manufacturing processes, and the predicted liposome percentage for 5 formulations was within 25 % absolute difference from the percentage determined by cryogenic electron microscopy (cryo-EM). We envision this method being routinely used to characterize liposome and lipodisk shape heterogeneity during formulation screening as well as on stability studies. Potentially, the method can be converted to in-process control method and extended to other categories of nanoparticles beyond liposomes.

How Cryo-EM Revolutionized the Field of Bioenergetics

Abstract Ten years ago, the term “resolution revolution” was used for the first time to describe how cryogenic electron microscopy (cryo-EM) marked the beginning of a new era in the field of structural biology, enabling the investigation of previously unsolvable protein targets. The success of cryo-EM was recognized with the 2017 Chemistry Nobel Prize and has become a widely used method for the structural characterization of biological macromolecules, quickly catching up to x-ray crystallography. Bioenergetics is the division of biochemistry that studies the mechanisms of energy conversion in living organisms, strongly focused on the molecular machines (enzymes) that carry out these processes in cells. As bioenergetic enzymes can be arranged in complexes characterized by conformational heterogeneity/flexibility, they represent challenging targets for structural investigation by crystallography. Over the last decade, cryo-EM has therefore become a powerful tool to investigate the structure and function of bioenergetic complexes; here, we provide an overview of the main achievements enabled by the technique. We first summarize the features of cryo-EM and compare them to x-ray crystallography, and then, we present the exciting discoveries brought about by cryo-EM, particularly but not exclusively focusing on the oxidative phosphorylation system, which is a crucial energy-converting mechanism in humans.

A High-Rate and Ultrastable Re2Te5/MXene Anode for Potassium Storage Enabled by Amorphous/Crystalline Heterointerface Engineering.

The pursuit of anode materials capable of rapid and reversible potassium storage performance is a challenging yet fascinating target. Herein, a heterointerface engineering strategy is proposed to prepare a novel superstructure composed of amorphous/crystalline Re2Te5 anchored on MXene substrate (A/C-Re2Te5/MXene) as an advanced anode for potassium-ion batteries (KIBs). The A/C-Re2Te5/MXene anode exhibits outstanding reversible capacity (350.4 mAh g-1 after 200 cycles at 0.2 A g-1), excellent rate capability (162.5 mAh g-1 at 20 A g-1), remarkable long-term cycling capability (186.1 mAh g-1 at 5 A g-1 over 5000 cycles), and reliable operation in flexible full KIBs, outperforming state-of-the-art metal chalcogenides-based devices. Experimental and theoretical investigations attribute this high performance to the synergistic effect of the A/C-Re2Te5 with a built-in electric field and the elastic MXene, enabling improved pseudocapacitive contribution, accelerated charge transfer behavior, and high K+ ion adsorption/diffusion ability. Meanwhile, a combination of intercalation and conversion reactions mechanism is observed within A/C-Re2Te5/MXene. This work offers a new approach for developing metal tellurides- and MXene-based anodes for achieving stable cyclability and fast-charging KIBs.

The Missing Link(er): A Roadmap to Macrocyclization in Drug Discovery.

Macrocycles are one of nature's preferred choices to generate large but cell-permeable bioactive molecules. Macrocyclization is increasingly prominent in medicinal chemistry beyond natural products, especially for difficult-to-drug targets. However, strategies to best exploit the potential of macrocycles are only beginning to emerge. Here we survey drug discovery campaigns from the past decade that cumulated in advanced macrocyclic drug-like compounds or drug candidates. Most macrocycles were conceived by ring closing based on U- or C-shaped bioactive conformations observed in co-crystal structures. We focus on the key step from linear precursors to the first macrocycle and the follow-up optimization of the resulting macrocyclic scaffold. Conformational control recurrently emerged as a key factor for macrocycle properties and linkers as an opportunity for optimization. With increasingly challenging drug targets, we expect these trends to become more prominent and relevant.

Triscysteine disulfide-directing motifs enabling design and discovery of multicyclic peptide binders

Peptides are valuable for therapeutic development, with multicyclic peptides showing promise in mimicking antigen-binding potency of antibodies. However, our capability to engineer multicyclic peptide scaffolds, particularly for the construction of large combinatorial libraries, is still limited. Here, we study the interplay of disulfide pairing between three biscysteine motifs, and designed a range of triscysteine motifs with unique disulfide-directing capability for regulating the oxidative folding of multicyclic peptides. We demonstrate that incorporating these motifs into random sequences allows the design of disulfide-directed multicyclic peptide (DDMP) libraries with up to four disulfide bonds, which have been applied for the successful discovery of peptide binders with nanomolar affinity to several challenging targets. This study encourages the use of more diverse disulfide-directing motifs for creating multicyclic peptide libraries and opens an avenue for discovering functional peptides in sequence and structural space beyond existing peptide scaffolds, potentially advancing the field of peptide drug discovery.

Novel physics-informed optimization framework for complex multi-physics problems: Implementation for a sweeping gas membrane distillation module

The application of advanced machine learning algorithms such as deep learning is limited for the surrogate-based process optimization of complex multi-physics problems because high-quality experimental and numerical training samples required for deep-learning model training are expensive and scarce. To address this issue, in this study, a novel physics-informed process optimization (PIPO) framework was introduced. In the first step, surrogate models based on conventional neural networks (NN) were trained using a few available high-quality training samples. In the second step, the trained NN models were coupled to the process optimization solver in which the loss of physical laws was added to the optimizer’s objective function to find optimal design points that satisfy the laws of physics. As a result, the generalization performance of the framework was greatly improved for design targets outside the training range of NN models. PIPO is substantially different from the physics-informed neural networks where the loss of physics is added to the loss function used during NN model training. The PIPO framework was used to optimize a sweeping gas membrane distillation (SGMD) module. Eight input design variables, including process and geometrical parameters, were optimized for different challenging targets to achieve the best SGMD performance in terms of ammonia recovery ratio and concentration. It was shown that for noticeably few training samples of 68 experiments, the proposed framework was able to achieve the optimization targets within a reasonable computational cost. The optimum designs were verified and analyzed in detail by high-resolution computational fluid dynamics models.

Effects of NOx and NH3 on the secondary organic aerosol formation from α-pinene photooxidation

Understanding the effects of mixed anthropogenic pollutants on the photooxidation of volatile organic compounds (VOCs) is essential for unraveling the formation pathways of secondary organic aerosols (SOA). Yet, it remains a highly challenging experimental target owing to the complexities in the precise measurement of molecular compositions of products and number/mass concentrations of particles as a function of pollutant concentration in the ambient atmosphere. Here, a series of well-defined chamber experiments were performed to explore the effects of NOx and NH3 on the SOA formation from photooxidation of the most abundant monoterpene, α-pinene. The results indicate that the suppression effect of NO and NO2 on the α-pinene photooxidation shows monotonous and parabolic trends, respectively. The presence of NH3 enhances particle number concentrations during the α-pinene + NOx photooxidation by participating in reactions with organic acids. New compounds, including organic peroxides, esters, organic nitrates, and peroxyacyl nitrates, are observed at molecular weight (MW) = 166, 173, 217, 231, 280, 282, 304, and 410 through threshold photoionization making use of a recently constructed vacuum ultraviolet free electron laser in positive ion mode. The molecular structures and formation paths of these species are speculated, which advance the category of VOC oxidation products. Our study provides significant understanding of the influence of NOx and NH3 on the VOC photooxidation, which can be utilized to establish predictive SOA formation networks and to improve atmospheric models.

Structure-Based Design of Bicyclic Helical Peptides That Target the Oncogene β-Catenin.

The inhibition of intracellular protein-protein interactions is challenging, in particular, when involved interfaces lack pronounced cavities. The transcriptional co-activator protein and oncogene β‑catenin is a prime example of such a challenging target. Despite extensive targeting efforts, available high-affinity binders comprise only large molecular weight Inhibitors. This hampers the further development of therapeutically useful compounds.Herein, we report the design of a considerably smaller peptidomimetic scaffold derived from the α-helical β‑catenin-binding motif of Axin. Sequence maturation and bicyclization provided a stitched peptide with an unprecedented crosslink architecture. The binding mode and site were confirmed by a crystal structure. Further derivatization yielded a β-catenin inhibitor with single-digit micromolar activity in a cell-based assay. This study sheds a light on how to design helix mimetics with reduced molecular weight thereby improving their biological activity.