bluuebunny/crossref_metadata_2025 huggingface dataset:
Dual stimuli-responsive dynamic covalent peptide tags: Towards sequence-controlled release in tumor-like microenvironments
Authors: Zegota Maksymilian M. , Müller Michael Andreas , Lantzberg Bellinda , Kizilsavas Gönül , Coelho Jaime A. S. , Moscariello Pierpaolo , Martínez-Negro María , Morsbach Svenja , Gois Pedro M. P. , Wagner Manfred , Ng David Y. W. , Kuan Seah Ling , Weil Tanja
Published: February 2022

Dynamic covalent chemistry has emerged as a versatile synthetic tool for devising stable, stimuli-responsive bioconjugates. Nevertheless, dynamic covalent interactions often exhibit fast binding and dissociation events or vice versa, affecting their conversion rates or stabilities. To overcome this, we designed dual responsive peptide tags combining: (1) a pH responsive boronate ester with fast association and dissociation rates, and (2) a redox-active disulfide with slow formation and dissociation rate. Pre-coordination by boronic acid–catechol interaction improves self-sorting and selectivity in disulfide formation into heterodimers. The resulting bis-peptide conjugate exhibited improved complex stability in aqueous solution and acidic tumor-like extracellular microenvironment. The conjugate responds to pH changes and to a redox environment that is similar to certain conditions inside cancer cells. Such tags hold great promise for controlling the stability of bioconjugates under dilution in aqueous media, as well as designing intelligent pharmaceutics that react to distinct biological stimuli in cells.
Titanium carbide MXene shows an electrochemical anomaly in water-in-salt electrolytes
Authors: Wang Xuehang , Mathis Tyler S. , Sun Yangyunli , Tsai Wan-Yu , Shpigel Netanel , Shao Hui , Zhang Danzhen , Hantanasirisakul Kanit , Malchick Fyodor , Balke Nina , Jiang De-en , Simon Patrice , Gogotsi Yury
Published: February 2022

Identifying and understanding charge storage mechanisms is important for advancing energy storage, especially when new materials and electrolytes are explored. Well-separated peaks in cyclic voltammograms (CVs) are considered key indicators of diffusion-controlled electrochemical processes with distinct Faradic charge transfer. Herein, we report on an electrochemical system with separated CV peaks, accompanied by surface-controlled partial charge transfer, in 2D Ti3C2Tx MXene in water-in-salt electrolytes. The process involves the insertion/desertion of desolvation-free cations, leading to an abrupt change of the interlayer spacing between MXene sheets. This unusual behavior increases charge storage at positive potentials, thereby increasing the amount of energy stored. This also demonstrates new opportunities for the development of high-rate aqueous energy storage devices and electrochemical actuators using safe and inexpensive aqueous electrolytes.
Facile Formation of Giant Elastin-like Polypeptide Vesicles as Synthetic Cells
Authors: Sharma Bineet , Ma Yutao , Hiraki Harrison , Baker Brendon , Ferguson Andrew , Liu Allen
Published: February 2022

vesicles that are more robust and stable were developed as a new chassis material for synthetic cells. Here we demonstrate the facile and robust generation of giant peptide vesicles made of elastin-like polypeptides (ELPs) by using an emulsion transfer method. We show the robust nature of the peptide vesicles by challenging them with chemical (Triton-X 100) and physical (osmotic) stresses. We find that these peptide vesicles can stably encapsulate biomolecules and can host cell-free expression reactions. Lastly, we show incorporation of another cell-free expressed amphiphilic ELP into existing peptide vesicles’ membrane, demonstrating a potential approach to grow ELP vesicles. Since ELPs are genetically encoded, the approaches presented here provide exciting opportunities to engineer synthetic cell membranes.
Photogearing – A Concept for Translation of Precise Motions at the Nanoscale
Authors: Gerwien Aaron , Gnannt Frederik , Mayer Peter , Dube Henry
Published: February 2022

Molecular gearing systems allow the integration of multiple motions in a correlated fashion, to translate motions from one locality to another and to change their speed and direction. However, currently no powerful concepts exist to implement active driving of gearing motions at the molecular scale. Herein, we present a light-fueled molecular gearing system and evidence its superiority over passive thermally activated gearing. Translation of a 180° rotation into a 120° rotation is achieved while at the same time the direction of the rotation axis is shifted by 120°. Within such photogearing process, precise motions at the nanoscale can be shifted in direction and decelerated similar to macroscopic bevel-gear operations in an energy consuming way – a necessary prerequisite to employ gearing as an active component in future integrated nanomachinery.
The Spectral Fingerprint of Excited-State Energy Transfer in Dendrimers through Polarization-Sensitive Transient-Absorption Pump-Probe Signals: On-the-Fly Nonadiabatic Dynamics Simulations
Authors: Hu Deping , Peng Jiawei , Chen Lipeng , Gelin Maxim , Lan Zhenggang
Published: February 2022

The time-resolved polarization-sensitive transient-absorption (TA) pump-probe (PP) spectra are simulated using on-the-fly surface-hopping nonadiabatic dynamics and the doorway-window (DW) representation of nonlinear spectroscopy. A typical dendrimer model system composed of two linear phenylene ethynylene units (2-ring, 3-ring) is taken as an example. The fewest switches trajectory surface hopping algorithm along with the TDDFT method is adopted in the nonadiabatic dynamics simulations. The ground-state bleach (GSB), stimulated emission (SE), excited-state absorption (ESA) contributions as well as the total TA PP signals are obtained and carefully analyzed. The correlations between these signals and the coupled nuclear-electronic dynamics are established. It is shown that intramolecular excited-state energy transfer from the 2-ring unit to the 3-ring unit can be conveniently monitored and accurately identified by employing pump and probe pulses with different polarizations. Our on-the-fly nonadiabatic simulation results demonstrate that time-resolved polarization-sensitive TA PP signals provide a powerful tool for the elucidation of excited-state energy transfer pathways, notably in molecular systems possessing several optically-bright nonadiabatically-coupled electronic states with different orientations of transition dipole moments.
Comparison of Material Activity and Selectivity in the Electrocatalytic Oxidation of Dibenzothiophene
Authors: Swierk John , Kompanijec Victoria
Published: February 2022

Due to adverse effects of sulfur-containing compounds present in fuel oils, there is an increasing demand for an efficient and cost-effective method of removing sulfur from oil products, such as oxidative desulfurization. In this work, a set of five materials (gold, glassy carbon, nickel, palladium and platinum) were evaluated as electrochemical catalysts for the oxidation of DBT. Electrolysis at 1.58 V was performed without water present (producing a dimer of DBT) and with the addition of 2 M water (producing DBTO). LC-MS and NMR were used to characterize the oxidation products. It was found that the Faradaic efficiencies ranged from 18.4 – 56.5% for consumption of DBT without water present and there was a correlation between higher rate constants, lower activation energies and more efficient DBT oxidation. After the addition of water, the formation of DBTO was found to have the highest selectivity when catalyzed by gold, with a Faradaic efficiency of 87.9%. The group ten metals demonstrated low Faradaic efficiencies due to the competitive water oxidation taking place. Though there were differences in the observed selectivity for DBT oxidation, all catalysts reduced the concentration of DBT in solution by similar amounts. Of the materials tested, gold served as the most selective for oxidation to DBTO, with the presence of water improving the overall reaction activity.
RetroTRAE: retrosynthetic translation of atomic environments with Transformer
Authors: Ucak Umit Volkan , Ashyrmamatov Islambek , Ko Junsu , Lee Juyong
Published: February 2022

Herein we present a new retrosynthesis prediction method, viz. RetroTRAE, which uses fragment-based tokenization combined with the Transformer architecture. RetroTRAE mimics chemical reasoning, and predicts reactant candidates by learning the changes of atomic environments associated with the chemical reaction. Atom environments stand as ideal, chemically meaningful building blocks, which together produce a high-resolution molecular representation. Describing a molecule with a set of atom environments establishes a clear relationship between translated product-reactant pairs due to the conservation of atoms in the reactions. Our model achieved a top-1 accuracy of 68.1% within the bioactively similar range for the USPTO test dataset, outperforming other state-of-the-art translation methods. Besides yielding a high level of overall accuracy, the proposed method solves the translation issues arising from the SMILES-based retrosynthesis planning methods effectively. Through careful inspection of reactant candidates, we demonstrated atom environments as promising descriptors for studying reaction route prediction and discovery. RetroTRAE provides fast and reliable retrosynthetic route planning for substances whose fragmentation patterns are revealed. Our methodology offers a novel way of devising a retrosynthetic planning model using fragmental and topological descriptors as natural inputs for chemical translation tasks.
RetroTRAE: retrosynthetic translation of atomic environments with Transformer
Authors: Ucak Umit Volkan , Ashyrmamatov Islambek , Ko Junsu , Lee Juyong
Published: February 2022

We present a new single-step retrosynthesis prediction method, viz. RetroTRAE, using fragment-based tokenization and the Transformer architecture. RetroTRAE mimics chemical reasoning, and predicts reactant candidates by learning the changes of atom environments (AEs) associated with the chemical reaction. AEs are the ideal stand-alone chemically meaningful building blocks providing a high-resolution molecular representation. Describing a molecule with a set of AEs establishes a clear relationship between translated product-reactant pairs due to the conservation of atoms in the reactions. Our model achieved a top-1 accuracy of 58.3% on the USPTO test dataset. When highly similar analogs were considered the accuracy increased to 61.6%. These results outperform other state-of-the-art neural machine translation-based methods. Besides yielding a high level of overall accuracy, the proposed method does not suffer from the SMILES-based translation issues such as invalid SMILES. Additionally, the attention matrices of RetroTRAE are shown to capture chemical changes around reaction sites successfully. Through careful inspection of reactant candidates, we demonstrated that AEs are promising descriptors for studying reaction route prediction and discovery, which has been underexplored yet. Our methodology offers a novel way of devising a retrosynthetic planning model using fragmental and topological descriptors as natural inputs for chemical translation tasks, and opens new possibilities for developing other sequence-based machine-learning methods in chemistry.
Using machine learning to estimate wildfire PM2.5 at California ZIP codes (2006-2020)
Authors: Aguilera Rosana , Luo Nana , Basu Rupa , Wu Jun , Gershunov Alexander , Benmarhnia Tarik
Published: February 2022

Epidemiological studies on the detrimental health impacts of exposure to fine particulate matter (PM2.5) from different sources of emission can inform regulatory policy and identify vulnerable communities. Though PM2.5 has decreased in the U.S. in the two past decades, the increasing frequency and severity of wildfires contribute to episodically impair air quality in wildfire-prone regions and beyond. Monitoring air quality extensively is challenging. Since government-operated monitors are sparsely located across California and the U.S., several regions and populations remain unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on gathering large amounts of data, such as satellite-derived aerosol properties and meteorological variables. and direct use of low-cost air sensor measurements that may be associated with substantial uncertainty Furthermore, modelling wildfire-specific PM2.5 is often based on chemical transport model predictions, which results in highly computationally intensive efforts. Our study used an ensemble model that integrated multiple machine learning algorithms and a large set of predictor variables to estimate daily PM2.5 at the ZIP code level, a relevant spatio-temporal resolution for epidemiological and public health studies. Our models achieved comparable results to previous machine learning studies for PM2.5 prediction, but avoided processing larger, computationally intensive datasets. In addition, we use machine learning to estimate the wildfire-specific PM2.5 concentrations through a novel multiple imputation approach.
DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers
Authors: Rajan Kohulan , Zielesny Achim , Steinbeck Christoph
Published: February 2022

The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, optical chemical structure recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.
Intramolecular Charge Transfer in the Azathioprine Prodrug Quenches Intersystem Crossing to the Reactive Triplet State in 6-Mercaptopurine
Authors: Ortiz-Rodríguez Luis A. , Ortiz-Zayas Glesmarie , Pollum Marvin , Hoehn Sean J. , Jockusch Steffen , Crespo-Hernández Carlos
Published: February 2022

The thiopurine prodrugs 6-mercaptopurine and azathioprine are among the world’s essential medications for acute lymphoblastic leukemia, immunosuppression, and several autoimmune conditions. Thiopurine prodrugs are efficient UVA absorbers and singlet oxygen generators and the long-term treatment with these prodrugs correlates with a high incidence of sunlight-induced skin cancer in patients. In this contribution, we show that the electronic relaxation mechanisms and photochemical properties of azathioprine are remarkably different from those of 6-mercaptopurine upon absorption of UVA radiation. UVA excitation of 6-mercaptopurine results in nearly 100% triplet yield and up to 30% singlet oxygen generation, whereas excitation of azathioprine with UVA leads to triplet yields of 15 to 3% depending on pH of the aqueous solution and less than 1% singlet oxygen generation. While photoexcitation of 6-mercaptopurine and other thiopurine prodrugs can facilitate oxidatively generated cell damage, azathioprine’s poor photosensitization ability reveals the use of interchromophoric charge transfer interactions for the molecular design of photostable prodrugs exhibiting a remarkable reduction in photocytotoxic side effects before drug metabolization.
Zero-field J-Spectroscopy of Urea: Spin-Topology Engineering by Chemical-Exchange
Authors: Alcicek Seyma , Put Piotr , Barskiy Danila , Kontul Vladimir , Pustelny Szymon
Published: February 2022

Well-resolved and information-rich J-spectra are the foundation for chemical analysis based on zero-field NMR. Yet, even in relatively small molecules, the spectra may gain complexity, hindering the analysis. To address this problem, we investigate an example biomolecule characterized with a complex J-coupling network -- urea, a key metabolite in protein catabolism -- and demonstrate ways of simplifying its zero-field spectra by modifying spin topology. This goal is achieved by controlling pH-dependent chemical-exchange rates of 1H nuclei and varying the composition of the D2O/H2O mixture used as a solvent. Specifically, we demonstrate that by increasing hydrogen chemical-exchange rate in [13C, 15N2]-urea solution, the molecule, being an effective spin system XAB2A'B'2, behaves as a much simpler XA2 system (where X = 13C, A = 15N, B = 1H), manifesting through a single narrow spectral peak. Additionally, we show that introducing spin-1 nuclei into the molecule and investigating J-spectra of 1H/D isotopologues of [15N2]-urea allows to study various isolated spin subsystems: XA2, (XA)B, and XB2 (here X = 15N, A = 1H, B = D), again greatly simplifying spectra analysis. The influence of the chemical exchange process on zero-field $J$-spectra for each urea solution is elucidated by theoretical studies, demonstrating solid agreement between results and simulations. This study shows the applicability of zero-field NMR to detect complex biomolecules in aqueous solutions, and it opens the means for future in vivo/in vitro biochemical investigations, particularly in biofluids with a high concentration of water.
Sanitize It Yourself: human-based sanitization checker against machine-generated chemical structures
Authors: Yoshikawa Naruki , Rikimaru Kentaro , Yamamoto Kazuki
Published: February 2022

Many computer-aided drug design (CADD) methods using deep learning have recently been proposed to explore the chemical space toward novel scaffolds efficiently. However, there is a tradeoff between the ease of generating novel structures and the chemical feasibility of structural formulas. To overcome the limitations of computational filtering, we have implemented a web application that allows easy compound sanitization by humans. The application is available at https://sanitizer.chemical.space/.
Epoxidation and late-stage C-H functionalization by P450 TamI is mediated by variant heme-iron oxidizing species
Authors: Espinoza Rosa V. , Maskeri Mark A. , Turlik Aneta , Nangia Anjanay , Khatri Yogan , Montgomery John , Houk Ken N. , Sherman David H.
Published: February 2022

P450-catalyzed hydroxylation reactions are well understood mechanistically including the identity of the active oxidizing species. However, the catalytically active heme-iron species in P450 iterative oxidation cascades that involve mechanistically divergent pathways and distinct carbon atoms within a common substrate remains unexplored. Recently, we reported the enzymatic synthesis of tri-functionalized tirandamycin O (9) and O’ (10) using a bacterial P450 TamI variant and developed mechanistic hypotheses to explore their formation. Here, we report the ability of bacterial P450 TamI L295A to shift between different oxidizing species as it catalyzes the sequential epoxidation, hydroxylation and radical-catalyzed epoxide-opening cascade to create new tirandamycin antibiotics. We also provide evidence that the TamI peroxo-iron species could be a viable catalyst to enable nucleophilic epoxide opening in the absence of iron-oxo Compound I. Using site-directed mutagenesis, kinetic solvent isotope effects, artificial oxygen surrogates, end-point assays, and density functional theory (DFT) calculations, we provide new insights into the active oxidant species that P450 TamI employs to introduce its unique pattern of oxidative decorations.
Influence of the first coordination of uranyl on its luminescence properties: study of uranyl binitrate with N,N-dialkyl amide DEHiBA and water
Authors: Oher Hanna , Ferru Geoffroy , Couston Laurent , Berthon Laurence , Guillaumont Dominique , Réal Florent , Vercouter Thomas , Vallet Valérie
Published: February 2022

Uranyl binitrate complexes have a particular interest in the nuclear industry, especially in the reprocessing of spent nuclear fuel. The modified PUREX extraction process is designed to extract U(VI) in the form of UO2(NO3)2(L)2 as it has been confirmed by EXAFS, XRD and TRLFS measurements. In this study, the L ligands are two molecules of N,N-di-(ethyl- 2-hexyl)isobutyramide (DEHiBA) monoamide used to bind uranyl in its first coordination sphere. DEHiBA ligands can coordinate uranyl in either trans- or cis-positions with respect to the nitrate ligands and these two conformers may co-exist in solution. In order to use luminescence spectroscopy as a speciation technique, it is important to determine whether or not these conformers can be discriminated by their spectroscopic properties. To answer this question, the spectra of trans- and cis-UO2(NO3)2(DEiBA)2 conformers were modeled with ab initio methods and compared to the experimental time-resolved luminescence spectra on the UO2(NO3)2(DEHiBA)2 systems. Moreover, the hydrated uranyl binitrate UO2(NO3)2(H2O)2 complexes in the same trans and cis configurations were modeled to quantify the impact of organic DEHiBA on the luminescence properties.
Communication: Multi-reference Approach to the Computation of Double-Core-Hole Spectra
Authors: Tenorio Bruno Nunes Cabral , Decleva Piero , Coriani Sonia
Published: February 2022

Double-Core Hole (DCH) states of small molecules are assessed with the restricted

active space self-consistent field (RASSCF) and multi-state restricted active space perturbation

theory of second order (MS-RASPT2) approximations. To ensure an unbiased

description of the relaxation and correlation effects on the DCH states, the neutral

ground state and DCH wave functions are optimized separately, whereas the spectral

intensities are computed with a biorthonormalized set of molecular orbitals within the

state-interaction (SI) approximation. Accurate shake-up satellites binding energies and

intensities of double-core-ionized states (K-2) are obtained for H2O, N2, CO and C2H2n

(n=1–3). The results are analyzed in detail and show excellent agreement with recent theoretical and experimental data. The K^{-2} shake-up spectra of H2O and the C2H2n molecules are here completely characterized for the first time.
Correlating topography and elastic properties of Elastin-Like Polypeptide scaffolds probed at the nanoscale: Intermodulation Atomic Force Microscopy experiments and Molecular Dynamic simulations
Authors: Trusso Sebastiano , Firman Samantha , Balasubramanian Janani , Khatami Mohammad Hassan , deHaan Hendrick , Agarwal Nisha Rani
Published: February 2022

The synthesis and property characterization of soft biomaterials has taken precedence in recent years. Although bulk physical-chemical properties are well known for these bio-materials, nanoscale properties still need to be probed and evaluated to fine tune the bio-compatibility (structural as well as functional) with natural tissues for regenerative medicine, prosthetics and other biological applications. In this study, we focus on a popular soft biomaterial, ELastin-like polypeptide (ELP) which has been prepared under different pH conditions. We explore the topographical features of the ELP at the nanoscale using Atomic Force Microscopy (AFM). Additionally, we employ a non linear mode of AFM called Intermodulation-AFM (ImAFM) to correlate the elastic properties (Young's modulus) of ELP probed at the nanoscale with the topographical features which gives us a deep insight into the mechanical properties offered by ELP when the structural features are altered by change in the ELP synthesis conditions. The noteworthy point is that we measure theses properties at a spatial resolution of 0.9 nm. Finally, we explain the change in the structural features of ELP with varying pH through atomistic Molecular Dynamics Simulations. We follow the interaction mechanisms of the amino acid sequences and crosslinkers with proteins as they form the backbone and sidechain of the ELP at different pH.
Spiro-based thermally activated delayed fluorescence emitters with reduced non-radiative decay for high quantum efficiency, low roll-off, light-emitting diodes
Authors: Sharma Nidhi , Maciejczyk Michal , Hall David , Li Wenbo , Liegeois Vincent , Beljonne David , Olivier Yoann , Robertson Neil , Samuel Ifor , Zysman-Colman Eli
Published: February 2022

Herein, we report the use of spiro-configured fluorene-xanthene scaffolds as a novel, promising and effective strategy in thermally activated delayed fluorescence (TADF) emitter design to attain high photoluminescence quantum yields (PL), short delayed luminescence lifetime, high external quantum efficiency (EQE) and minimum efficiency roll-off characteristics in organic light-emitting diodes (OLEDs). The optoelectronic and electroluminescence properties of SFX-, spiro-(fluorene-9,9’-xanthene), based emitters (SFX-PO-DPA, SFX-PO-DPA-Me and SFX-PO-DPA-OMe) were investigated both theoretically and experimentally. All three emitters exhibited sky blue to green emission enabled by a Herzberg-Teller mechanism in the excited state. They possess short excited state delayed lifetimes (<10 s), high photoluminescence quantum yields (PL ~70%) and small singlet-triplet splitting energies (EST < 0.10 eV) in the doped films in an mCP host matrix. The OLEDs showed some of the highest EQEs using spiro-containing emitters where maximum external quantum efficiencies (EQEmax) of 11% and 16% were obtained for devices using SFX-PO-DPA and SFX-PO-DPA-OMe, respectively. Further, a record EQEmax of 23% for a spiro-based emitter coupled with a small efficiency roll off (19% at 100 cd m-2) was attained with SFX-PO-DPA-Me.
Ultrafast reclamation of fracking effluents using surface-engineered nanosilicon sponges
Authors: Cherukupally Pavani , Sun Wei , Williams Daryl R , Ozin Geoffrey A , Bilton Amy M
Published: February 2022

Effluents from the fracking process are typically discharged at elevated temperatures and are a major environmental concern. We applied a surface-engineered sponge (SEnS) with thermal stability up to 220 ºC to reclaim emulsified oily wastewater at discharge temperatures between 30-100 ºC. The sponge achieved 92-96% removal efficiency within 5 minutes, where speed increased by 27% by melting waxes. The adsorbed oil from the SEnS was also recovered within 1-2 minutes by diluent wash. These performance metrics suggest that SEnS could emerge as a practical solution to achieve fracking water reclamation processes’ Net-Zero goals.
COVID-19: Invades Erythrocytes through Plasmodium Falciparum Antigen and Complement-Like System
Authors: liu wenzhong , li hualan
Published: February 2022

Malaria symptoms are very similar to those of COVID-19, and infections can be symptomatic or asymptomatic. Common immunodominant epitopes are shared by the SARS-CoV-2 proteins and the Plasmodium falciparum antigen. Through bioinformatics methods such as domain search, this study discovered that the S, ORF3a proteins contained Plasmodium antigens rich in tryptophan and threonine. ORF3a, ORF8, S, and N and others also had more extended autotransporter domains. The Plasmodium antigen of S protein contained a C1q domain capable of binding to the complement receptor 1 on the red blood cell membrane. ORF3a contained the Plasmodium antigen EBA-175 domain, which was capable of binding to glycophorin A on the red blood cell membrane. S and ORF3a were bound to band 4.1 to anchor on the erythrocyte membrane skeleton, respectively. The Membrane attack complex component of the S protein formed fusion pores on the red blood cell membrane. Then it injected viral genetic material into the mature red blood cell. ORF3a used a thiol-activated cytolysin domain to create hemolytic pores in the red blood cell membrane. The coagulation factor calcium ions were involved in the red blood cell invasion process. The invasion would have no discernible hemolysis or hypoxia reactions. According to the Plasmodium antigen type for SARS-COV-2, the blood cells of people with blood types A and Knops were susceptible to attack by SARS-COV-2 virus proteins.
A Favorable Path to Domain Separation in the Orange Carotenoid Protein
Authors: Sharawy Mahmoud , Pigni Natalia B. , May Eric R. , Gascón José A.
Published: February 2022

The Orange Carotenoid Protein (OCP) is responsible for nonphotochemical quenching (NPQ) in cyanobacteria, a defense mechanism against potentially damaging effects of excess light conditions. Domain separation is a critical step in the photocycle of OCP because it exposes the N-terminal domain to perform quenching of the phycobilisomes. Details regarding the mechanism and energetics of OCP domain separation remain unknown. In this work, we apply metadynamics to elucidate the protein rearrangements that lead to the active, domain-separated form of OCP. We find that translocation of the ketocarotenoid canthaxanthin has a profound effect on the energetic landscape and that domain separation only becomes favorable following translocation. Through pathway optimization methods, we characterize the most probable pathway to domain separation and reveal the barriers along that pathway. We find that the free energy barriers are relatively small (<5 kcal/mol), but the overall estimated kinetic rate is consistent with experimental measurements (>1 ms).
Modular Type III Porous Liquids based on Porous Organic Cage Microparticles
Authors: Kai Aiting , Egleston Benjamin D. , Clowes Rob , Briggs Michael E. , Cooper Andrew I. , Greenaway Rebecca L.
Published: February 2022

The dispersion of particulate porous solids in size-excluded liquids has emerged as a method to create Type III porous liquids, mostly using insoluble microporous materials such as metal-organic frameworks (MOFs) and zeolites. Here, we present the first examples of Type III porous liquids based on porous organic cages (POCs). By exploiting the solution processability of the POCs, racemic and quasiracemic cage microparticles were formed by chiral recognition. Dispersion of these porous microparticles in a range of size-excluded liquids, including oils and ionic liquids, formed stable POC-based Type III porous liquids. The flexible pairing between the solid POC particles and a carrier liquid allows the formation of a range of compositions, pore sizes, and other physicochemical properties to suit different applications and operating conditions.
Metasurfaces as energy valves for sustainable energy management
Authors: Nishijima Yoshiaki , Kimura Shunya , Takeshima Yu , Jodkazis Saulius
Published: February 2022

Control of light absorption and transmission by metal-insulator-metal (MIM) metasurfaces are important for applications in optical windows. In this study we show realization of photo-thermal energy conversion for the radiative cooling. By controlling absorption and transparency of MIM metasurfaces with thin metal substrate and Indium-Tin-Oxide (ITO) substrates, a high transparency at visible wavelengths and high absorption at mid-infrared wavelengths was realized for a prospective application in efficient cooling or heating of the room.
Selective Manganese-Catalyzed Semihydrogenation of Alkynes with in-situ Generated H2 from KBH4 and Methanol
Authors: Farrar-Tobar Ronald , Weber Stefan , Csendes Zita , Ammaturo Antonio , Fleissner Sarah , Hoffmann Helmuth , Veiros Luis , Kirchner Karl
Published: February 2022

The selective semihydrogenation of alkynes with the Mn(I) alkyl catalyst fac-[Mn(dippe)(CO)3(CH2CH2CH3)] (dippe = 1,2-bis(di-iso-propylphosphino)ethane) as pre-catalyst is described. Hydrogen gas required for the hydrogenation is generated in situ upon alcoholysis of KBH4 with methanol. A series of aryl-aryl, aryl-alkyl, alkyl-alkyl and terminal alkynes were readily hydrogenated to yield E-alkenes in good to excellent isolated yields. The reaction proceeds at 90°C with catalyst loadings of 0.5 -2 mol%. The implemented protocol tolerates a variety of electron donating and electron withdrawing functional groups including halides, phenols, nitriles, unprotected amines and heterocycles. The reaction can be upscaled to the gram scale. Mechanistic investigations including deuterium labelling studies and DFT calculations were undertaken to provide a reasonable reaction mechanism showing that initially formed Z-isomer undergoes fast isomerization to afford the thermodynamically more stable E-isomer.
EnzymeML – a data exchange format for biocatalysis and enzymology
Authors: Range Jan , Halupczok Colin , Lohmann Jens , Swainston Neil , Kettner Carsten , Bergmann Frank T. , Weidemann Andreas , Wittig Ulrike , Schnell Santiago , Pleiss Jürgen
Published: February 2022

EnzymeML is an XML–based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modelling tools, and databases. EnzymeML supports the scientific community by introducing a standardised data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An Application Programming Interface in Python and Java supports the integration of applications. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modelling using the modelling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
Direct Observation of Ultrafast Exciton Localization in an Organic Semiconductor with Soft X-ray Transient Absorption Spectroscopy
Authors: Garratt Douglas , Misiekis Lukas , Wood David , Witting Larsen Esben , Matthews Mary , Alexander Oliver , Ye Peng , Jarosch Sebastian , Ferchaud Clement , Bakulin Artem , Penfold Thomas , Marangos Jon
Published: February 2022

We report the first demonstration of time-resolved X-ray absorption spectroscopy to track previously undetected photoinduced dynamics of a paradigmatic crystalline conjugated polymer: poly(3-hexylthiophene) (P3HT) commonly used in solar cell devices. The pi to pi* transition, the first step of solar energy conversion, is pumped with a 15 fs optical pulse and the dynamics are probed by an attosecond soft X-ray pulse at the carbon K-edge. We observe direct spectroscopic signatures of the initially hot excitonic state, which is delocalized over multiple polymer chains, undergoing a rapid evolution on a sub 50 fs timescale which can be directly associated with cooling and localization to form the lowest excitonic state on a single polymer chain. This sensitivity of time-resolved X-ray spectroscopy to the primary electron dynamics occurring directly after excitation paves the way for new insights in a wide range of organic optoelectronic materials.
A Fluorogenic Labeling of Live Viruses
Authors: Zheng Bin , Li Xianhuang , Xie Sheng , Wang Shuodong , Lai Weiming , Zhou Zhibiao , Liu Qing , Yang Yujiao , Hou Bei , Ni Weiwei , Han Jinsong , Liu Yong , Zeng Zebing , Wang Tao , Tang Ben Zhong
Published: February 2022

Viruses are an important class of pathogens that can cause serious infectious diseases. The ability to directly analyze viral particles in real time is essential to understand their pathological functions. In this work, we developed a strategy for fluorogenic labeling of live viruses by using an optimal tetrazolate-functionalized AIEgen (Aggregation-induced emission luminogen), named PBET. Due to its AIE activities, this dye fluoresces weakly when dissolved in aqueous solution, thus providing dark fluorescence background. The fluorescence turn-on labeling is achieved by binding of PBET molecules into the outmost viral proteins (e.g., structural Capsid protein VP1), which triggers protein-induced fluorescence enhancement (PIFE) of PBET. Probably due to this PIFE/AIE-based sensing strategy, these PBET-labeled viruses can retain infectivity to a large extent, which is a great advantage for biologically friendly labeling. This fluorogenic labeling may find uses in imaging studies to combat virus-associated infectious diseases, especially those associated with previously unknown wild-type viruses.
A unified synthetic strategy to introduce heteroatoms via electrochemical functionalization of alkyl organoboron reagents
Authors: Lee Hong Geun , Chung Taek Dong , Go Su Yong , Chung Hyunho , Shin Samuel Jaeho , An Sohee , Youn Ju Hyun , Im Tae Yeong , Kim Ji Yong
Published: February 2022

Based on systematic electrochemical analyses, an integrated synthetic platform of C(sp3)-based organoboron compounds was established for the introduction of heteroatoms. The electrochemically mediated bond-forming strategy was shown to be highly effective for the functionalization of sp3-hybridized carbon atoms with significant steric hindrance. Moreover, virtually all the nonmetallic heteroatoms could be utilized as reaction partners using one unified protocol. The observed reactivity stems from the two consecutive single-electron oxidations of the substrate, which eventually generates an extremely reactive carbocation as the key intermediate. The detailed reaction profile could be elucidated through multifaceted electrochemical studies and the examination of the reaction kinetics. Ultimately, a new dimension in the activation strategies for organoboron compounds was accomplished through the electrochemically driven reaction development.
Many-Worlds QM Tested by Quantum Chemistry: a New Kind of Reaction Rate
Authors: Tipler Frank
Published: February 2022

Quantum chemists have recorded a huge number of data points which standard quantum mechanics cannot interpret. Many-Worlds quantum mechanics can. Many-Worlds quantum mechanics differs from standard quantum mechanics because in Many-Worlds, the wave function is a relative density of universes in the multiverse amplitude rather than a probability amplitude. In Many-Worlds, the Born frequencies are approached rather than given a priori. Thus in Many-Worlds, the rate of approach to the final frequencies can be calculated and compared with observation. This is a new type of reaction rate. I show how to use Many-Worlds to analyze the breakup of ${\rm Br}_2$. Such analysis will experimentally test Many-Worlds fermionic and time-dependent quantum mechanics. Conversely, experimental quantum chemistry has become too sophisticated to be analyzed by standard quantum mechanics. Only Many-Worlds can do the job
Machine Learning Dynamic Correlation in Chemical Kinetics
Authors: Kim Changhae Andrew , Ricke Nathan D. , Van Voorhis Troy
Published: February 2022

The kinetics of surface reactions are often described using a lattice model. Since it is expensive to propagate the configuration probabilities of the entire lattice, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation (CME). Unfortunately, simple closures, such as the mean-field (MF) and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics, using the lattice Lotka-Volterra model (LLVM) as a model system. We trained feed-forward neural networks (FFNNs) on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the machine learning moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pre-trained closures that would enable researchers to extract useful insight at a fraction of the computational cost.
Short-Range Imbalances in the AMBER Lennard-Jones Potential for (Deoxy)Ribose…Nucleobase Lone-pair…π Contacts in Nucleic Acids.
Authors: Mrazikova Klaudia , Sponer Jiri , Mlynsky Vojtech , Auffinger Pascal , Kruse Holger
Published: February 2022

The lone-pair…π (lp…π) (deoxy)ribose…nucleobase stacking is a recurring structural motif in Z DNA and RNAs that is characterized by sub-van der Waals lp…π contacts (<3.0 Å). It is part of the structural signature of the CpG Z-steps in Z-DNA and r(UNCG) tetraloops. These nucleic acid structures are poorly behaving in molecular dynamics (MD) simulations. Although the exact origin of these issues remains unclear, a significant part of the problem might be due to an imbalanced description of non-bonded interactions including the characteristic lp…π stacking. To gain insights into the links between lp…π stacking and MD issues, we present an in-depth comparison between accurate large-basis-set double-hybrid Kohn-Sham density functional theory calculations DSD-BLYP-D3/ma-def2-QZVPP (DHDF-D3) and data obtained with the non-bonded potential of the AMBER force field (AFF) for NpN Z-steps (N = G, A, C, U). Among other differences, we found that the AFF overestimates the DHDF D3 lp…π distances by ~0.1-0.2 Å while the deviation between the DHDF-D3 and AFF descriptions sharply increases in the short-range region of the interaction. Based on atom-in-molecule (AIM) polarizabilities and SAPT analysis, we inferred that the DHDF-D3 vs. AFF differences partly originate in the Lennard-Jones (LJ) parameters that are identical for nucleobase carbon atoms despite the presence/absence of connected electron withdrawing groups that lead to different effective volumes or vdW radii. Thus, to precisely model the very short CpG lp…π contact distances, we recommend revision of the nucleobase atom LJ parameters. Additionally, we suggest that the large discrepancy between DHDF-D3 and AFF short-range repulsive part of the interaction energy potential may significantly contribute to the poor performances of MD simulations of nucleic acid systems containing Z-steps. Understanding where, and if possible why, the point-charge-type effective potentials reach their limits is vital for developing next-generation FFs and for addressing specific issues in contemporary MD simulations.
Ten Essential Delocalization Learning Outcomes: How Well Are They Achieved?
Authors: Carle Myriam S. , El Issa Romeo Junior , Pilote Nicolas , Flynn Alison B.
Published: February 2022

OBJECTIVE: Delocalization (resonance) is a concept in organic chemistry that influences the chemical reactivity, activity, structure, and physical properties of molecules. However, the concept has proven challenging for students. The goal of the present study was to investigate to what extent ten essential delocalization learning outcomes (LOs) were achieved by students, how students use and reason about delocalization as well as the connections between the LOs. The goal is to discover where and how students may be struggling when answering delocalization-related exam questions and uncover potential barriers to learning delocalization. METHODS: We analyzed students’ responses (N = 3787) on twelve exam questions related to seven of the ten LOs for the degree of achievement, common errors, and scientific reasoning. RESULTS: The achievement on the LOs was variable. We report types of errors and strategies used, the errors are primarily related to drawing resonance structures or the resonance. Six key findings emerged from the analysis: (1) the majority of answers had few (<10%) representational errors (2) in an implicit question where delocalization or inductive effect concepts could be used to justify a response, half the students used delocalization concepts, (3) delocalization was used in 10–20% of answers when relevant but not prompted or required, (4) strategies that helped students reason with the representations (i.e., drawing out electrons or expanding a structure) were correlated with higher achievement of the LOs, (5) students’ reasoning aligned with course expectations, and (6) students who achieved later LOs typically (60–95%) also achieved LO1 and LO2 (Identify that electron delocalization is relevant, Draw resonance structures). CONCLUSIONS: The findings have implications on how students achieve the LOs and suggest ways educators can better support learners with the tools to achieve the LOs. IMPLICATIONS: The findings from this work could be used to design and evaluate new teaching techniques or materials, including scaffolding concepts. Further investigations could lead to a deeper understanding of students’ mental models and thought processes related to delocalization concepts.
A Comparative Study of Marginalized Graph Kernel and Message Passing Neural Network
Authors: Xiang Yan , Tang Yu-Hang , Lin Guang , Sun Huai
Published: February 2022

This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combining with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the Directed Message Passing Neural Network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operates on molecular graphs and global molecular features, respectively. Bayesian optimization was used to get the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that the predictions of both models are correlated with similar performance, and the ensemble prediction of both models performs better than either of them. Through principal component analysis, we found that the features extracted by the hybrid kernel are similar to those extracted by D-MPNN. The advantage of D-MPNN lies in computational efficiency, while the advantage of the graph kernel models lies in the inherent uncertainty quantification and accurate uncertainty quantification. All codes for graph kernel machines used in this work can be found at https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine.