Steric effects and quantum interference in the inelastic scattering of NO(X) + Ar

Rotationally inelastic collisions of NO(X) with Ar are investigated in unprecedented detail using state-to-state, crossed molecular beam experiments. The NO(X) molecules are selected in the Ω = 0.5, j = 0.5, f state and then oriented such that either the ‘N’ or ‘O’ end of the molecule is directed towards the incoming Ar atom. Velocity map ion imaging is then used to probe the scattered NO molecules in well-defined quantum states. We show that the fully quantum state-resolved differential steric asymmetry, which quantifies how the relative efficiency for scattering off the ‘O’ and the ‘N’ ends of the molecule varies with scattering angle, is strongly affected by quantum interference. Significant changes in both integral and differential cross sections are found depending on whether collisions occur with the N or O ends of the molecule. The results are well accounted for by rigorous quantum mechanical calculations, in contrast to both classical trajectory calculations and more simplistic models that provide, at best, an incomplete picture of the dynamics.     

Designing molecules by optimizing potentials

The astronomical number of accessible discrete chemical structures makes rational molecular design extremely challenging. We formulate the design of molecules with specific tailored properties as performing a continuous optimization in the space of electron-nuclear attraction potentials. The optimization is facilitated by using a linear combination of atomic potentials (LCAP), a general framework that creates a continuous property landscape from an otherwise unlinked set of discrete molecular-property values. A demonstration of this approach is given for the optimization of molecular electronic polarizability and hyperpolarizability. We show that the optimal structures can be determined without enumerating and separately evaluating the characteristics of the combinatorial number of possible structures, a process that would be much slower. The LCAP approach may be used with quantum or classical Hamiltonians, suggesting possible applications to drug design and new materials discovery.     

High Throughput Screening of the NatureBank ‘Marine Collection’ in a Haemonchus Bioassay Identifies Anthelmintic Activity in Extracts from a Range of Sponges from Australian Waters

Widespread resistance in parasitic nematodes to most classes of anthelmintic drugs demands the discovery and development of novel compounds with distinct mechanisms of action to complement strategic or integrated parasite control programs. Products from nature—which assume a diverse ‘chemical space’—have significant potential as a source of anthelmintic compounds. In the present study, we screened a collection of extracts (n = 7616) derived from marine invertebrates sampled from Australian waters in a high throughput bioassay for in vitro anti-parasitic activity against the barber’s pole worm (Haemonchus contortus)—an economically important parasitic nematode of livestock animals. In this high throughput screen (HTS), we identified 58 active extracts that reduced larval motility by ≥70% (at 90 h), equating to an overall ‘hit rate’ of ~0.8%. Of these 58 extracts, 16 also inhibited larval development by ≥80% (at 168 h) and/or induced ‘non-wild-type’ (abnormal) larval phenotypes with reference to ‘wild-type’ (normal) larvae not exposed to extract (negative controls). Most active extracts (54 of 58) originated from sponges, three from chordates (tunicates) and one from a coral; these extracts represented 37 distinct species/taxa of 23 families. An analysis of samples by ¹H NMR fingerprinting was utilised to dereplicate hits and to prioritise a set of 29 sponge samples for future chemical investigation. Overall, these results indicate that a range of sponge species from Australian waters represents a rich source of natural compounds with nematocidal or nematostatic properties. Our plan now is to focus on in-depth chemical investigations of the sample set prioritised herein.     

A Comparison between Different Agro-wastes and Carbon Nanotubes for Removal of Sarafloxacin from Wastewater: Kinetics and Equilibrium Studies

In the current study, eco-structured and efficient removal of the veterinary fluoroquinolone antibiotic sarafloxacin (SARA) from wastewater has been explored. The adsorptive power of four agro-wastes (AWs) derived from pistachio nutshells (PNS) and Aloe vera leaves (AV) as well as the multi-walled carbon nanotubes (MWCNTs) has been assessed. Adsorbent derived from raw pistachio nutshells (RPNS) was the most efficient among the four tested AWs (%removal ‘%R’ = 82.39%), while MWCNTs showed the best adsorptive power amongst the five adsorbents (%R = 96.20%). Plackett-Burman design (PBD) was used to optimize the adsorption process. Two responses (‘%R’ and adsorption capacity ‘q_e’) were optimized as a function of four variables (pH, adsorbent dose ‘AD’ (dose of RPNS and MWCNTs), adsorbate concentration [SARA] and contact time ‘CT’). The effect of pH was similar for both RPNS and MWCNTs. Morphological and textural characterization of the tested adsorbents was carried out using FT-IR spectroscopy, SEM and BET analyses. Conversion of waste-derived materials into carbonaceous material was investigated by Raman spectroscopy. Equilibrium studies showed that Freundlich isotherm is the most suitable isotherm to describe the adsorption of SARA onto RPNS. Kinetics’ investigation shows that the adsorption of SARA onto RPNS follows a pseudo-second order (PSO) model.     

The synthesis of a chemically reactive and polymeric luminescent gel

In the past, chemically reactive polymeric interfaces have been considered to be of potential interest for developing functional materials for a wide range of practical applications. Furthermore, the rational incorporation of luminescence properties into such chemically reactive interfaces could provide a basis for extending the horizon of their prospective utility. In this report, a simple catalyst-free chemical approach is introduced to develop a chemically reactive and optically active polymeric gel. Branched-polyethyleneimine (BPEI)-derived, inherently luminescent carbon dots (BPEI-CDs) were covalently crosslinked with pentaacrylate (5Acl) through a 1,4-conjugate addition reaction under ambient conditions. The synthesized polymeric gel was milky white under visible light; however, it displayed fluorescence under UV light. Additionally, the residual acrylate groups in the synthesized fluorescent gel allowed its chemical functionality to be tailored through facile, robust 1,4-conjugate addition reactions with primary-amine-containing small molecules under ambient conditions. The chemical reactivity of the luminescent gel was further employed for a proof-of-concept demonstration of portable and parallel ‘ON’/‘OFF’ toxic chemical sensing (namely, the sensing of nitrite ions as a model analyte). First, the chemically reactive luminescent gel derived from BPEI-CDs was covalently post-modified with aniline for the selective synthesis of a diazo compound in the presence of nitrite ions. During this process, the color of the gel under visible light changed from white to yellow and, thus, the colorimetric mode of the sensor was turned ‘ON’. In parallel, the luminescence of the gel under UV light was quenched, which was denoted as the ‘OFF’ mode of the sensor. This parallel and unambiguous ‘ON’/‘OFF’ sensing of a toxic chemical (nitrite ions, with a detection limit of 3 μM) was also achieved even in presence of other relevant interfering ions and at concentrations well below the permissible limit (65 μM) set by the World Health Organization (WHO). Furthermore, this chemically reactive luminescent gel could be of potential interest in a wide range of basic and applied contexts.

An unprecedented chemically reactive and polymeric luminescent gel is developed, and this material is further employed to develop a portable and rapid sensor for a practically relevant analyte (nitrite ions) with a sensitivity of 3 μM.     

Alkaloids Used as Medicines: Structural Phytochemistry Meets Biodiversity—An Update and Forward Look

Selecting candidates for drug developments using computational design and empirical rules has resulted in a broad discussion about their success. In a previous study, we had shown that a species’ abundance [as expressed by the GBIF (Global Biodiversity Information Facility)] dataset is a core determinant for the development of a natural product into a medicine. Our overarching aim is to understand the unique requirements for natural product-based drug development. Web of Science was queried for research on alkaloids in combination with plant systematics/taxonomy. All alkaloids containing species demonstrated an average increase of 8.66 in GBIF occurrences between 2014 and 2020. Medicinal Species with alkaloids show higher abundance compared to non-medicinal alkaloids, often linked also to cultivation. Alkaloids with high biodiversity are often simple alkaloids found in multiple species with the presence of ’driver species‘ and are more likely to be included in early-stage drug development compared to ‘rare’ alkaloids. Similarly, the success of an alkaloid containing species as a food supplement (‘botanical’) is linked to its abundance. GBIF is a useful tool for assessing the druggability of a compound from a certain source species. The success of any development programme from natural sources must take sustainable sourcing into account right from the start.     

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds.     

Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery

Fragment-based drug discovery (FBDD) is a powerful strategy for the identification of new bioactive molecules. FBDD relies on fragment libraries, generally of modest size, but of high chemical diversity. Although good chemical diversity in FBDD libraries has been achieved in many respects, achieving shape diversity – particularly fragments with three-dimensional (3D) structures – has remained challenging. A recent analysis revealed that >75% of all conventional, organic fragments are predominantly 1D or 2D in shape. However, 3D fragments are desired because molecular shape is one of the most important factors in molecular recognition by a biomolecule. To address this challenge, the use of inert metal complexes, so-called ‘metallofragments’ (mFs), to construct a 3D fragment library is introduced. A modest library of 71 compounds has been prepared with rich shape diversity as gauged by normalized principle moment of inertia (PMI) analysis. PMI analysis shows that these metallofragments occupy an area of fragment space that is unique and highly underrepresented when compared to conventional organic fragment libraries that are comprised of orders of magnitude more molecules. The potential value of this metallofragment library is demonstrated by screening against several different types of proteins, including an antiviral, an antibacterial, and an anticancer target. The suitability of the metallofragments for future hit-to-lead development was validated through the determination of IC₅₀ and thermal shift values for select fragments against several proteins. These findings demonstrate the utility of metallofragment libraries as a means of accessing underutilized 3D fragment space for FBDD against a variety of protein targets.

Fragment-based drug discovery (FBDD) using 3-dimensional metallofragments is a new strategy for the identification of bioactive molecules.     

Controlled synthesis of Bi- and tri-nuclear Cu-oxo nanoclusters on metal–organic frameworks and the structure–reactivity correlations

Precisely tuning the nuclearity of supported metal nanoclusters is pivotal for designing more superior catalytic systems, but it remains practically challenging. By utilising the chemical and molecular specificity of UiO-66-NH₂ (a Zr-based metal–organic framework), we report the controlled synthesis of supported bi- and trinuclear Cu-oxo nanoclusters on the Zr₆O₄ nodal centres of UiO-66-NH₂. We revealed the interplay between the surface structures of the active sites, adsorption configurations, catalytic reactivities and associated reaction energetics of structurally related Cu-based ‘single atoms’ and bi- and trinuclear species over our model photocatalytic formic acid reforming reaction. This work will offer practical insight that fills the critical knowledge gap in the design and engineering of new-generation atomic and nanocluster catalysts. The precise control of the structure and surface sensitivities is important as it can effectively lead to more reactive and selective catalytic systems. The supported bi- and trinuclear Cu-oxo nanoclusters exhibit notably different catalytic properties compared with the mononuclear ‘Cu₁’ analogue, which provides critical insight for the engineering of more superior catalytic systems.

The controlled synthesis of novel bi- and trinuclear Cu-oxo nanoclusters supported on UiO-66-NH₂ that show notably different catalytic properties in the photocatalytic formic acid decomposition reaction is reported.     

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

Deep learning-accelerated docking coupled with computational hit selection strategies enable the identification of inhibitors for the SARS-CoV-2 main protease from a chemical library of 40 billion small molecules.     

Structural rearrangement of Neisseria meningitidis transferrin binding protein A (TbpA) prior to human transferrin protein (hTf) binding

Gram-negative bacterium Neisseria meningitidis, responsible for human infectious disease meningitis, acquires the iron (Fe³⁺) ion needed for its survival from human transferrin protein (hTf). For this transport, transferrin binding proteins TbpA and TbpB are facilitated by the bacterium. The transfer cannot occur without TbpA, while the absence of TbpB only slows down the transfer. Thus, understanding the TbpA-hTf binding at the atomic level is crucial for the fight against bacterial meningitis infections. In this study, atomistic level of mechanism for TbpA-hTf binding is elucidated through 100 ns long all-atom classical MD simulations on free (uncomplexed) TbpA. TbpA protein underwent conformational change from ‘open’ state to ‘closed’ state, where two loop domains, loops 5 and 8, were very close to each other. This state clearly cannot accommodate hTf in the cleft between these two loops. Moreover, the helix finger domain, which might play a critical role in Fe³⁺ ion uptake, also shifted downwards leading to unfavorable Tbp-hTf binding. Results of this study indicated that TbpA must switch between ‘closed’ state to ‘open’ state, where loops 5 and 8 are far from each other creating a cleft for hTf binding. The atomistic level of understanding to conformational switch is crucial for TbpA-hTf complex inhibition strategies. Drug candidates can be designed to prevent this conformational switch, keeping TbpA locked in ‘closed’ state.     

Emergent synthetic methods for the modular advancement of sp3-rich fragments

Fragment-based drug discovery is an important and increasingly reliable technology for the delivery of clinical candidates. Notably, however, sp³-rich fragments are a largely untapped resource in molecular discovery, in part due to the lack of general and suitably robust chemical methods available to aid their development into higher affinity lead and drug compounds. This Perspective describes the challenges associated with developing sp³-rich fragments, and succinctly highlights recent advances in C(sp³)–H functionalisations of high potential value towards advancing fragment hits by ‘growing’ functionalised rings and chains from unconventional, carbon-centred vectors.

This Perspective reviews recently developed methods that are likely to be of value to the elaboration of sp³-rich fragments from carbon-centred vectors, whilst maintaining key fragment-to-target binding interactions.     

Can we predict materials that can be synthesised?

The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated ‘recipe’ for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials.

Materials discovery is a crucial yet experimentally slow and wasteful process. We discuss how discovery can be accelerated by focusing on making predictions that are synthetically realisable.     

A novel chemical biology and computational approach to expedite the discovery of new-generation polymyxins against life-threatening Acinetobacter baumannii

Multidrug-resistant Gram-negative bacteria represent a major medical challenge worldwide. New antibiotics are desperately required with ‘old’ polymyxins often being the only available therapeutic option. Here, we systematically investigated the structure–activity relationship (SAR) of polymyxins using a quantitative lipidomics-informed outer membrane (OM) model of Acinetobacter baumannii and a series of chemically synthesized polymyxin analogs. By integrating chemical biology and all-atom molecular dynamics simulations, we deciphered how each residue of the polymyxin molecule modulated its conformational folding and specific interactions with the bacterial OM. Importantly, a novel designed polymyxin analog FADDI-287 with predicted stronger OM penetration showed improved in vitro antibacterial activity. Collectively, our study provides a novel chemical biology and computational strategy to expedite the discovery of new-generation polymyxins against life-threatening Gram-negative ‘superbugs’.

Multidrug-resistant Gram-negative bacteria have been an urgent threat to global public health. Novel antibiotics are desperately needed to combat these ''superbugs''.     

Content of Phenolic Compounds and Organic Acids in the Flowers of Selected Tulipa gesneriana Cultivars

The study focused on the determination of phenolic acids, flavonoids and organic acids in five tulip cultivars ‘Barcelona’, ‘Columbus’, ‘Strong Gold’, ‘Super Parrot’ and ‘Tropicana’. The cultivars grown in field and in a greenhouse were exposed after cutting to different times of storage (0, 3 and 6 days). The phenolic profile contained 4-hydroxybenzoic, 2,5-dihydroxybenzoic, gallic, vanillic, syringic, salicylic, protocatechuic, trans-cinnamic, p-coumaric, caffeic, ferulic, chlorogenic and sinapic acids, as well as quercetin, rutin, luteonin, catechin and vitexin. The mean phenolic acid content was in the following order: ‘Columbus’ and ‘Tropicana’ > ’Barcelona’ > ’Strong Gold’ > ’Super Parrot’, while the levels of flavonoids were as follows: ‘Strong Gold’ > ’Barcelona’ > ’Tropicana’ > ’Columbus’ > ’Super Parrot’. The highest content of phenolic acids was confirmed for Columbus and Tropicana, while the lowest was for Super Parrot. However total phenolic content was very similar, observed between the place of cultivation, time of storage and cultivars. Malonic, succinic, acetic and citric acids were the major organic acid components in tulip petals. More organic acids (except malonic) were accumulated in tulip petals from fields than those from the greenhouse, while changes during storage were strictly correlated with cultivars.     

Sea Buckthorn Leaf Powders: The Impact of Cultivar and Drying Mode on Antioxidant,Phytochemical, and Chromatic Profile of Valuable Resource

Sea buckthorn (Hippophae rhamnoides L. (HR)) leaf powders are the underutilized, promising resource of valuable compounds. Genotype and processing methods are key factors in the preparation of homogenous, stable, and quantified ingredients. The aim of this study was to evaluate the phenolic, triterpenic, antioxidant profiles, carotenoid and chlorophyll content, and chromatic characteristics of convection-dried and freeze-dried HR leaf powders obtained from ten different female cultivars, namely ‘Avgustinka’, ‘Botaniceskaja Liubitelskaja’, ‘Botaniceskaja’, ‘Hibrid Percika’, ‘Julia’, ‘Nivelena’, ‘Otradnaja’, ‘Podarok Sadu’, ‘Trofimovskaja’, and ‘Vorobjovskaja’. The chromatic characteristics were determined using the CIELAB scale. The phytochemical profiles were determined using HPLC-PDA (high performance liquid chromatography with photodiode array detector) analysis; spectrophotometric assays and antioxidant activities were investigated using ABTS (2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) and FRAP (ferric ion reducing antioxidant power) assays. The sea buckthorn leaf powders had a yellowish-green appearance. The drying mode had a significant impact on the total antioxidant activity, chlorophyll content, and chromatic characteristics of the samples; the freeze-dried samples were superior in antioxidant activity, chlorophyll, carotenoid content, and chromatic profile, compared to convection-dried leaf powder samples. The determined triterpenic and phenolic profiles strongly depend on the cultivar, and the drying technique had no impact on qualitative and quantitative composition. Catechin, epigallocatechin, procyanidin B3, ursolic acid, α-amyrin, and β-sitosterol could be used as quantitative markers in the phenolic and triterpenic profiles. The cultivars ‘Avgustinka’, ‘Nivelena’, and ‘Botaniceskaja’ were superior to other tested cultivars, with the phytochemical composition and antioxidant activity.     

Molecular electrostatic potentials and partial atomic charges from correlated wave functions: Applications to the electronic ground and excited states of 3-methylindole

Procedures have been developed to generate molecular electrostatic potentials based on correlated wave function from ab initio or semiempirical electronic structure programs. A new algorithm for point-wise sampling of the potential is described and used to obtain partial atomic charges via a linear, least squares fit between classical and quantum mechanical electrostatic potentials. The proposed sampling algorithm is efficient and promises to introduce less rotational variance in the potential derived partial charges than algorithms applied previously. Electrostatic potentials and fitted atomic charges from ab initio (HF/6–31G* and MP2/6-31G*) and semiempirical (INDO/S; HF, SECI, and SDCI) wave functions are presented for the electronic ground (S₀) and excited (¹L_b, ¹L_a) states of 3-methylindole. © 1992 by John Wiley & Sons, Inc.     

Sensory Quality Evaluation of Korla Pear from Different Orchards and Analysis of Their Primary and Volatile Metabolites

Metabolites play vital roles in shaping the quality of fresh fruit. In this study, Korla pear fruit harvested from twelve orchards in South Xinjiang, China, were ranked in sensory quality by fuzzy logic sensory evaluation for two consecutive seasons. Then, gas chromatography-mass spectrometry (GC-MS) was applied to determine the primary metabolites and volatile compounds. Sensory evaluation results showed that the panelists were more concerned about ‘mouth feel’ and ‘aroma’ than about ‘fruit size’, ‘fruit shape’ and ‘peel color’. In total, 20 primary metabolites and 100 volatiles were detected in the pear fruit. Hexanal, (E)-2-hexenal, nonanal, d-limonene, (Z)-3-hexen-1-yl acetate and hexyl acetate were identified as the major volatile compounds. Correlation analysis revealed that l-(+)-tartaric acid, hexanoic acid, trans-limonene oxide and 2,2,4-trimethyl-1,3-pentanediol diisobutyrate were negatively correlated with sensory scores. Furthermore, OPLS-DA results indicated that the fruit from three orchards with lower ranks in quality could be distinguished from other samples based on the contents of l-(+)-tartaric acid and other eight metabolites, which were all associated with ‘mouth feel’ and ‘aroma’. This study reveals the metabolites that might be closely associated with the sensory quality attributes of Korla pear, which may provide some clues for promoting the fruit quality in actual production.