From quantum-derived principles underlying cysteine reactivity to combating the COVID-19 pandemic

A switch-on luminescent europium(iii) probe for selective and time-resolved detection of adenosine diphosphate (ADP)

Adenosine diphosphate (ADP) is a key product of two essential classes of biological reactions, catalysed by ATPases and kinases. This makes ADP a highly appealing target for supramolecular detection. However, doing so selectively is exceedingly difficult due to ADP's lower overall charge and similar structure to ATP and the need for compatibility with biological media. Overcoming this challenge, here we present a water-soluble, ADP-selective, luminescent europium(iii) probe suitable for use in vitro and in cellular microscopy. This negatively charged Eu(iii) complex binds ADP reversibly and responds by switching on its luminescence, whilst showing minimal interference from ATP, pyrophosphate and a wide range of biological anions. The probe is equipped with two π-conjugated quinolyl-phenoxyacetate antennae, facilitating excitation at 355 nm in fluorescence microscopy. The ancillary carboxylate groups ensure high water solubility and suppress non-specific binding to albumin protein. Our novel probe demonstrates a level of sensing selectivity for ADP that is unrivaled, producing a linear emission response across the physiologically relevant concentration range (10-400 μM), even in the presence of excess millimolar ATP. We demonstrate that this amphiphilic Eu(iii) probe permeates mammalian cells and localises within the mitochondria and lysosomes. The low background emission of the probe combined with its excellent ADP selectivity and long-lived luminescence makes it a promising tool for visualising ADP levels in living cells.

Evaluation of Density-Functional Tight-Binding Methods for Simulation of Protic Molecular Ion Pairs

In this work, we benchmark the accuracy of the density-functional tight-binding (DFTB) method, namely the long-range corrected second-order (LC-DFTB2) and third-order (DFTB3) models, for predicting energetics of imidazolium-based ionic liquid (IL) ion pairs. We compare the DFTB models against popular density functionals such as LC-ωPBE and B3LYP, using ab initio domain-based local pair-natural orbital coupled cluster (DLPNO-CC) energies as reference. Calculations were carried out in the gas phase, as well as in aqueous solution using implicit solvent methods. We find that the LC-DFTB2 model shows excellent performance in the gas phase and agrees well with reference energies in implicit solvent, often outperforming DFTB3 predictions for complexation energetics. Our study identifies a range of opportunities for use of the LC-DFTB method and quantifies its sensitivity to protonation states and the types of chemical interactions between ion pairs.

More π, please: What drives the formation of unsaturated molecules in the interstellar medium?

We present a computational investigation into the fragmentation pathways of ethanolamine (C2H7NO, EtA), propanol (C3H8O, PrO), butanenitrile (C4H7N, BuN), and glycolamide (C2H5NO2, GlA)-saturated organic molecules detected in the interstellar medium (ISM), particularly in the molecular cloud complex Sagittarius B2 (Sgr B2) and its molecular cloud G+0.693-0.027. Using electron-impact ionization data and Born-Oppenheimer molecular dynamics simulations, we investigate how cosmic rays, cosmic-ray-induced UV fields, and shock-induced heating can induce the fragmentation of these molecules, resulting in the formation of unsaturated species with extended π-bond networks. Despite the attenuation of external UV radiation in G+0.693-0.027, these energetic processes are capable of driving partial transformations of saturated into unsaturated molecules, supporting the coexistence of species like EtA and GlA alongside unsaturated nitriles such as cyanoacetylene (HC3N), cyanopropyne (CH3C3N), and cyanoallene (CH2CCHCN). Our findings underscore the significance of high-energy mechanisms in enhancing chemical complexity within molecular clouds and offer insights into the pathways that govern the evolution of organic molecules in the ISM.

The Effect of Chalcogen-Chalcogen Bond Formation in the New Delhi Metallo-β-Lactamase 1 Enzyme to Counteract Antibiotic Resistance

New Delhi metallo-β-lactamase 1 (NDM-1) is an enzyme involved in the drug resistance of many bacteria against most of the widely adopted antibiotics, such as penicillins, cephalosporins, and carbapenems. Consequently, inhibiting NDM-1 swiftly has gained significant interest as a strategy to counteract this bacterial defense mechanism, thereby restoring the effectiveness of antibiotics. Among the inhibitors tested against the enzyme, ebselen (EbSe) showed particularly promising results. This molecule, renowned for its numerous benefits to the human body, targets the enzyme's active site at Cys208 with its selenium atom, facilitating the expulsion of the catalytic zinc ion from the active pocket. Since the inhibitory mechanism of EbSe remains poorly understood, gaining detailed information about it is highly desirable. In the present work, density functional theory calculations and μs-long molecular dynamics simulations are carried out to investigate the reaction mechanism of EbSe with NDM-1, unveiling the structural implications of the inhibition. A large model of the NDM-1 active site is built to investigate the different mechanistic proposals for the SeEbSe-SCys208 bond formation. Deeper insights into Lys211 are also provided to consolidate its role during the inhibition process. Furthermore, the chemical reaction with the ebsulfur (EbS) molecule is also investigated to compare its behavior with that of the periodic relative selenium. Molecular dynamics simulations, besides evidencing the role of the L3 and L10 loops in the occurrence of the inhibition, corroborate the Zn ion release from the active site as a result of the complete disruption of its coordination sphere caused by the creation of the SeEbSe-SCys208 covalent bond.

An efficient and flexible approach for local distortion: distortion distribution analysis enabled by fragmentation

Distortion can play crucial roles in influencing structures and properties, as well as enhancing reactivity or selectivity in many chemical and biological systems. The distortion/interaction or activation-strain model is a popular and powerful method for deciphering the origins of activation energies, in which distortion and interaction energies dictate an activation energy. However, decomposition of local distortion energy at the atomic scale remains less clear and straightforward. Knowing such information should deepen our understanding of reaction processes and improve reaction design. Herein, an efficient, general and flexible fragmentation-based approach was proposed to evaluate local distortion energies for various chemical and biological molecules, which can be obtained computationally and/or experimentally. Moreover, our distortion analysis is readily applicable to multiple structures from molecular dynamics (or the minimum energy path) as well as can be evaluated by different computational chemistry methods. Our systematic analysis shows that our approach not only aids computational and experimental chemists in visualizing (relative) distortion distributions within molecules (distortion map) and identifies the key distorted pieces, but also offers deeper understanding and insights into structures, reaction mechanisms and dynamics in various chemical and biological systems. Furthermore, our analysis offers indices of local distortion energy, which can potentially serve as a new descriptor for multi-linear regression (MLR) or machine learning (ML) modelling.

Modelling ligand exchange in metal complexes with machine learning potentials

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.

Slow magnetic relaxation and strong magnetic coupling in the nitroxyl radical complexes of lanthanide(iii) with diamagnetic ground state (Ln = Lu, Eu)

Radical lanthanide complexes are appealing platforms to investigate the possibility to engineer relevant magnetic couplings between the two magnetic centers by exploiting the strongly donating magnetic orbitals of the radical. In this paper, we report a spectroscopic and magnetic study on [LnRad(NO3)3], where Ln = Eu3+ or Lu3+ and Rad is the tridentate tripodal nitroxyl radical 4,4-dimethyl-2,2-bis(pyridin-2-yl)-1,3-oxazolidine-3-oxyl. A thorough magnetic investigation by Electron Paramagnetic Resonance (EPR) spectroscopy and magnetometry, fully supported by ab initio calculations, allowed us to unravel an unprecedentedly large antiferromagnetic coupling between the Eu3+ and the radical (J 12 = +19.5 cm-1, +J 12 S 1 S 2 convention). Remarkably, both europium and lutetium complexes showed slow magnetization dynamics below 20 K. The field and temperature dependent relaxation dynamics, dominated by Raman and direct processes were modelled simultaneously, allowing us to assess that the Raman process is field dependent.

Performance of quantum chemistry methods for a benchmark set of spin-state energetics derived from experimental data of 17 transition metal complexes (SSE17)

Accurate prediction of spin-state energetics for transition metal (TM) complexes is a compelling problem in applied quantum chemistry, with enormous implications for modeling catalytic reaction mechanisms and computational discovery of materials. Computed spin-state energetics are strongly method-dependent and credible reference data are scarce, making it difficult to conduct conclusive computational studies of open-shell TM systems. Here, we present a novel benchmark set of first-row TM spin-state energetics, which is derived from experimental data of 17 complexes containing FeII, FeIII, CoII, CoIII, MnII, and NiII with chemically diverse ligands. The estimates of adiabatic or vertical spin-state splittings, which are obtained from spin crossover enthalpies or energies of spin-forbidden absorption bands, suitably back-corrected for the vibrational and environmental effects, are employed as reference values for benchmarking density functional theory (DFT) and wave function methods. The results demonstrate a high accuracy of the coupled-cluster CCSD(T) method, which features the mean absolute error (MAE) of 1.5 kcal mol-1 and maximum error of -3.5 kcal mol-1, and outperforms all the tested multireference methods: CASPT2, MRCI+Q, CASPT2/CC and CASPT2+δMRCI. Switching from Hartree-Fock to Kohn-Sham orbitals is not found to consistently improve the CCSD(T) accuracy. The best performing DFT methods are double-hybrids (PWPB95-D3(BJ), B2PLYP-D3(BJ)) with the MAEs below 3 kcal mol-1 and maximum errors within 6 kcal mol-1, whereas the DFT methods so far recommended for spin states (e.g., B3LYP*-D3(BJ) and TPSSh-D3(BJ)) are found to perform much worse with the MAEs of 5-7 kcal mol-1 and maximum errors beyond 10 kcal mol-1. This work is the first such extensive benchmark study of quantum chemistry methods for TM spin-state energetics making use of experimental reference data. The results are relevant for the proper choice of methods to characterize TM systems in computational catalysis and (bio)inorganic chemistry, and may also stimulate new developments in quantum-chemical or machine learning approaches.

Tailored charge-neutral self-assembled L2Zn2 container for taming oxalate

Dicarboxylic acids and their derivatives play crucial roles in various biological processes, necessitating the development of effective receptors for their detection. In particular, the smallest dicarboxylate, oxalate, presents a significant importance due to its widespread presence in nature and its association with various diseases. Yet, very little attention was devoted to the recognition of oxalate with metal-driven self-assemblies like cages or containers while numerous classic organic receptors for oxalate exist. This discrepancy is astonishing because metallocontainers or metallocages have advantages over classic macrocycles or organocages like a higher modularity and good preorganization paired with a ready receptor preparation by metal complexation. The reason for the underrepresentation is the competitive nature and excellent ligand properties of oxalate which not only is associated with the aforementioned diseases but also poses a serious hazard for metal-driven self-assemblies because the dianion can easily replace ligand strands leading to a partial or full receptor decomposition. Herein, we present a charge-neutral zinc(II)-based metallocontainer which was tuned to contest oxalate as most competitive dicarboxylate. The dianion is bound in a 1:1 fashion with a binding constant of log K = 4.39 selectively over other dicarboxylates by maintaining the receptor stability. This study highlights the importance of a highly modular receptor design so that tailored hosts can be designed to tackle the recognition of challenging competitive analytes.

London dispersion driven compaction of coordination cages in the gas-phase - a combined ion mobility and theoretical study

Large self-assembled systems (such as metallosupramolecular rings and cages) can be difficult to structurally characterize, in particular when they show a highly dynamic behavior. In the gas-phase, Ion Mobility Spectrometry (IMS), in tandem with Electrospray Ionization Mass Spectrometry (ESI MS), can yield valuable insights into the size, shape and dynamics of such supramolecular assemblies. However, the detailed relationship between experimental IMS data and the actual gas-phase structure is still poorly understood for soft and flexible self-assemblies. In this study, we combine high resolution Trapped Ion Mobility Spectrometry (TIMS), yielding collisional cross section data (CCS), with computational modeling and theoretical CCS calculations to obtain and interpret gas-phase structural data for a series of palladium-based coordination cages. We focus on derivatives of a homoleptic lantern-shaped [Pd2L4]4+ cage and its interpenetrated dimer ([3X@Pd4L8]5+, X = Cl, Br) to study the influence of flexible side chains of different lengths, counter anions and π-stacking tendencies between the ligands in the absence of solvent. The gained insights as well as the presented CCS calculation and evaluation workflow establish a basis for the systematic gas-phase characterization of a wider range of flexible, chain-decorated and guest-modulated assemblies.

Kinetics of sulfur-transfer from titanocene (poly)sulfides to sulfenyl chlorides: rapid metal-assisted concerted substitution

The kinetics of sulfur transfer from titanocene (poly)sulfides (RCp2TiS5, Cp2TiS4CMe2, Cp2Ti(SAr)2, Cp2TiCl(SAr)) to sulfenyl chlorides (S2Cl2, RSCl) have been investigated by a combination of stopped-flow UV-Vis/NMR reaction monitoring, titration assays, numerical kinetic modelling and KS-DFT calculations. The reactions are rapid, proceeding to completion over timescales of milliseconds to minutes, via a sequence of two S-S bond-forming steps (k 1, k 2). The archetypical polysulfides Cp2TiS5 (1a) and Cp2TiS4C(Me2) (2a) react with disulfur dichloride (S2Cl2) through rate-limiting intermolecular S-S bond formation (k 1) followed by a rapid intramolecular cyclization (k 2, with k 2 ≫ k 1 [RSCl]). The monofunctional sulfenyl chlorides (RSCl) studied herein react in two intermolecular S-S bond forming steps proceeding at similar rates (k 1 ≈ k 2). Reactions of titanocene bisthiophenolates, Cp2Ti(SAr)2 (5), with both mono- and di-functional sulfenyl chlorides result in rapid accumulation of the monothiophenolate, Cp2TiCl(SAr) (6) (k 1 > k 2). Across the range of reactants studied, the rates are relatively insensitive to changes in temperature and in the electronics of the sulfenyl chloride, moderately sensitive to the electronics of the titanocene (poly)sulfide (ρ (Ti-(SAr)) ≈ -2.0), and highly sensitive to the solvent polarity, with non-polar solvents (CS2, CCl4) leading to the slowest rates. The combined sensitivities are the result of a concerted, polarized and late transition state for the rate-limiting S-S bond forming step, accompanied by a large entropic penalty. Each substitution step {[Ti]-SR' + Cl-SR → [Ti]-Cl + RS-SR'} proceeds via titanium-assisted Cl-S cleavage to generate a transient pentacoordinate complex, Cl-[Cp2TiX]-S(R')-SR, which then undergoes rapid Ti-S dissociation.

Ultrafast photogeneration of a metal-organic nitrene from 1,1'-diazidoferrocene

Ferrocene and its derivatives have fascinated chemists for more than 70 years, not least due to the analogies with the properties of benzene. Despite these similarities, the obvious difference between benzene and ferrocene is the presence of an iron ion and hence the availability of d-orbitals for properties and reactivity. Phenylnitrene with its rich photochemistry can be considered an analogue of nitrenoferrocene. As with most organic and inorganic nitrenes, nitrenoferrocene can be obtained by irradiating the azide precursor. We study the photophysical and photochemical processes of dinitrogen release from 1,1'-diazidoferrocene to form 1-azido-1'-nitrenoferrocene with UV-pump-mid-IR-probe transient absorption spectroscopy and time-dependent density functional theory calculations including spin-orbit coupling. An intermediate with a bent azide moiety is identified that is pre-organised for dinitrogen release via a low-lying transition state. The photochemical decay paths on the singlet and triplet surfaces including the importance of spin-orbit coupling are discussed. We compare our findings with the processes discussed for photochemical dinitrogen activation and highlight implications for the photochemistry of azides more generally.

An Antibody-Drug Conjugate for Multiple Myeloma Prepared by Multi-Arm Linkers

First-line treatment of multiple myeloma, a prevalent blood cancer lacking a cure, using anti-CD38 daratumumab antibody and lenalidomide is often inadequate due to relapse and severe side effects. To enhance drug safety and efficacy, an antibody-drug conjugate, TE-1146, comprising six lenalidomide drug molecules site-specifically conjugated to a reconfigured daratumumab to deliver cytotoxic lenalidomide to tumor cells is developed. TE-1146 is prepared using the HighDAR platform, which employs i) a maleimide-containing "multi-arm linker" to conjugate multiple drug molecules creating a drug bundle, and ii) a designed peptide with a Zn2+-binding cysteine at the C-termini of a reconfigured daratumumab for site-specific drug bundle conjugation. It is shown that TE-1146 remains intact and effectively enters CD38-expressing tumor cells, releasing lenalidomide, leading to enhanced cell-killing effects compared to lenalidomide/daratumumab alone or their combination. This reveals the remarkable potency of lenalidomide once internalized by myeloma cells. TE-1146 precisely delivers lenalidomide to target CD38-overexpressing tumor cells. In contrast, lenalidomide without daratumumab cannot easily enter cells, whereas daratumumab without lenalidomide relies on Fc-dependent effector functions to kill tumor cells.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Targeting Cysteine Oxidation in Thrombotic Disorders

Oxidative stress increases the risk for clinically significant thrombotic events, yet the mechanisms by which oxidants become prothrombotic are unclear. In this review, we provide an overview of cysteine reactivity and oxidation. We then highlight recent findings on cysteine oxidation events in oxidative stress-related thrombosis. Special emphasis is on the signaling pathway induced by a platelet membrane protein, CD36, in dyslipidemia, and by protein disulfide isomerase (PDI), a member of the thiol oxidoreductase family of proteins. Antioxidative and chemical biology approaches to target cysteine are discussed. Lastly, the knowledge gaps in the field are highlighted as they relate to understanding how oxidative cysteine modification might be targeted to limit thrombosis.

Site-specific Conjugation of 6 DOTA Chelators to a CA19-9-targeting scFv-Fc Antibody for Imaging and Therapy

Antibodies conjugated with diagnostic/therapeutic radionuclides are attractive options for inoperable cancers lacking accurate imaging methods and effective therapeutics, such as pancreatic cancer. Hence, we have produced an antibody radionuclide conjugate termed TE-1132 comprising a α-CA19-9 scFv-Fc that is site-specifically conjugated at each C-terminus to 3 DOTA chelators via a cysteine-containing peptide linker. The smaller scFv-Fc size facilitates diffusivity within solid tumors, whereas the chelator-to-antibody ratio of six enabled 177Lu-radiolabeled TE-1132 to exhibit high radioactivity up to 520 MBq/nmol. In mice bearing BxPC3 tumors, immuno-SPECT/CT imaging of [111In]In-TE-1132 and the biodistribution of [177Lu]Lu-TE-1132 showed selective tumor accumulation. Single and multiple doses of [177Lu]Lu-TE-1132 effectively inhibited the BxPC3 tumor growth and prolonged the survival of mice with no irreversible body weight loss or hematopoietic damage. The adequate pharmacokinetic parameters, prominent tumor accumulation, and efficacy with good safety in mice encourage the further investigation of theranostic TE-1132 for treating pancreatic cancer.

Disulfiram: Mechanisms, Applications, and Challenges

Background: Since disulfiram’s discovery in the 1940s and its FDA approval for alcohol use disorder, other indications have been investigated. This review describes potential clinical applications, associated risks, and challenges. Methods: For this narrative review, a PubMed search was conducted for articles addressing in vivo studies of disulfiram with an emphasis on drug repurposing for the treatment of human diseases. The key search terms were “disulfiram” and “Antabuse”. Animal studies and in vitro studies highlighting important mechanisms and safety issues were also included. Results: In total, 196 sources addressing our research focus spanning 1948–2022 were selected for inclusion. In addition to alcohol use disorder, emerging data support a potential role for disulfiram in the treatment of other addictions (e.g., cocaine), infections (e.g., bacteria such as Staphylococcus aureus and Borrelia burgdorferi, viruses, parasites), inflammatory conditions, neurological diseases, and cancers. The side effects range from minor to life-threatening, with lower doses conveying less risk. Caution in human use is needed due to the considerable inter-subject variability in disulfiram pharmacokinetics. Conclusions: While disulfiram has promise as a “repurposed” agent in human disease, its risk profile is of concern. Animal studies and well-controlled clinical trials are needed to assess its safety and efficacy for non-alcohol-related indications.

A strategy for evaluating potential antiviral resistance to small molecule drugs and application to SARS-CoV-2

Alterations in viral fitness cannot be inferred from only mutagenesis studies of an isolated viral protein. To-date, no systematic analysis has been performed to identify mutations that improve virus fitness and reduce drug efficacy. We present a generic strategy to evaluate which viral mutations might diminish drug efficacy and applied it to assess how SARS-CoV-2 evolution may affect the efficacy of current approved/candidate small-molecule antivirals for Mpro, PLpro, and RdRp. For each drug target, we determined the drug-interacting virus residues from available structures and the selection pressure of the virus residues from the SARS-CoV-2 genomes. This enabled the identification of promising drug target regions and small-molecule antivirals that the virus can develop resistance. Our strategy of utilizing sequence and structural information from genomic sequence and protein structure databanks can rapidly assess the fitness of any emerging virus variants and can aid antiviral drug design for future pathogens.

From quantum-derived principles underlying cysteine reactivity to combating the COVID-19 pandemic

The COVID-19 pandemic poses a challenge in coming up with quick and effective means to counter its cause, the SARS-CoV-2. Here, we show how the key factors governing cysteine reactivity in proteins derived from combined quantum mechanical/continuum calculations led to a novel multi-targeting strategy against SARS-CoV-2, in contrast to developing potent drugs/vaccines against a single viral target such as the spike protein. Specifically, they led to the discovery of reactive cysteines in evolutionary conserved Zn2+-sites in several SARS-CoV-2 proteins that are crucial for viral polypeptide proteolysis as well as viral RNA synthesis, proofreading, and modification. These conserved, reactive cysteines, both free and Zn2+-bound, can be targeted using the same Zn-ejector drug (disulfiram/ebselen), which enables the use of broad-spectrum anti-virals that would otherwise be removed by the virus's proofreading mechanism. Our strategy of targeting multiple, conserved viral proteins that operate at different stages of the virus life cycle using a Zn-ejector drug combined with other broad-spectrum anti-viral drug(s) could enhance the barrier to drug resistance and antiviral effects, as compared to each drug alone. Since these functionally important nonstructural proteins containing reactive cysteines are highly conserved among coronaviruses, our proposed strategy has the potential to tackle future coronaviruses. This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisStructure and Mechanism > Computational Biochemistry and BiophysicsElectronic Structure Theory > Density Functional Theory.

Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network

Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.

Study on heterogeneous OH oxidation of 3-methyltetraol sulfate in the atmosphere under high NO conditions

Organosulfates (OSs), also known as organic sulfate esters, are ubiquitous in atmospheric particles and used as secondary organic aerosol (SOA) markers. However, the chemical transformation mechanism of these OSs remains unclear. Therefore, we investigated the heterogeneous OH oxidation of 3-methyltetraol sulfate (3-MTS), which is one of the most abundant particulate organosulfates, by using quantum chemical and kinetic calculations. 3-MTS can easily undergo abstraction reaction with OH radicals, and the reaction rate constant is about 7.87 × 10-12 cm3 per molecule per s. The generated HCOOH, CH3COOH, HCHO, CH3CHO and 2-methyl-2,3-dihydroxypropionic acid are low-volatility species with increased water solubility, which are the main components of SOA. In addition, the OH radicals obtained from the reaction can continue to promote the oxidation reaction. The results of this study provide insights into the heterogeneous OH reactivity of other organosulfates in atmospheric aerosols, and it also provides a new understanding of the conversion of sulfur (S) between its organic and inorganic forms during the heterogeneous OH oxidation of organic sulfates.