SyntaLinker: automatic fragment linking with deep conditional transformer neural networks

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural networks. This state-of-the-art transformer can link molecular fragments automatically by learning from the knowledge of structures in medicinal chemistry databases (e.g. ChEMBL database). Conventionally, linking molecular fragments was viewed as connecting substructures that were predefined by empirical rules. In SyntaLinker, however, the rules of linking fragments can be learned implicitly from known chemical structures by recognizing syntactic patterns embedded in SMILES notations. With deep conditional transformer neural networks, SyntaLinker can generate molecular structures based on a given pair of fragments and additional restrictions. Case studies have demonstrated the advantages and usefulness of SyntaLinker in FBDD.

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD).     

Prediction of drug metabolites using neural machine translation

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.

The structure of the drug, represented with a SMILES sequence, is being translated into the structures of possible metabolites that can be formed in the human body.     

Root-aligned SMILES: a tight representation for chemical reaction prediction

Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method.

We propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient sequence-based synthesis prediction.     

Enhanced enzymatic activity exerted by a packed assembly of a single type of enzyme

In contrast to the dilute conditions employed for in vitro biochemical studies, enzymes are spatially organized at high density in cellular micro-compartments. In spite of being crucial for cellular functions, enzymatic reactions in such highly packed states have not been fully addressed. Here, we applied a protein adaptor to assemble a single type of monomeric enzyme on a DNA scaffold in the packed or dispersed states for carbonic anhydrase. The enzymatic reactions proceeded faster in the packed than in the dispersed state. Acceleration of the reaction in the packed assembly was more prominent for substrates with higher hydrophobicity. In addition, carbonic anhydrase is more tolerant of inhibitors in the packed assembly. Such an acceleration of the reaction in the packed state over the dispersed state was also observed for xylose reductase. We propose that the entropic force of water increases local substrate or cofactor concentration within the domain confined between enzyme surfaces, thus accelerating the reaction. Our system provides a reasonable model of enzymes in a packed state; this would help in engineering artificial metabolic systems.

The enzymatic reactions proceeded faster in the packed than in the dispersed state.     

Construction of coacervate-in-coacervate multi-compartment protocells for spatial organization of enzymatic reactions

Coacervate microdroplets, formed via liquid–liquid phase separation, have been extensively explored as a compartment model for the construction of artificial cells or organelles. In this study, coacervate-in-coacervate multi-compartment protocells were constructed using four polyelectrolytes, in which carboxymethyl-dextran and diethylaminoethyl-dextran were deposited on the surface of as-prepared polydiallyldimethyl ammonium/deoxyribonucleic acid coacervate microdroplets through layer-by-layer assembly. The resulting multi-compartment protocells were composed from two immiscible coacervate phases with distinct physical and chemical properties. Molecule transport experiments indicated that small molecules could diffuse between two coacervate phases and that macromolecular enzymes could be retained. Furthermore, a competitive cascade enzymatic reaction of glucose oxidase/horseradish peroxidase–catalase was performed in the multi-compartment protocells. The different enzyme organization and productions of H₂O₂ led to a distinct polymerization of dopamine. The spatial organization of different enzymes in immiscible coacervate phases, the distinct reaction fluxes between coacervate phases, and the enzymatic cascade network led to distinguishable signal generation and product outputs. The development of this multi-compartment structure could pave the way toward the spatial organization of multi-enzyme cascades and provide new ideas for the design of organelle-containing artificial cells.

A coacervate-in-coacervate micro-architecture is constructed as a multi-compartment protocell model, in which a multi-enzyme cascade is spatially organized for competitive enzymatic reactions.     

Cyclodepsipeptide alveolaride C: total synthesis and structural assignment

First stereoselective total synthesis of naturally occurring bioactive cyclodepsipeptide alveolaride C has been achieved using a convergent approach. This synthetic study enabled us to establish unambiguously the stereochemistry of three unassigned chiral centres embedded in the nonpeptidic segment as well as revised the stereochemistry of the proposed β-phenylalanine counterpart of the molecule. The key strategic features of this synthesis include Sharpless asymmetric dihydroxylation for installing the vicinal diol moiety, Julia–Kocienski olefination for constructing the aliphatic side chain, the Shiina protocol for intermolecular esterification, amide coupling and macrolactamization for the ring formation.

First total synthesis of natural cyclodepsipeptide alveolaride C has been accomplished with an unambiguous solution to its structural riddle.     

Hydrogen and proton exchange at carbon. Imbalanced transition state and mechanism crossover

A recent remarkable study of the C–H oxidation of substituted fluorenyl-benzoates together with the transfer of a proton to an internal receiving group by means of electron transfer outer-sphere oxidants, in the noteworthy absence of hydrogen-bonding interactions, is taken as an example to uncover the existence of a mechanism crossover, making the reaction pass from a CPET pathway to a PTET pathway as the driving force of the global reaction decreases. This was also the occasion to stress that considerations based on “imbalanced” or “asynchronous” transition states cannot replace activation/driving force models based on the quantum mechanical treatment of both electrons and transferring protons.

Using the remarkable study of C–H oxidation of substituted fluorenyl-benzoates as an example, we have shown that a mechanism crossover takes place upon decreasing the driving force, from a CPET pathway to a PTET pathway.     

Revealing the molecular mechanisms of proteolysis of SARS-CoV-2 Mpro by QM/MM computational methods

SARS-CoV-2 M^pro is one of the enzymes essential for the replication process of the virus responsible for the COVID-19 pandemic. This work is focused on exploring its proteolysis reaction by means of QM/MM methods. The resulting free energy landscape of the process provides valuable information on the species appearing along the reaction path and suggests that the mechanism of action of this enzyme, taking place in four steps, slightly differs from that of other cysteine proteases. Our predictions, which are in agreement with some recently published experimental data, can be used to guide the design of COVID-19 antiviral compounds with clinical potential.

The molecular mechanism of the proteolysis reaction catalyzed by SARS-CoV-2 M^pro, one of the enzymes essential for the replication process of the virus responsible for the COVID-19 pandemic, is described using computational QM/MM methods.     

In situ assembled ZIF superstructures via an emulsion-free soft-templating approach

Assembling well-defined MOF superstructures remains challenging as it requires easily removable hard templates or readily available immiscible solutions for an emulsion-based soft-template approach. In this work, a single-step emulsion-free soft templating approach is reported to spontaneously prepare hollow ZIF-8 and ZIF-67 colloidosomes with no further purification. These superstructures can load different enzymes regardless of the size and charge with a high encapsulation efficiency of 99%. We envisage that this work will expand the repertoires of MOF superstructures by the judicious selection of precursors and the reaction medium.

An emulsion-free approach for the preparation of hollow ZIF-8 superstructures in a single step is established based on metal sulfate hydrates in methanol.     

Enhanced catalytic reaction at an air–liquid–solid triphase interface

Gaseous reactant involved heterogeneous catalysis is critical to the development of clean energy, environmental management, health monitoring, and chemical synthesis. However, in traditional heterogeneous catalysis with liquid–solid diphase reaction interfaces, the low solubility and slow transport of gaseous reactants strongly restrict the reaction efficiency. In this minireview, we summarize recent advances in tackling these drawbacks by designing catalytic systems with an air–liquid–solid triphase joint interface. At the triphase interface, abundant gaseous reactants can directly transport from the air phase to the reaction centre to overcome the limitations of low solubility and slow transport of the dissolved gas in liquid–solid diphase reaction systems. By constructing a triphase interface, the efficiency and/or selectivity of photocatalytic reactions, enzymatic reactions, and (photo)electrochemical reactions with consumption of gaseous reactants oxygen, carbon dioxide, and nitrogen are significantly improved.

Gaseous reactant involved liquid–solid diphase interface reactions can be significantly enhanced using rationally designed and constructed air–liquid–solid triphase systems.     

The noncovalent dimerization of a G-quadruplex/hemin DNAzyme improves its biocatalytic properties

While many protein enzymes exert their functions through multimerization, which improves both selectivity and activity, this has not yet been demonstrated for other naturally occurring catalysts. Here, we report a multimerization effect applied to catalytic DNAs (or DNAzymes) and demonstrate that the enzymatic efficiency of G-quadruplexes (GQs) in interaction with the hemin cofactor is remarkably enhanced by homodimerization. The resulting non-covalent dimeric GQ–DNAzyme system provides hemin with a structurally defined active site in which both the cofactor (hemin) and the oxidant (H₂O₂) are activated. This new biocatalytic system efficiently performs peroxidase- and peroxygenase-type biotransformations of a broad range of substrates, thus providing new perspectives for biotechnological application of GQs.

Cofactor hemin is sandwiched between 3′ homodimeric G-quadruplexes, leading to an excellent DNAzyme as a mimic of peroxidase and monooxygenase.     

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce four metrics (coverage, class diversity, round-trip accuracy and Jensen–Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks have an excellent performance with few weaknesses related to the training data. The use of the introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks by focusing on the performance of the single-step model only.

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.     

Catalytic asymmetric synthesis of N-substituted tetrahydroquinoxalines via regioselective Heyns rearrangement and stereoselective transfer hydrogenation in one pot

N-Substituted tetrahydroquinoxalines (37 examples) were step-economically obtained in good yield (<97%) and ee (<99%) with readily available substrates. The reaction proceeds through an interesting regioselective Heyns rearrangement/enantioselective transfer hydrogenation in one pot. The substrate scope and the reaction mechanism were systematically investigated.

N-Substituted tetrahydroquinoxalines were step-economically obtained in good yield and ee with readily available substrates.     

Coordination-based self-assembled capsules (SACs) for protein,CRISPR–Cas9, DNA and RNA delivery

Biologics, such as functional proteins and nucleic acids, have recently dominated the drug market and comprise seven out of the top 10 best-selling drugs. Biologics are usually polar, heat sensitive, membrane impermeable and subject to enzymatic degradation and thus require systemic routes of administration and delivery. Coordination-based delivery vehicles, which include nanosized extended metal–organic frameworks (nMOFs) and discrete coordination cages, have gained a lot of attention because of their remarkable biocompatibility, in vivo stability, on-demand biodegradability, high encapsulation efficiency, easy surface modification and moderate synthetic conditions. Consequently, these systems have been extensively utilized as carriers of biomacromolecules for biomedical applications. This review summarizes the recent applications of nMOFs and coordination cages for protein, CRISPR–Cas9, DNA and RNA delivery. We also highlight the progress and challenges of coordination-based platforms as a promising approach towards clinical biomacromolecule delivery and discuss integral future research directions and applications.

SACs can be efficiently used to load biologics such as proteins, CRISPR–Cas9, DNA and RNA and release them on-demand.     

Synthesis of oxalamides by acceptorless dehydrogenative coupling of ethylene glycol and amines and the reverse hydrogenation catalyzed by ruthenium

A sustainable, new synthesis of oxalamides, by acceptorless dehydrogenative coupling of ethylene glycol with amines, generating H₂, homogeneously catalyzed by a ruthenium pincer complex, is presented. The reverse hydrogenation reaction is also accomplished using the same catalyst. A plausible reaction mechanism is proposed based on stoichiometric reactions, NMR studies, X-ray crystallography as well as observation of plausible intermediates.

Ruthenium catalyzed acceptorless dehydrogenative coupling of ethylene glycol and amines to oxalamides is reported. The reverse hydrogenation reaction is also accomplished.     

Development and application of ubiquitin-based chemical probes

Protein ubiquitination regulates almost every process in eukaryotic cells. The study of the many enzymes involved in the ubiquitination system and the development of ubiquitination-associated therapeutics are important areas of current research. Synthetic tools such as ubiquitin-based chemical probes have been making an increasing contribution to deciphering various biochemical components involved in ubiquitin conjugation, recruitment, signaling, and deconjugation. In the present minireview, we summarize the progress of ubiquitin-based chemical probes with an emphasis on their various structures and chemical synthesis. We discuss the utility of the ubiquitin-based chemical probes for discovering and profiling ubiquitin-dependent signaling systems, as well as the monitoring and visualization of ubiquitin-related enzymatic machinery. We also show how the probes can serve to elucidate the molecular mechanism of recognition and catalysis. Collectively, the development and application of ubiquitin-based chemical probes emphasizes the importance and utility of chemical protein synthesis in modern chemical biology.

This article reviews the design, synthesis, and application of different classes of Ub-based chemical probes.     

Thermal effects – an alternative mechanism for plasmon-assisted photocatalysis

Recent experiments claimed that the catalysis of reaction rates in numerous bond-dissociation reactions occurs via the decrease of activation barriers driven by non-equilibrium (“hot”) electrons in illuminated plasmonic metal nanoparticles. Thus, these experiments identify plasmon-assisted photocatalysis as a promising path for enhancing the efficiency of various chemical reactions. Here, we argue that what appears to be photocatalysis is much more likely thermo-catalysis, driven by the well-known plasmon-enhanced ability of illuminated metallic nanoparticles to serve as heat sources. Specifically, we point to some of the most important papers in the field, and show that a simple theory of illumination-induced heating can explain the extracted experimental data to remarkable agreement, with minimal to no fit parameters. We further show that any small temperature difference between the photocatalysis experiment and a control experiment performed under external heating is effectively amplified by the exponential sensitivity of the reaction, and is very likely to be interpreted incorrectly as “hot” electron effects.

A simple Arrhenius-based theory of heating, rather than “hot electrons”, can reproduce some high-profile photocatalysis experimental results to remarkable accuracy. Flaws in temperature measurement may have led to wrong conclusions.