Deep reaction network exploration of glucose pyrolysis.

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple [Formula: see text]-D-glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state-guided reaction exploration has been performed that provides complete pathways to most significant experimental pyrolysis products of [Formula: see text]-D-glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semiempirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized with density function theory, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of the Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)identified pathways to most major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yields in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Reductive Doping Inhibits the Formation of Isomerization-Derived Structural Defects in N-doped Poly(benzodifurandione) (n-PBDF).

Recently, solution-processable n-doped poly(benzodifurandione) (n-PBDF) has been made through in-situ oxidative polymerization and reductive doping, which exhibited exceptionally high electrical conductivities and optical transparency. The discovery of n-PBDF is considered a breakthrough in the field of organic semiconductors. In the initial report, the possibility of structural defect formation in n-PBDF was proposed, based on the observation of structural isomerization from (E)-2H,2'H-[3,3'-bibenzofuranylidene]-2,2'-dione (isoxindigo) to chromeno[4,3-c]chromene-5,11-dione (dibenzonaphthyrone) in the dimer model reactions. In this study, we present clear evidence that structural isomerization is inhibited during polymerization. We reveal that the dimer (BFD1) and the trimer (BFD2) can be reductively doped by several mechanisms, including hydride transfer, forming charge transfer complexes (CTC) or undergoing an integer charge transfer (ICT) with reactants available during polymerization. Once the hydride transfer adducts, the CTC, or the ICT product forms, structural isomerization can be effectively prevented even at elevated temperatures. Our findings provide a mechanistic understanding of why isomerization-derived structural defects are absent in n-PBDF backbone. It lays a solid foundation for the future development of n-PBDF as a benchmark polymer for organic electronics and beyond.

Thermally Accessible Prebiotic Pathways for Forming Ribonucleic Acid and Protein Precursors from Aqueous Hydrogen Cyanide.

The search for prebiotic chemical pathways to biologically relevant molecules is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. However, the advent of computational network exploration methodologies has created the opportunity to compare the kinetic plausibility of various channels and even propose new pathways. Here, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN), two established prebiotic candidates for generating biological precursors, was comprehensively explored with a state-of-the-art exploration algorithm. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules. Reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. Accounting for water-catalyzed reactions qualitatively affects the interpretation of the network kinetics. The case-study also highlights omissions of simpler and lower barrier reaction pathways to certain products by other algorithms that qualitatively affect the interpretation of HCN reactivity.

Algorithmic Explorations of Unimolecular and Bimolecular Reaction Spaces.

Algorithmic reaction exploration based on transition state searches has already made inroads into many niche applications, but its potential as a general-purpose tool is still largely unrealized. Computational cost and the absence of benchmark problems involving larger molecules remain obstacles to further progress. Here an ultra-low cost exploration algorithm is implemented and used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovers all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. The diversity of these benchmarks illustrate that reaction exploration algorithms are approaching general-purpose capability.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields.

Self-Consistent Component Increment Theory for Predicting Enthalpy of Formation.

The gas-phase enthalpy of formation (ΔHf) plays a fundamental role in predicting reaction thermodynamics and constructing kinetic models. With advances in computational power and method development, chemically accurate quantum chemistry methods that can predict ΔHf values for small molecules are available; however, large molecules are still out of reach. Increment theories provide a means of extending the prediction capability of high-level methods by decomposing the molecular ΔHf into the additive contributions from individual atoms, bonds, groups, or components. Here, we introduce a novel component increment theory, topology-automated force-field interaction component increment theory (TCIT), in which all component contributions are derived exclusively from Gaussian-4 (G4) results for algorithmically generated model compounds. In a benchmark evaluation of noncyclic compounds from the Pedley, Naylor, and Kline experimental ΔHf dataset, TCIT exhibits consistently lower signed and absolute errors compared with the conventional Benson group increment theory (BGIT). These results pave the way for future extensions of TCIT to ring-containing, ionic, and radical species for which experimental data scarcity currently limits the application of BGIT.

Clinical efficacy, safety and tolerability of Aliskiren Monotherapy (AM): an umbrella review of systematic reviews.

BACKGROUND: Aliskiren is a newly developed drug. Its role in lowering BP has been recognized. However, the role of aliskiren in treating heart and renal diseases are still controversial. OBJECTIVE: To evaluate the existing evidence about clinical efficacy, safety and tolerability of aliskiren monotherapy (AM). METHODS: An umbrella review of systematic reviews of interventional studies. We searched Pubmed, Embase and Cochrane Library up to June 2019. Two reviewers applied inclusion criteria to the select potential articles independently. The extract and analyze of accessible data were did by two reviewers independently too. Discrepancies were resolved with discussion or the arbitration of the third author. RESULTS: Eventually, our review identified 14 eligible studies. Results showed that for essential hypertension patients, aliskiren showed a great superiority over placebo in BP reduction, BP response rate and BP control rate. Aliskiren and placebo, ARBs or ACEIs showed no difference in the number or extent of adverse events. For heart failure patients, AM did not reduce BNP levels (SMD -0.08, - 0.31 to 0.15) or mortality rate (RR 0.76, 0.32 to 1.80), but it decreased NT-proBNP (SMD -0.12, - 0.21 to - 0.03) and PRA levels (SMD 0.52, 0.30 to 0.75), increased PRC levels (SMD -0.66, - 0.8 to - 0.44). For patients who are suffered from hypertension and diabetes and/or nephropathy or albuminuria at the same time, aliskiren produced no significant effects (RR 0.97, 0.81 to 1.16). CONCLUSION: We found solid evidence to support the benefits of aliskiren in the treatment of essential hypertension, aliskiren can produce significant effects in lowering BP and reliable safety. However, the effects of aliskiren in cardiovascular and renal outcomes were insignificant. TRIAL REGISTRATION: Study has been registered in PROSPERO (CRD42019142141).

Structural basis for recognition and repair of the 3'-phosphate by NExo, a base excision DNA repair nuclease from Neisseria meningitidis.

NExo is an enzyme from Neisseria meningitidis that is specialized in the removal of the 3'-phosphate and other 3'-lesions, which are potential blocks for DNA repair. NExo is a highly active DNA 3'-phosphatase, and although it is from the class II AP family it lacks AP endonuclease activity. In contrast, the NExo homologue NApe, lacks 3'-phosphatase activity but is an efficient AP endonuclease. These enzymes act together to protect the meningococcus from DNA damage arising mainly from oxidative stress and spontaneous base loss. In this work, we present crystal structures of the specialized 3'-phosphatase NExo bound to DNA in the presence and absence of a 3'-phosphate lesion. We have outlined the reaction mechanism of NExo, and using point mutations we bring mechanistic insights into the specificity of the 3'-phosphatase activity of NExo. Our data provide further insight into the molecular origins of plasticity in substrate recognition for this class of enzymes. From this we hypothesize that these specialized enzymes lead to enhanced efficiency and accuracy of DNA repair and that this is important for the biological niche occupied by this bacterium.

Improved CRISPR-Cas12a-assisted one-pot DNA editing method enables seamless DNA editing.

As the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas12a (previously known as Cpf1) system cleaves double-stranded DNA and produces a sticky end, it could serve as a useful tool for DNA assembly/editing. To broaden its application, a variety of engineered FnCas12a proteins are generated with expanded protospacer adjacent motif (PAM) requirements. Two variants (FnCas12a-EP15 and EP16) increased the targeting range of FnCas12a by approximately fourfold. They can efficiently recognize a broad range of PAM sequences including YN (Y = C or T), TAC and CAA. Meanwhile, based on our demonstration that FnCas12a is active from 16 to 60°C, we developed an "improved CRISPR-Cas12a-assisted one-pot DNA editing" (iCOPE) method to facilitate DNA editing by combining the crRNA transcription, digestion, and ligation in one pot. By applying iCOPE, the editing efficiency reached 72-100% for two DNA fragment assemblies, and for the 21 kb large DNA construct modification, the editing efficiency can reach 100%. Thanks to the advantages of Cas12a, iCOPE with only one digestion enzyme could replace current a variety of restriction enzymes to perform the cloning in one pot with almost no sequence constraints. Taken together, this study offers an expanded DNA targeting scope of CRISPR systems and could serve as an efficient seamless one-pot DNA editing tool.

Harmonic surface mapping algorithm for fast electrostatic sums.

We propose a harmonic surface mapping algorithm (HSMA) for electrostatic pairwise sums of an infinite number of image charges. The images are induced by point sources within a box due to a specific boundary condition which can be non-periodic. The HSMA first introduces an auxiliary surface such that the contribution of images outside the surface can be approximated by the least-squares method using spherical harmonics as basis functions. The so-called harmonic surface mapping is the procedure to transform the approximate solution into a surface charge and a surface dipole over the auxiliary surface, which becomes point images by using numerical integration. The mapping procedure is independent of the number of the sources and is considered to have a low complexity. The electrostatic interactions are then among those charges within the surface and at the integration points, which are all the forms of Coulomb potential and can be accelerated straightforwardly by the fast multipole method to achieve linear scaling. Numerical calculations of the Madelung constant of a crystalline lattice, electrostatic energy of ions in a metallic cavity, and the time performance for large-scale systems show that the HSMA is accurate and fast, and thus is attractive for many applications.

Specialization of an Exonuclease III family enzyme in the repair of 3' DNA lesions during base excision repair in the human pathogen Neisseria meningitidis.

We have previously demonstrated that the two Exonuclease III (Xth) family members present within the obligate human pathogen Neisseria meningitidis, NApe and NExo, are important for survival under conditions of oxidative stress. Of these, only NApe possesses AP endonuclease activity, while the primary function of NExo remained unclear. We now reveal further functional specialization at the level of 3'-PO(4) processing for NExo. We demonstrate that the bi-functional meningococcal glycosylases Nth and MutM can perform strand incisions at abasic sites in addition to NApe. However, no such functional redundancy exists for the 3'-phosphatase activity of NExo, and the cytotoxicity of 3'-blocking lesions is reflected in the marked sensitivity of a mutant lacking NExo to oxidative stress, compared to strains deficient in other base excision repair enzymes. A histidine residue within NExo that is responsible for its lack of AP endonuclease activity is also important for its 3'-phosphatase activity, demonstrating an evolutionary trade off in enzyme function at the single amino acid level. This specialization of two Xth enzymes for the 3'-end processing and strand-incision reactions has not previously been observed and provides a new paradigm within the prokaryotic world for separation of these critical functions during base excision repair.

Differential modes of DNA binding by mismatch uracil DNA glycosylase from Escherichia coli: implications for abasic lesion processing and enzyme communication in the base excision repair pathway.

Mismatch uracil DNA glycosylase (Mug) from Escherichia coli is an initiating enzyme in the base-excision repair pathway. As with other DNA glycosylases, the abasic product is potentially more harmful than the initial lesion. Since Mug is known to bind its product tightly, inhibiting enzyme turnover, understanding how Mug binds DNA is of significance when considering how Mug interacts with downstream enzymes in the base-excision repair pathway. We have demonstrated differential binding modes of Mug between its substrate and abasic DNA product using both band shift and fluorescence anisotropy assays. Mug binds its product cooperatively, and a stoichiometric analysis of DNA binding, catalytic activity and salt-dependence indicates that dimer formation is of functional significance in both catalytic activity and product binding. This is the first report of cooperativity in the uracil DNA glycosylase superfamily of enzymes, and forms the basis of product inhibition in Mug. It therefore provides a new perspective on abasic site protection and the findings are discussed in the context of downstream lesion processing and enzyme communication in the base excision repair pathway.

Δ2 machine learning for reaction property prediction.

The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ2 model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ2 model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ2 model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ2 model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ2 model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy.

Comprehensive exploration of graphically defined reaction spaces.

Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.

Conformational Sampling for Transition State Searches on a Computational Budget.

Transition state searches are the basis for computationally characterizing reaction mechanisms, making them a pivotal tool in myriad chemical applications. Nevertheless, common search algorithms are sensitive to reaction conformations, and the conformational spaces of even medium-sized reacting systems are too complex to explore with brute force. Here, we show that it is possible to train a classifier to learn the features of reaction conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search. The efficacy and transferability of this approach were tested using four distinct benchmarks comprising over three hundred individual reactions. Neglecting conformer contributions led to qualitatively incorrect activation energy estimations for a broad range of reactions, whereas simple random forest classifiers reliably down-selected low-barrier reaction conformers for unseen reactions. The robust performance of these machine learning classifiers mitigates cost as a factor when implementing conformational sampling into contemporary reaction prediction workflows and opens up many avenues for further improvements as transition state data grow.

Deep reaction network exploration at a heterogeneous catalytic interface.

Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.

MOF-derived RuCoP nanoparticles-embedded nitrogen-doped polyhedron carbon composite for enhanced water splitting in alkaline media.

Water splitting is considered as a promising candidate for renewable and sustainable energy systems, while developing efficient, inexpensive and robust bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) still remains a challenge. Herein, the well-designed RuCoP nanoparticles embedded in nitrogen-doped polyhedron carbon (RuCoP@CN) composite is fabricated by in-situ carbonization of Co based zeolitic imidazolate framework (ZIF-67) and phosphorization. Ru-substituted phosphate is proved to be imperative for the electrochemical activity and stability of individual catalysts, which can efficiently yield the active electronic states and promote the intrinsic OER and HER activity. As a result, a current density of 10 mA cm-2 is achieved at a cell voltage as low as 1.60 V when the RuCoP@CN electrocatalyst applied for the overall water splitting, which is superior to the reported RuO2 and Pt/C couple electrode (1.64 V). The density functional theory (DFT) calculations reveal that the introduction of Ru and P atoms increase the electronic states of Co d-orbital near the Fermi level, decreasing the free energy of the hydrogen adsorption and H2O dissociation for HER and the rate-limiting step for OER in alkaline media.