Biocatalysis is a rapidly evolving field with increasing impact in organic synthesis, chemical manufacturing and medicine. The Faraday Discussion reflected the current state of biocatalysis, covering the design of de novo enzymatic activities, but especially methods for the improvement of enzymes targeting a broad range of applications (i.e., hydroxylations by P450 monooxygenases, enzymatic deprotection of organic compounds under mild conditions, synthesis of chiral intermediates, plastic degradation, silicone polymer synthesis, and peptide synthesis). Central themes have been how to improve an enzyme using methods of rational design and directed evolution, informed by computer modelling and machine learning, and the incorporation of new catalytic functionalities to create hybrid and artificial enzymes.
{"title":"Concluding remarks: biocatalysis.","authors":"Uwe T Bornscheuer","doi":"10.1039/d4fd00127c","DOIUrl":"https://doi.org/10.1039/d4fd00127c","url":null,"abstract":"<p><p>Biocatalysis is a rapidly evolving field with increasing impact in organic synthesis, chemical manufacturing and medicine. The <i>Faraday Discussion</i> reflected the current state of biocatalysis, covering the design of <i>de novo</i> enzymatic activities, but especially methods for the improvement of enzymes targeting a broad range of applications (<i>i.e.</i>, hydroxylations by P450 monooxygenases, enzymatic deprotection of organic compounds under mild conditions, synthesis of chiral intermediates, plastic degradation, silicone polymer synthesis, and peptide synthesis). Central themes have been how to improve an enzyme using methods of rational design and directed evolution, informed by computer modelling and machine learning, and the incorporation of new catalytic functionalities to create hybrid and artificial enzymes.</p>","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141490043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electrochemical leaf enables the electrification and control of multi-enzyme cascades by exploiting two discoveries: (i) the ability to electrify the photosynthetic enzyme ferredoxin NADP+ reductase (FNR), driving it to catalyse the interconversion of NADP+/NADPH whilst it is entrapped in a highly porous, metal oxide electrode, and (ii) the evidence that additional enzymes can be co-entrapped in the electrode pores where, through one NADP(H)-dependent enzyme, extended cascades can be driven by electrical connection to FNR, via NADP(H) recycling. By changing a critical active-site tyrosine to serine, FNR's exclusivity for NADP(H) is swapped for unphosphorylated NAD(H). Here we present an electrochemical study of this variant FNR, and show that in addition to the intended inversion of cofactor preference, this change to the active site has altered FNR's tuning of the flavin reduction potential, making it less reductive. Exploiting the ability to monitor the variant's activity with NADP(H) as a function of potential has revealed a trapped intermediate state, relieved only by applying a negative overpotential, which allows catalysis to proceed. Inhibition by NADP+ (very tightly bound) with respect to NAD(H) turnover was also revealed and interestingly, this inhibition changes depending on the applied potential. These findings are of critical importance for future exploitation of the electrochemical leaf.
{"title":"Retuning the potential of the electrochemical leaf.","authors":"Marta M Dolińska, Adam J Kirwan, Clare F Megarity","doi":"10.1039/d4fd00020j","DOIUrl":"https://doi.org/10.1039/d4fd00020j","url":null,"abstract":"<p><p>The electrochemical leaf enables the electrification and control of multi-enzyme cascades by exploiting two discoveries: (i) the ability to electrify the photosynthetic enzyme ferredoxin NADP<sup>+</sup> reductase (FNR), driving it to catalyse the interconversion of NADP<sup>+</sup>/NADPH whilst it is entrapped in a highly porous, metal oxide electrode, and (ii) the evidence that additional enzymes can be co-entrapped in the electrode pores where, through one NADP(H)-dependent enzyme, extended cascades can be driven by electrical connection to FNR, <i>via</i> NADP(H) recycling. By changing a critical active-site tyrosine to serine, FNR's exclusivity for NADP(H) is swapped for unphosphorylated NAD(H). Here we present an electrochemical study of this variant FNR, and show that in addition to the intended inversion of cofactor preference, this change to the active site has altered FNR's tuning of the flavin reduction potential, making it less reductive. Exploiting the ability to monitor the variant's activity with NADP(H) as a function of potential has revealed a trapped intermediate state, relieved only by applying a negative overpotential, which allows catalysis to proceed. Inhibition by NADP<sup>+</sup> (very tightly bound) with respect to NAD(H) turnover was also revealed and interestingly, this inhibition changes depending on the applied potential. These findings are of critical importance for future exploitation of the electrochemical leaf.</p>","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141287424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anushree Mondal, Pronay Roy, Jaclyn Carrannanto, Prathamesh M Datar, Daniel J DiRocco, Katherine Hunter, E Neil G Marsh
The prenylated-flavin mononucleotide-dependent decarboxylases (also known as UbiD-like enzymes) are the most recently discovered family of decarboxylases. The modified flavin facilitates the decarboxylation of unsaturated carboxylic acids through a novel mechanism involving 1,3-dipolar cyclo-addition chemistry. UbiD-like enzymes have attracted considerable interest for biocatalysis applications due to their ability to catalyse (de)carboxylation reactions on a broad range of aromatic substrates at otherwise unreactive carbon centres. There are now ∼35 000 protein sequences annotated as hypothetical UbiD-like enzymes. Sequence similarity network analyses of the UbiD protein family suggests that there are likely dozens of distinct decarboxylase enzymes represented within this family. Furthermore, many of the enzymes so far characterized can decarboxylate a broad range of substrates. Here we describe a strategy to identify potential substrates of UbiD-like enzymes based on detecting enzyme-catalysed solvent deuterium exchange into potential substrates. Using ferulic acid decarboxylase (FDC) as a model system, we tested a diverse range of aromatic and heterocyclic molecules for their ability to undergo enzyme-catalysed H/D exchange in deuterated buffer. We found that FDC catalyses H/D exchange, albeit at generally very low levels, into a wide range of small, aromatic molecules that have little resemblance to its physiological substrate. In contrast, the sub-set of aromatic carboxylic acids that are substrates for FDC-catalysed decarboxylation is much smaller. We discuss the implications of these findings for screening uncharacterized UbiD-like enzymes for novel (de)carboxylase activity.
{"title":"Surveying the scope of aromatic decarboxylations catalyzed by prenylated-flavin dependent enzymes.","authors":"Anushree Mondal, Pronay Roy, Jaclyn Carrannanto, Prathamesh M Datar, Daniel J DiRocco, Katherine Hunter, E Neil G Marsh","doi":"10.1039/d4fd00006d","DOIUrl":"10.1039/d4fd00006d","url":null,"abstract":"<p><p>The prenylated-flavin mononucleotide-dependent decarboxylases (also known as UbiD-like enzymes) are the most recently discovered family of decarboxylases. The modified flavin facilitates the decarboxylation of unsaturated carboxylic acids through a novel mechanism involving 1,3-dipolar cyclo-addition chemistry. UbiD-like enzymes have attracted considerable interest for biocatalysis applications due to their ability to catalyse (de)carboxylation reactions on a broad range of aromatic substrates at otherwise unreactive carbon centres. There are now ∼35 000 protein sequences annotated as hypothetical UbiD-like enzymes. Sequence similarity network analyses of the UbiD protein family suggests that there are likely dozens of distinct decarboxylase enzymes represented within this family. Furthermore, many of the enzymes so far characterized can decarboxylate a broad range of substrates. Here we describe a strategy to identify potential substrates of UbiD-like enzymes based on detecting enzyme-catalysed solvent deuterium exchange into potential substrates. Using ferulic acid decarboxylase (FDC) as a model system, we tested a diverse range of aromatic and heterocyclic molecules for their ability to undergo enzyme-catalysed H/D exchange in deuterated buffer. We found that FDC catalyses H/D exchange, albeit at generally very low levels, into a wide range of small, aromatic molecules that have little resemblance to its physiological substrate. In contrast, the sub-set of aromatic carboxylic acids that are substrates for FDC-catalysed decarboxylation is much smaller. We discuss the implications of these findings for screening uncharacterized UbiD-like enzymes for novel (de)carboxylase activity.</p>","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141247676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data-driven techniques for establishing quantitative structure property relations are a pillar of modern materials and molecular discovery. Fuelled by the recent progress in deep learning methodology and the abundance of new algorithms, it is tempting to chase benchmarks and incrementally build ever more capable machine learning (ML) models. While model evaluation has made significant progress, the intrinsic limitations arising from the underlying experimental data are often overlooked. In the chemical sciences data collection is costly, thus datasets are small and experimental errors can be significant. These limitations of such datasets affect their predictive power, a fact that is rarely considered in a quantitative way. In this study, we analyse commonly used ML datasets for regression and classification from drug discovery, molecular discovery, and materials discovery. We derived maximum and realistic performance bounds for nine such datasets by introducing noise based on estimated or actual experimental errors. We then compared the estimated performance bounds to the reported performance of leading ML models in the literature. Out of the nine datasets and corresponding ML models considered, four were identified to have reached or surpassed dataset performance limitations and thus, they may potentially be fitting noise. More generally, we systematically examine how data range, the magnitude of experimental error, and the number of data points influence dataset performance bounds. Alongside this paper, we release the Python package NoiseEstimator and provide a web- based application for computing realistic performance bounds. This study and the resulting tools will help practitioners in the field understand the limitations of datasets and set realistic expectations for ML model performance. This work stands as a reference point, offering analysis and tools to guide development of future ML models in the chemical sciences.
建立定量结构属性关系的数据驱动技术是现代材料和分子发现的支柱。近年来，深度学习方法论取得了长足进步，新算法层出不穷，因此，追逐基准并逐步建立能力更强的机器学习（ML）模型很有诱惑力。虽然模型评估已经取得了重大进展，但底层实验数据带来的内在局限性往往被忽视。在化学科学领域，数据收集成本很高，因此数据集很小，实验误差可能很大。这些数据集的局限性影响了它们的预测能力，而这一事实却很少得到定量考虑。在本研究中，我们分析了药物发现、分子发现和材料发现中用于回归和分类的常用 ML 数据集。通过引入基于估计或实际实验误差的噪声，我们得出了九个此类数据集的最大和实际性能界限。然后，我们将估计的性能边界与文献中报道的主要 ML 模型的性能进行了比较。在考虑的九个数据集和相应的 ML 模型中，我们发现有四个已经达到或超过了数据集的性能限制，因此，它们有可能是拟合噪声。更广泛地说，我们系统地研究了数据范围、实验误差的大小和数据点的数量如何影响数据集的性能界限。在发表这篇论文的同时，我们还发布了 Python 软件包 NoiseEstimator，并提供了一个基于网络的应用程序，用于计算现实的性能边界。这项研究和由此产生的工具将帮助该领域的从业人员了解数据集的局限性，并对 ML 模型的性能设定切合实际的期望值。这项工作可作为一个参考点，为指导化学科学领域未来 ML 模型的开发提供分析和工具。
{"title":"Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.","authors":"Daniel Crusius, Flaviu Cipcigan, Philip Biggin","doi":"10.1039/d4fd00091a","DOIUrl":"https://doi.org/10.1039/d4fd00091a","url":null,"abstract":"Data-driven techniques for establishing quantitative structure property relations are a pillar of modern materials and molecular discovery. Fuelled by the recent progress in deep learning methodology and the abundance of new algorithms, it is tempting to chase benchmarks and incrementally build ever more capable machine learning (ML) models. While model evaluation has made significant progress, the intrinsic limitations arising from the underlying experimental data are often overlooked. In the chemical sciences data collection is costly, thus datasets are small and experimental errors can be significant. These limitations of such datasets affect their predictive power, a fact that is rarely considered in a quantitative way. In this study, we analyse commonly used ML datasets for regression and classification from drug discovery, molecular discovery, and materials discovery. We derived maximum and realistic performance bounds for nine such datasets by introducing noise based on estimated or actual experimental errors. We then compared the estimated performance bounds to the reported performance of leading ML models in the literature. Out of the nine datasets and corresponding ML models considered, four were identified to have reached or surpassed dataset performance limitations and thus, they may potentially be fitting noise. More generally, we systematically examine how data range, the magnitude of experimental error, and the number of data points influence dataset performance bounds. Alongside this paper, we release the Python package NoiseEstimator and provide a web- based application for computing realistic performance bounds. This study and the resulting tools will help practitioners in the field understand the limitations of datasets and set realistic expectations for ML model performance. This work stands as a reference point, offering analysis and tools to guide development of future ML models in the chemical sciences.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew S. Emerson, Raphael Ogbodo, Claudio Javier Margulis
The structure of ionic liquids (ILs), which a decade or two ago was the subject of polite but heated debate, is now much better understood. This has opened opportunities to ask more sophisticated questions about the role of structure in transport, the structure of systems with ions that are not prototypical, and the similarity between ILs and other dense ionic fluids such as the high-temperature inorganic molten salts; let alone the fact that new areas of research have emerged that sprung from our collective understanding of the structure of ILs such as the deep eutectic solvents, the polymerized ionic liquids, and the zwitterionic liquids. Yet, our understanding of the structure of prototypical ILs may not be as complete as we think it to be, given that recent experiments appear to show that in some cases there may be more than one liquid phase resulting in liquid-liquid (L-L) phase transitions. This article presents a perspective on what we think are key topics related to the structure and structural dynamics of ILs and to some extent high-temperature molten salts.
{"title":"Spiers Memorial Lecture: From Cold to Hot, The Structure and Structural Dynamics of Dense Ionic Fluids","authors":"Matthew S. Emerson, Raphael Ogbodo, Claudio Javier Margulis","doi":"10.1039/d4fd00086b","DOIUrl":"https://doi.org/10.1039/d4fd00086b","url":null,"abstract":"The structure of ionic liquids (ILs), which a decade or two ago was the subject of polite but heated debate, is now much better understood. This has opened opportunities to ask more sophisticated questions about the role of structure in transport, the structure of systems with ions that are not prototypical, and the similarity between ILs and other dense ionic fluids such as the high-temperature inorganic molten salts; let alone the fact that new areas of research have emerged that sprung from our collective understanding of the structure of ILs such as the deep eutectic solvents, the polymerized ionic liquids, and the zwitterionic liquids. Yet, our understanding of the structure of prototypical ILs may not be as complete as we think it to be, given that recent experiments appear to show that in some cases there may be more than one liquid phase resulting in liquid-liquid (L-L) phase transitions. This article presents a perspective on what we think are key topics related to the structure and structural dynamics of ILs and to some extent high-temperature molten salts.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Taiana L.E. Pereira, Jon Serrano-Sevillano, Beatriz Diaz Moreno, Joel Reid, Dany Carlier, Gillian Goward
In our recent study, we demonstrated using 7Li solid-state Nuclear Magnetic Resonance (ssNMR) and single-crystal X-ray diffraction, that the cathode LiFeV2O7 possesses a defect associated with the positioning of vanadium atoms. We proposed that this defect could be the source of extra signals detected in the 7Li NMR spectra. In this context, we now apply density functional theory (DFT) calculations to assign the experimental signals observed in 7Li NMR spectra of the pristine sample. The calculation results are in strong agreement with the experimental observations. DFT calculations are a useful tool to interpret the observed paramagnetic shifts and understand how the presence of disorder affects the spectra behavior through the spin-density transfer processes. Furthermore, we conducted a detailed study of the lithiated phase combining operando synchrotron powder X-ray diffraction (SPXRD) and DFT calculations. A noticeable volume expansion is observed through the first discharge cycle which likely contributes to the enhanced lithium dynamics in the bulk material, as supported by previously published ssNMR data. DFT calculations are used to model the lithiated phase and demonstrate that both iron and vanadium participate in the redox process. The unusual electronic structure of the V4+ -exhibits a single electron on the 3dxy orbital perpendicular to the V-O-Li bond being a source of a negative Fermi contact shift observed in the 7Li NMR of the lithiated phase.
{"title":"A Combined 7Li NMR, Density Functional Theory and Operando Synchrotron X-Ray Powder Diffraction to Investigate a Structural Evolution of Cathode Material LiFeV2O7","authors":"Taiana L.E. Pereira, Jon Serrano-Sevillano, Beatriz Diaz Moreno, Joel Reid, Dany Carlier, Gillian Goward","doi":"10.1039/d4fd00077c","DOIUrl":"https://doi.org/10.1039/d4fd00077c","url":null,"abstract":"In our recent study, we demonstrated using <small><sup>7</sup></small>Li solid-state Nuclear Magnetic Resonance (ssNMR) and single-crystal X-ray diffraction, that the cathode LiFeV<small><sub>2</sub></small>O<small><sub>7</sub></small> possesses a defect associated with the positioning of vanadium atoms. We proposed that this defect could be the source of extra signals detected in the <small><sup>7</sup></small>Li NMR spectra. In this context, we now apply density functional theory (DFT) calculations to assign the experimental signals observed in 7Li NMR spectra of the pristine sample. The calculation results are in strong agreement with the experimental observations. DFT calculations are a useful tool to interpret the observed paramagnetic shifts and understand how the presence of disorder affects the spectra behavior through the spin-density transfer processes. Furthermore, we conducted a detailed study of the lithiated phase combining operando synchrotron powder X-ray diffraction (SPXRD) and DFT calculations. A noticeable volume expansion is observed through the first discharge cycle which likely contributes to the enhanced lithium dynamics in the bulk material, as supported by previously published ssNMR data. DFT calculations are used to model the lithiated phase and demonstrate that both iron and vanadium participate in the redox process. The unusual electronic structure of the V<small><sup>4+</sup></small> -exhibits a single electron on the 3d<small><sub>xy</sub></small> orbital perpendicular to the V-O-Li bond being a source of a negative Fermi contact shift observed in the <small><sup>7</sup></small>Li NMR of the lithiated phase.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Taylor, Patrick Butler, Graeme Matthew Day
Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure reoptimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.
{"title":"Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes","authors":"Christopher Taylor, Patrick Butler, Graeme Matthew Day","doi":"10.1039/d4fd00105b","DOIUrl":"https://doi.org/10.1039/d4fd00105b","url":null,"abstract":"Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4% of observed experimental structures, and ranking a large majority of these (74%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure reoptimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marija Blazic, Candice Gautier, Thomas Norberg, Mikael Widersten
Epoxide hydrolase StEH1, from potato, is similar in overall structural fold and catalytic mechanism to haloalkane dehalogenase DhlA from Xanthobacter autotrophicus. StEH1 displays low (promiscuous) hydrolytic activity with (2-chloro)- and (2-bromo)ethanebenzene producing 2-phenylethanol. To investigate possibilities to amplify these very low dehalogenase activities, StEH1 was subjected to targeted randomized mutagenesis at five active-site amino acid residues and the resulting protein library was challenged for reactivity towards a bait chloride substrate. Enzymes catalyzing the first half-reaction of a hydrolytic cycle were isolated following monovalent phage display of the mutated proteins. Several StEH1 derived enzymes were identified with enhanced dehalogenase activities.
{"title":"High-throughput selection of (new) enzymes: phage display-mediated isolation of alkyl halide hydrolases from a library of active-site mutated epoxide hydrolases.","authors":"Marija Blazic, Candice Gautier, Thomas Norberg, Mikael Widersten","doi":"10.1039/d4fd00001c","DOIUrl":"https://doi.org/10.1039/d4fd00001c","url":null,"abstract":"<p><p>Epoxide hydrolase StEH1, from potato, is similar in overall structural fold and catalytic mechanism to haloalkane dehalogenase DhlA from <i>Xanthobacter autotrophicus</i>. StEH1 displays low (promiscuous) hydrolytic activity with (2-chloro)- and (2-bromo)ethanebenzene producing 2-phenylethanol. To investigate possibilities to amplify these very low dehalogenase activities, StEH1 was subjected to targeted randomized mutagenesis at five active-site amino acid residues and the resulting protein library was challenged for reactivity towards a bait chloride substrate. Enzymes catalyzing the first half-reaction of a hydrolytic cycle were isolated following monovalent phage display of the mutated proteins. Several StEH1 derived enzymes were identified with enhanced dehalogenase activities.</p>","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141199017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lei Chen, Carlos Bornes, Oscar Bengtsson, Andreas Erlebach, Ben Slater, Lukáš Grajciar, Christopher J. Heard
One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the 23Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the 23Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Sin-Al sequence length. Furthermore, neglect of thermal and dynamical contributions were found to lead to errors of several ppm, and have a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for 23Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.
{"title":"A machine learning approach for dynamical modelling of Al distributions in zeolites via 23Na/27Al solid-state NMR","authors":"Lei Chen, Carlos Bornes, Oscar Bengtsson, Andreas Erlebach, Ben Slater, Lukáš Grajciar, Christopher J. Heard","doi":"10.1039/d4fd00100a","DOIUrl":"https://doi.org/10.1039/d4fd00100a","url":null,"abstract":"One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the <small><sup>23</sup></small>Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the <small><sup>23</sup></small>Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Si<small><sub>n</sub></small>-Al sequence length. Furthermore, neglect of thermal and dynamical contributions were found to lead to errors of several ppm, and have a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for <small><sup>23</sup></small>Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin
The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.
对分布函数（PDF）是表征复杂材料结构的重要指标，但众所周知，有意义的不同结构模型有时会产生等效的 PDF。在本文中，我们将讨论如何使用模型似然值作为区分此类等效结构解的一般方法。通过两个主要的案例研究--一个是关于小肽的结构，另一个是关于无定形碳酸钙--我们展示了即使在 PDF 信息特别贫乏的情况下，考虑模型似然性如何有助于推动稳健的结构求解。这些单独案例研究的明显线索是机器学习方法在帮助指导 PDF 结构确定方面的潜在作用，我们的论文最后沿着这些线索进行了一些前瞻性讨论。
{"title":"When can we trust structural models derived from pair distribution function measurements?","authors":"Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin","doi":"10.1039/d4fd00106k","DOIUrl":"https://doi.org/10.1039/d4fd00106k","url":null,"abstract":"The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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