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Author: Kayla Benson

Bailey Lab Published in ChemBioChem

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The Bailey Lab also published “Site Directed Mutagenesis of Modular Polketide Synthase Ketoreductase Domains for Altered Stereochemical Control” in ChemBioChem.

Bacterial modular type I polyketide synthases (PKSs) are complex multidomain assembly line proteins that produce a range of pharmaceutically relevant molecules with a high degree of stereochemical control. Due to their colinear properties, they have been considerable targets for rational biosynthetic pathway engineering. Among the domains harbored within these complex assembly lines, ketoreductase (KR) domains have been extensively studied with the goal of altering their stereoselectivity by site-directed mutagenesis, as they confer much of the stereochemical complexity present in pharmaceutically active reduced polyketide scaffolds. Here we review all efforts to date to perform site-directed mutagenesis on PKS KRs, most of which have been done in the context of excised KR domains on model diffusible substrates such as beta-keto N-acetyl cysteamine thioesters. We also discuss the challenges around translating the findings of these studies to alter stereocontrol in the context of a complex multidomain enzymatic assembly line.

Computational Chemistry and Machine Learning in the Vogiatzis Group

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Research in the Vogiatzis Group centers on the development of computational methods based on electronic structure theory and machine learning algorithms for describing chemical systems relevant to clean, green technologies.

“We are particularly interested in new methods for non-covalent interactions and bond-breaking reactions of small molecules with transition metals,” Vogiatzis said. “Our overall objectives are to elucidate the fundamental physical principles underlying the reactivity and properties of molecules and materials, as well as to assist in the interpretation of experimental data.”

In June 2020, the group was published in Nature Communications for their work “Representation of molecular structures with persistent homology for machine learning applications in chemistry.” This was a unique collaborative opportunity between chemistry department’s Jacob Townsend, graduate student, John Hymel, undergraduate student, Konstantinos Vogiatzis, assistant professor, along with Cassie Micucci and Vasileios Maroulas, Department of Mathematics. The group presents a novel molecular representation method based on persistent homology, an applied branch of topology, which encodes the atomistic structure of molecules.

They began their study by computing with density functional theory (DFT) the CO2 interaction energies of 100 organic molecules. “Since the initial, limited 100 data points were not capturing the diversity of the GDB-9 database, we applied a technique called active learning in order to incrementally obtain data which helped us efficiently screen the 133,885 molecules,” Vogiatzis said. “We found out that the combination of PIs with active learning performed well with data (interaction energies) from only 220 molecules in order to identify new molecules with stronger CO2 binding.”

Their data-driven methodology was able to identify molecular patterns previously unknown to us that increase the CO2 affinity of organic molecules.

The Vogiatzis Group broke a record with their work “Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies.” 

Machine learning methods have enabled the low-cost evaluation of molecular properties such as energy at an unprecedented scale. While many of such applications have focused on molecular input based on geometry, few studies consider representations based on the underlying electronic structure.

Directing the attention to the electronic structure offers a unique challenge that allows for a more detailed representation of the underlying physics and how they affect molecular properties. The target of this work is to efficiently encode a lower-cost correlated wave function derived from MP2 to predict a higher-cost coupled-cluster singles-and-doubles (CCSD) wave function based on correlation-pair energies and the contributing electron promotions (excitations) and integrals.

The new molecular representation explores the short-range behavior of electron correlation and utilizes distinct models that differentiate between two-electron promotions from the same molecular orbital or from two different orbitals. The group presents a re-engineered set of input features that provide an intuitive description of the orbital properties involved in electron correlation. The overall models are found to be highly transferable and size extensive, necessitating very few training instances to approach the chemical accuracy of a broad spectrum of organic molecules.

“Coupled-cluster theory is the level of theory that provides the most accurate quantum chemical results in a reasonable computational time. Typically, we need ~10 minutes for computing the energy of a small molecule with coupled-cluster and for a database with ~133,000 small molecules, we will need ~1,330,000 minutes or ~2.5 years of computations,” Vogiatzis said. “In this work, we demonstrated that we can use the results from only 100 coupled-cluster calculations for training a machine learning model that can predict, without loss of accuracy, the energy of the full 133,000 molecule database a few hours.”

 

 

 

Larese Group Featured Cover on J. Chem. Phys. C

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The Larese Group’s research Adsorption of Pentane and Hexane Thin Films on the Surface of Graphite(0001) was featured on the cover of The Journal of Physical Chemistry C.

Surface adsorption plays an important role in a variety of industrial and technological processes, especially in energy conversion, storage, and transformation. As a result, there is a growing need for advancing the current understanding of fundamental interactions that govern these types of processes. 

This research characterizes the interaction of n-pentane and n-hexane with graphite using high-resolution volumetric adsorption isotherms along with molecular dynamic simulations. The thermodynamics of adsorption were obtained for n-pentane and n-hexane adsorbed on the basal plane of graphite in the temperature ranges 190–235 and 230–280 K, respectively, using high-resolution volumetric adsorption isotherms. These linear molecules exhibit a van der Waals interaction with the surface of graphite(0001) and yield an overall greater binding than on boron nitride and MgO(100).

The averaged areas per molecule calculated for the fluid monolayer phases were determined to be 52.03 and 61.35 Å2 for pentane and hexane, respectively, which are in agreement with previous diffraction measurements performed for the monolayer solid. MD simulations were performed in order to provide additional microscopic insight.

Density profiles normal to the graphite substrate revealed that the stabilization of the layer nearest to the surface by the fluid multilayer exists for linear alkanes as small as pentane, however to a much lesser extent than that observed previously for adsorption on boron nitride and MgO. Intermolecular radial distribution functions and diffusion coefficients derived from the molecular trajectories suggest that a liquid crystal phase exists in the layer nearest the surface at temperatures well above the bulk triple-point temperatures.

Dai Group Published in ACS Energy Letters

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The Dai group published their research “Surpassing the Organic Cathode Performance for Lithium-Ion Batteries with Robust Fluorinated Covalent Quinazoline Networks” in  ACS Energy Letters.

Organic electrode materials have promising application prospects in energy storage, but issues including rapid capacity fading and poor power capacity restrict their practical applications. Herein, nanoporous fluorinated covalent quinazoline networks (F-CQNs) were constructed by condensation of fluorinated aromatic aminonitrile precursors via an ionothermal pathway.

Precise control of the reaction parameters afforded F-CQN-1-600 material featuring high surface area, permanent porosity, high nitrogen content (23.49 wt %), extended π-conjugated architecture, layered structure, and bipolar combination of benzene and tricycloquinazoline. Synergy among these unique properties leads to a good performance as a cathode source for lithium-ion batteries (LIBs) in terms of high capacity (250 mA h g–1 at 0.1 A g–1), high rate capability (105 mA h g–1 at 5.0 A g–1), and impressive cycling stability (95.8% retention rate after 2000 cycles at 2.0 A g–1 together with a high Coulombic efficiency of 99.95%), surpassing most of the previous organic cathode counterparts

Dai Published in Chem

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In a collaborative piece, UT Chemistry’s Sheng Dai and Pasquale Fulvio from Texas A&M’s Department of Nuclear Engineering published their work “Porous Liquids: The Next Frontier” in Chem.

Porous liquids are a new class of molecular- and colloidal-size porous materials that combine permanent porosity of solid sorbents and fluid properties of liquids. Different from transient molecular clathrates, porous liquids have the potential to reinvent materials syntheses and unify homogeneous and heterogeneous separations and catalytic and energy-related processes, previously ascribed to liquids and porous solids, respectively.

Surface areas and pore volumes of the first examples of porous liquids based on porous molecular organic cages restricted their potential for technological applications. Recent advances in ionic liquid-based colloidal suspensions or covalently stabilized nanocomposites have improved the adsorption properties and increased our ability to tailor chemical composition and pore architecture. These hybrid porous liquids, however, still present challenges such as high melting temperatures, density, and viscosity.

This critical review discusses these challenges and presents opportunities for selected emerging applications based on analogous structure to that of traditional colloidal systems.

Darko Lab Published in Dalton Transactions

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The Darko Lab published their work “Tuning Rh(ii)-catalysed cyclopropanation with tethered thioether ligands” in Dalton Transactions. 

Dirhodium(II) paddlewheel complexes have high utility in diazo-mediated cyclopropanation reactions and ethyl diazoacetate is one of the most commonly used diazo compounds in this reaction. In this study, the lab reports efforts to use tethered thioether ligands to tune the reactivity of RhII-carbene mediated cyclopropanation of olefins with ethyl diazoacetate.

Microwave methods enabled the synthesis of a family of RhII complexes in which tethered thioether moieties were coordinated to axial sites of the complex. Different tether lengths and thioether substituents were screened to optimise cyclopropane yields and minimise side product formation.

Good yields were obtained when equimolar diazo and olefin were used. Structural and spectroscopic investigation revealed that tethered thioethers changed the electronic structure of the rhodium core, which was instrumental in the performance of the catalysts. Computational modelling of the catalysts provided further support that the tethered thioethers were responsible for increased yields.

Vogiatzis Group Published in JCTC

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Electronic structure theory describes the motions of electrons in atoms or molecules, and provides a versatile framework for the calculation of molecular geometries, chemical bonding, electronic and spectroscopic properties, reaction barriers, intermolecular interactions, and more. Wave function theory-based methods such as coupled-cluster methods, provide accurate results in a systematic manner, but they typically carry a significant computational cost.

The Vogiatzis group uses machine learning, the field of study allowing computers to learn without explicit programming, to provide novel approaches on the learning of the underlying electronic structure while subverting a significant portion of the computational expense. In a recent article published in the Journal of Chemical Theory and Computation, Jacob Townsend, under the supervision of Kostas Vogiatzis, presents a new efficient methodology that explores the local nature of the correlated motion of electrons which offers scalability and transferability between different chemical systems.

The novel approach provides coupled-cluster quality electronic energies at the cost of second-order perturbation theory (MP2), a computationally more affordable method. As a result, the authors were able to predict energies of a large molecular database, known as the GDB-9 dataset, at high accuracy at Jacob Townsenda fraction of the cost. Additionally, it was shown that the introduced method could be used to accurately predict energies of large chemical systems based on models trained on smaller ones.

Townsend recently received his PhD in the chemistry doctoral program at UT.

Dai 2020 Highly Cited Researcher

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Each year, Clarivate™ identifies the world’s most influential researchers ─ the select few who have been most frequently cited by their peers over the last decade. In 2020, fewer than 6,200, or about 0.1%, of the world’s researchers, in 21 research fields and across multiple fields, have earned this exclusive distinction.
Sheng Dai Sheng Dai is among this elite group recognized for exceptional research influence, demonstrated by the production of multiple highly-cited papers that rank in the top 1% by citations for field and year in the Web of Science™.

2020 BOV Meeting Held Virtually

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The annual BOV meeting will be held virtually on November 6, 2020. 

The BOV is a volunteer advisory body dedicated to helping the Department successfully fulfill its teaching, research and service missions and become one of the preeminent chemistry departments in the nation.

The BOV has a vision of enriching the research and teaching endeavors and the intellectual capital of the Department.

Membership on the Board of Visitors is one of the highest honors that the Department can bestow upon its supporters. The professional experience and perspectives represented collectively in the members of the Board is of great value to the department in helping achieve its missions and guide its future directions.

View Schedule Here

View Research Competition  Program

 

Filed Under: BOV

Musfeldt Group Published in Nature Communications

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The Musfeldt group published their work “Site-specific spectroscopic measurement of spin and charge in (LuFeO3)m/(LuFe2O4)1 multiferroic superlattices” in a collaborative piece in Nature Communications.

Interface materials offer a means to achieve electrical control of ferrimagnetism at room temperature as was recently demonstrated in (LuFeO3)m/(LuFe2O4)1 superlattices. A challenge to understanding the inner workings of these complex magnetoelectric multiferroics is the multitude of distinct Fe centres and their associated environments. This is because macroscopic techniques characterize average responses rather than the role of individual iron centres.

In this article, researchers combine optical absorption, magnetic circular dichroism and first-principles calculations to uncover the origin of high-temperature magnetism in these superlattices and the charge-ordering pattern in the m = 3 member. In a significant conceptual advance, interface spectra establish how Lu-layer distortion selectively enhances the Fe2+ →  Fe3+ charge-transfer contribution in the spin-up channel, strengthens the exchange interactions and increases the Curie temperature.

Comparison of predicted and measured spectra also identifies a non-polar charge ordering arrangement in the LuFe2O4 layer. This site-specific spectroscopic approach opens the door to understanding engineered materials with multiple metal centres and strong entanglement.