Category: Chemistry

Alexis Delgado: Unusually Long C-C Bonds

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Co-authors: Alan Humason, Robert Kalescky, Marek Freindorf, and Elfi Kraka

https://youtu.be/bbkQbnUKqMo

For decades chemists have attempted to create a molecule with the longest covalent C-C bond possible. This has typically been done through steric effects or molecular strain. More recently the diamino-o-carborane analog, di-N,N-dimethylamino-o-carborane, has been observed to have a unusually long C-C bond which has been attributed to negative hyperconjugation effects (i.e.charge transfers). In our work we computationally analyzed the C-C bonds for a unique set of 53 molecules including clamped bonds, highly sterically strained complexes, electron deficient species, and the di-N,N-dimethylamino-o-carborane molecule in order to consider all routes for obtaining the longest C-C bond. We derive local vibrational stretching force constants for targeted C-C bonds to quantify the intrinsic strengths of these bonds. Our systematic study quantifies for the first time that whereas steric hindrance and/or strain definitely elongate a C-C bond, electronic effects can lead to even longer and weaker C-C bonds. Within our set of molecules the electron deficient ethane radical cation, in D3d symmetry, acquires the longest C-C bond with a length of 1.935 angstroms followed by di-N,N-dimethylamino-o-carborane with a bond length of 1.930 angstroms. However, the C-C bond in di-N,N-dimethylamino-o-carborane is the weakest compared the ethane radical cation; revealing that the longer bond is n

Alexis Delgado
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Elfi Kraka

Uroob Haris: Light-Mediated Microprinting

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https://youtu.be/QE7l6JbfxCk

Techniques for nanofabrication and high resolution lithography are increasingly in demand owing to applications of nanomaterials in technology and therapeutics. This poster will cover research towards a microprinting technique that combines chemistry with structured light patterning to achieve micrometer resolution pattern printing on solid surfaces.

Uroob Haris
Program: Chemistry
Faculty Mentor: Alex Lippert

Matthew Heaney (U): Are azo pigments really azo pigments? Structural and spectroscopic characterization of β-naphthol reds

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https://youtu.be/-mYpdUdu15o

Naphthol reds are a group of widely used pigments with prominent historical, commercial, and cultural significance and are frequently characterized as part of a wider group called azo pigments. Azo pigments have two fundamental properties, they have the vibrant colors and low solubilities characteristic of pigments, and the presence of an azo group within their structure. The latter property, however, as other studies have found is oftentimes completely unfounded. Some of these pigments do not even possess azo groups at all, rather they possess a similar, but fundamentally different group known as a hydrazone group. In other cases these pigments can possess both the azo and hydrazone forms at the same time or undergo an enol/keto tautomeric shift under certain conditions. There are however still many common pigments which are still marketed and referred to as azo pigments with frequently little to no evidence. In this study by using the capabilities afforded to us by X-ray diffraction techniques as well as Rietveld refinement it was possible to determine not only the presence of the hydrazone group but also to elucidate the crystal structures of two pigments. Ultimately these results can fundamentally change both how we refer to these pigments from now on and how these pigments may be better used and applied in the future.

Matthew Heaney
Major: Chemistry; Minor: Geology
Faculty mentor: ‪Tomče Runčevski‬

Lisa Kim (U): Measuring Nitric Oxide Metabolites as a Biomarker of Concussion

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https://youtu.be/bHQxBQYfZRk

A concussion is a traumatic injury that affects brain function. Although sports-related concussions are so common, the physiology of concussions is still not yet fully understood. Nonetheless, several types of research are focusing on this study to explain the physiology of concussion and identify possible biological markers. Since blood vessels in the brain behave similarly to those in the other parts of the body, one can further study the brain and the concussion by measuring the blood pressure and flow. With this idea, a research study has found nitric oxide (NO) as an important biological marker for concussion, indicating either possible inhibition or stimulation of nitric oxide synthase (NOS) in our body. By incorporating this information into my project, I have performed Griess assay to measure nitrate and nitrite in blood samples extracted from SMU athletes who have experienced concussions.

Yujin Lisa Kim
Major: Biochemistry; Minor: Chinese
Faculty mentor: Alex Lippert

Bo Li: Chemiluminescent H2S and peroxynitrite probes synthesis

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Co-authors: Lucas Ryan, Briley Bezner and Husain Kagalwala

https://youtu.be/LHoCo4omytM

Hydrogen sulfide (H2S) and peroxynitrite are important biological signaling molecules that have been recognized alongside nitric oxide and carbon monoxide which could impact multiple physiological functions. To detect them, there has been a focus on developing fluorescent probes to target particular analytes; however, fluorescent probes lack sensitivity and depth penetration due to background autofluorescence and light scattering. Chemiluminescence does not require light excitation, which greatly reduces the amount of autofluorescence and photoactivation. In order to detect them in living systems with high sensitivity, a series of sterically stabilized 1,2-dioxetane chemiluminescent reduction-reaction based hydrogen sulfide probes have been synthesized.

Bo Li
Program: PhD in Chemistry
Faculty mentor: Alex Lippert

Christina McConville: Peritectic phase transition of benzene and acetonitrile into a cocrystal relevant to Titan, Saturn’s moon

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Winner: Chemistry (Graduate)

https://youtu.be/avwu36FZq4g

Titan, Saturn’s largest moon, is the only body in the solar system known to have stable bodies of liquid—lakes, rivers, and seas—that undergo dynamic processes similar to Earth’s hydrological cycle. To study the potential formation of minerals on Titan, we use a combination of structural characterization methods including high-resolution synchrotron powder X-ray diffraction (PXRD) and differential scanning calorimetry (DSC) to analyze the constituents present on Titan's surface and evaluate their potential for cocrystal formation. Among the compounds detected on the surface of Titan are two common laboratory solvents: benzene and acetonitrile. Here we report the phase diagram of mixtures of acetonitrile and benzene, which features incongruent melting and a peritectic phase transition of solid benzene and liquid acetonitrile into a 1:3 acetonitrile:benzene cocrystal. The crystal structure of this cocrystal was solved and refined from in situ diffraction data using synchrotron radiation. Additionally, to mimic the environment on Titan more accurately, we tested the stability of the structure under liquid ethane. These results provide new insights into the structure and stability of potential extraterrestrial minerals and contribute to a better understanding of the surface composition of Titan.

Christina McConville
Program: PhD in Chemistry
Faculty Mentor: Tomče Runčevski

Ankita Puri: Copper, nickel and palladium complexes bearing bidentate redox-active ligands with tunable H-bond donors and acceptors.

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https://youtu.be/r3ZCZzJshoo

In this study, we explore the structure and spectroscopic properties of a family of copper, nickel and palladium complexes bearing bidentate redox-active ligands that contain H-bonding donor and H-bonding acceptor groups. Single crystal X-ray crystallography shows intramolecular H-bonding interactions between the two ligand scaffolds can be finely tuned by different factors including metal center, ligand substitution, solvent of crystallization as well as counterion of the metal complex. Interestingly, the ability to control the intramolecular H-bonding interaction forces the complexes to adopt a certain geometry which can eventually permit to control the reactivity and catalytic performance of these complexes.

Ankita Puri
Program: PhD in Chemistry
Faculty mentor: Isaac Garcia-Bosch

Ishna Satyarth: Application of Neural Networks in Quantum Chemistry

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Winner: Computer Science (Graduate)

https://youtu.be/NcELhfqCZXo

Understanding the motion of electrons in a molecule is a big piece of the puzzle to understand the quantum world that these sub-atomic particles live in. This complexity increases several folds when correlation of pairs of electrons are interpreted in the wave function. Current methods of including pair correlation are too expensive to apply to large molecules. Our goal is to reduce the error in the Tensor Hypercontraction approximation, which can reduce the cost of such calculations. Artificial Intelligence and in particular the application of neural networks can prove to be a savior in such situations of many unknown parameters. A neural network works like a collection of brain cells where each neuron is responsible for processing a single piece of information and increasing the number of neurons allows adding complexity in a systematic way. Since there are more than 22 different input variables accounting for the energy error, we will be using a multilayer perceptron model. Using an Artificial Neural Network, we have tried to structure them into a systematic network and tried to decipher how each variable contributes towards the outcome of total energy or bond strength. We are currently working on collecting as many data points to train our Neural Network, so that we can get the most accurate results in the future.

Ishna Satyarth
Program: PhD in Computer Science
Faculty mentor: Devin Matthews (Chemistry)

Megan Simons: Transition-potential coupled cluster

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https://youtu.be/oTt_Ut-7E4o

The problem of orbital relaxation in computational core-hole spectroscopies, including x-ray absorption and x-ray photoionization, has long plagued linear response approaches, including equation-of-motion coupled cluster with singles and doubles (EOM-CCSD). Instead of addressing this problem by including additional electron correlation, we propose an explicit treatment of orbital relaxation via the use of “transition potential” reference orbitals, leading to a transition-potential coupled cluster (TP-CC) family of methods. One member of this family, in particular, TP-CCSD(12/ 1 2 ), is found to essentially eliminate the orbital relaxation error and achieve the same level of accuracy for the core-hole spectra as is typically expected of EOM-CCSD in the valence region. These results show that very accurate x-ray absorption spectra for molecules with first-row atoms can be computed at a cost essentially the same as that for EOM-CCSD.

Megan Simons
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Devin Matthews

Zilin Song: Quantifying Energy Contribution in Enzyme Catalysis using Machine-Learning based Regression Analysis

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Winner: Theoretical and Computational Chemistry (Graduate)

https://youtu.be/3IXmP6v7jRM

The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non- linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.

Zilin Song
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Hao Tian: Prediction of allosteric sites through ensemble learning

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Winner: Theoretical and Computational Chemistry (Graduate)

Co-author: Xi Jiang

https://youtu.be/qKg_rBE1UQM

Allostery is the process by which proteins transmit perturbations caused by the binding effect at one site to another distal site. Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. An interactive and quick-response server, PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), is provided to facilitate further analysis in drug discovery.

Hao Tian
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Francesco Trozzi: UMAP as Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules

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https://youtu.be/J3CfmaX6vXg

Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free energy landscapes. However, the inherently high dimensionality of a protein free energy landscape poses a challenge on the rationalization of how proteins perform their functions. For this reason. dimensionality reduction (DR) is an active field of research for molecular biologists. The Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction method based on a fuzzy topological analysis of data. In the present study, the performance of UMAP is compared to other popular dimensionality reduction methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE), Principal Component Analysis (Analysis (PCA), and time-structure Independent Components Analysis (t-ICA) in context of analyzing Molecular Dynamics simulations of the circadian clock protein Vivid. A good dimensionality reduction method should accurately represent the data structure on the projected components. The comparison of the raw high-dimensional data with the projections obtained using different DR methods, showed the superiority of UMAP compared to linear reduction methods (PCA, t-ICA) and comparable performance with t-SNE, thus far the state-of-the-art method.

Francesco Trozzi
Program: PhD in Theoretical and Computational Chemistry
Faculty mentor: Peng Tao

Thomas Truong (U): VVD and ENV Equivalent Mutant Characterization

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Co-authors: Nischal Karki, Brian Zoltowski

https://youtu.be/7wFxV3gHAoQ

Light-Oxygen-Voltage (LOV) domains, present in all kingdoms of life, facilitate regulation of light dependent events in organisms by transducing light input into physiological signals. Examination of plant and fungal circadian networks has revealed that signaling mechanisms differ even within homologs of closely related species. Structural and computational studies of LOV-allostery have identified key signaling “hot-spots” that allow modification of the direction and amplitude of signal output. One key regulatory site was identified in Plant LOV domain proteins ZTL and FKF1, in which the amino acid residue at position 46 differentiates ZTL and FKF1 signaling mechanisms. Notably, the residue at this position differentiates FKF1-based signaling in monocots and dicots (Ala and Ser, respectively). An analogous residue substitution is observed in the LOV domain photoreceptors of two closely related filamentous fungi, Neurospora crassa VIVID and Trichoderma reesei ENVOY, where divergent signaling mechanisms gate adaptation to oxidative stress. Herein, we sought to test whether residue identity at the position equivalent to ZTL G46 (VIVID A72 and ENVOY S99) alters signal transduction in VIVID and ENVOY. To propagate signal transduction of blue-light, wildtype VVD forms a rapidly dissociating dimer whereas wildtype ENV requires oxidative conditions to form an irreversible disulfide dimer. Size exclusion chromatography (SEC) characterization revealed VVD A72S exhibits reduced dimerization capability and dimerizes at high concentrations only. Furthermore, ENV S99A dimerized under reducing conditions with a similar concentration-dependent increase in dimeric fraction. This altered dimerization capacity demonstrates evolutionary selection of G46 equivalent residues in differentiating LOV-domain photoreceptors and their signal transduction mechanisms.

Thomas Truong
Majors: Biology, Management
Faculty Mentor: Brian Zoltowski

Anderson Wey (U): Synthesis and Characterization of biodegradable plastics

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Co-author: Jamie Hall

A cross-linked degradable plastic network will be created by mixing and curing varying ratios of monomers derived from silicon. Properties that will be tested include biodegradability, thermal, and mechanical strength.

Anderson Wey
Major: Biochemistry
Faculty mentor: David Son