Publications using Blugold Supercomputing Cluster Resources
We publish
UWEC has maintained its commitment to high-impact learning practices with an emphasis on undergraduate research. Students (highlighted) and their mentor collaborators are continuously exploring newer problems, studying together, and publishing their findings!!! Following is a list of articles they published since 2012:
Publications (Since 2012) (UWEC undergraduate students are highlighted in Bold)
2023
1. Parsons, S. W., Hucek, D. G., Mishra, P., Plusquellic, D. F., Zwier, T. S., & Drucker, S. (2023). Jet-Cooled Phosphorescence Excitation Spectrum of the T1(n,π*) ← S0 Transition of 4H-Pyran-4-one. The Journal of Physical Chemistry A. American Chemical Society (ACS). https://doi.org/10.1021/acs.jpca.3c01059
2. Laatsch, B. F., Brandt, M., Finke, B., Fossum, C. J., Wackett, M. J., Lowater, H. R., Narkiewicz-Jodko, A., Le, C. N., Yang, T., Glogowski, E. M., Bailey-Hartsel, S. C., Bhattacharyya, S., & Hati, S. (2023). Polyethylene Glycol 20k. Does It Fluoresce? ACS Omega (Vol. 8, Issue 15, pp. 14208–14218). American Chemical Society (ACS). https://doi.org/10.1021/acsomega.3c01124
2022
1. Song R.; Shu M.; and Zhu W. The 2020 Global Stock Market Crash: Endogenous or Exogenous?, Physica A: Statistical Mechanics and its Applications, 2022, 585, 126425, (DOI: https://doi.org/10.1016/j.physa.2021.126425).
2. Ma Y. (2022) Computation and Simulation. In: Manthiram A., Fu Y. (eds) Advances in Rechargeable Lithium–Sulfur Batteries. Modern Aspects of Electrochemistry, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-030-90899-7_10
3. Fossum, C. J., Laatsch, B. F., Lowater, H. R., Narkiewicz-Jodko, A. W., Lonzarich, L., Hati, S., & Bhattacharyya, S. (2022). Pre-Existing Oxidative Stress Creates a Docking-Ready Conformation of the SARS-CoV-2 Receptor-Binding Domain. ACS Bio & Med Chem Au, 2(1), 84–93. https://doi.org/10.1021/acsbiomedchemau.1c00040 (Featured On Cover)
4. Wildenberg, J., Kamrowski, C., Senor, C., Mohan, P., & Gomes, R. (2022). Abstract No. 144 Automated IVC filter detection from abdominopelvic CT exams using deep learning. Journal of Vascular and Interventional Radiology (Vol. 33, Issue 6, p. S67). https://doi.org/10.1016/j.jvir.2022.03.225
5. Wozney, A. J., Smith, M. A., Abdrabbo, M., Birch, C. M., Cicigoi, K. A., Dolan, C. C., Gerzema, A. E. L., Hansen, A., Henseler, E. J., LaBerge, B., Leavens, C. M., Le, C. N., Lindquist, A. C., Ludwig, R. K., O’Reilly, M. G., Reynolds, J. H., Sherman, B. A., Sillman, H. W., Smith, M. A., Snortheim, M. J., Svaren, L. M., Vanderpas, E. C., Voon, A., Wackett, M. J., Weiss, M. M., Hati, S., & Bhattacharyya, S. (2022). Evolution of Stronger SARS-CoV-2 Variants as Revealed Through the Lens of Molecular Dynamics Simulations. The Protein Journal. https://doi.org/10.1007/s10930-022-10065-6
6. Siddiqui, N., Reither, T., Black, D., Bauer, T., Hanson, M., & Dave, R. (2022). A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation. 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE. https://doi.org/10.1109/cacml55074.2022.00020
7. Gomes, R., Paul, N., He, N., Huber, A. F., & Jansen, R. J. (2022). Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers. Genes, 13(9). https://doi.org/10.3390/genes13091557
8. Gomes, R., Kamrowski, C., Mohan, P.D., Senor, C., Langlois, J., & Wildenberg, J. (2022). Application of Deep Learning to IVC Filter Detection from CT Scans. Diagnostics, 12(10), 2475. https://doi.org/10.3390/diagnostics12102475
2021
1. Mitchell, N. and Whitney, K.D. Limited evidence for a positive relationship between hybridization and diversification across seed plant families. Evolution, 2021, 75, 1966-1982. (DOI: https://doi.org/10.1111/evo.14291).
2. Munos, J. A.; Lowney, D. T.; and Phillips J. A.Structural and energetic properties of OC–BX3 complexes: unrealized potential for bond-stretch isomerism, Phys. Chem. Chem. Phys., 2021, 23, 14678-14686 (DOI: https://doi.org/10.1039/D1CP02230J).
3. Shu M.; Song R.; and Zhu W. The 2021 Bitcoin Bubbles and Crashes—Detection and Classification, Stats, 2021, 4(4), 950-970, (DOI: https://doi.org/10.3390/stats4040056).
4. Shu M.; Song R.; and Zhu W. The ‘COVID’ Crash of the 2020 U.S. Stock Market, The North-American Journal of Economics and Finance, 2021, 58, 101497, (DOI: https://doi.org/10.1016/j.najef.2021.101497).
5. Gomes, R.; He, N.; Huber, A.; Jansen, R.; and Paul, N. A scalable deep learning framework for breast cancer prediction using DNA methylation data. American Society of Human Genetics 2021, 2021, (View Poster).
6. Pearson, C.; Seliya, N.; and Dave, R. Named Entity Recognition in Unstructured Medical Text Documents. 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 1–6, 2021 (DOI: https://doi.org/10.1109/ICECET52533.2021.9698694)
2020
1. Phillips, J. A.; Ley, A. R.; Treacy, P. W.; Wahl, B. W.; Zehner, B. C.; Donald, K. J.; Gillespie, S. Structural and energetic properties of RMX3‐NH3 complexes Int. J. Quan. Chem. 2020, 120, e26383. (DOI: https://doi.org/10.1002/qua.26383).
2. Hu,Q. H.; Williams, M.; Shulgina, I.; Fossum, C.; Weeks, K.; Adams, L.; Reinhardt, C. R.; Musier-Forsyth, K.; Hati, S.; and Bhattacharyya, S. Editing Domain Motions Preorganize the Synthetic Active Site of Prolyl-tRNA Synthetase ACS Catal., 2020, 10, 10229-10242 (DOI: https://doi.org/10.1021/acscatal.0c02381).
3. Zajac, J.; Anderson, H.; Adams, L.; Wangmo, D.; Suhail, S.; Almen, A.; Berns, L.; Coerber, B.; Dawson, L.; Hunger, A.; Jehn, J.; Johnson, J.; Plack, N.; Strasser, S.; Williams, M.; Bhattacharyya, S; and Hati, S. Effects of Distal Mutations on Prolyl-Adenylate Formation of Escherichia coli Prolyl-tRNA Synthetase Protein J., 2020, 39, 542-553. (DOI: https://doi.org/10.1007/s10930-020-09910-3).
4. Hati, S. and Bhattacharyya, S. Impact of Thiol–Disulfide Balance on the Binding of Covid-19 Spike Protein with Angiotensin-Converting Enzyme 2 Receptor, ACS Omega, 2020, 6, 16292–16298 (DOI: 10.1021/acsomega.0c02125).
2019
1. Phillips, J. A., Modeling Reaction Energies and Exploring Noble Gas Chemistry in the Physical Chemistry Laboratory, in ACS Monograph Series “Using Computations to Teach Chemical Concepts”, 2019, vol 1312, Chapter 4, 33-50 (DOI: 10.1021/bk-2019-1312.ch004).
2. Adams, L. A.; Andrews, R. J.; Hu, Q. H.; Schmit, H. L.; Hati, S.; Bhattacharyya, S., Crowder-induced Conformational Ensemble Shift in Escherichia Coli Prolyl-tRNA Synthetase. Biophys. J., 2019, 17, 1269-1284 (DOI:10.1016/j.bpj.2019.08.033).
3. Sessions, A. G.; McDonnell, M. P.; Christianson, D. A.; Drucker, S., Triplet and Singlet (N,Pi*) Excited States of 4H-Pyran-4-One Characterized by Cavity Ringdown Spectroscopy and Quantum-Chemical Calculations. J. Phys. Chem. A 2019, 123, 6269-6280. (DOI: 10.1021/acs.jpca.9b04238).
2018
1. Hati, S.; Bhattacharyya, S., Integrating Research into the Curriculum: A Low-Cost Strategy for Promoting Undergraduate Research. in ACS Symposium Series "Best Practices for Supporting and Expanding Undergraduate Research in Chemistry", 2018, 119-141. (DOI:10.1021/bk-2018-1275.ch008).
2. Arneson, C.; Wawrzyniakowski, Z. D.; Postlewaite, J. T.; Ma, Y., Lithiation and Delithiation Processes in Lithium–Sulfur Batteries from Ab Initio Molecular Dynamics Simulations. J. Phys. Chem. C 2018, 122, 8769-8779. (DOI:10.1021/acs.jpcc.8b00478).
3. Guo, W.; Bhargav, A.; Ackerson, J. D.; Cui, Y.; Ma, Y.; Fu, Y., Mixture Is Better: Enhanced Electrochemical Performance of Phenyl Selenosulfide in Rechargeable Lithium Batteries. Chemical Communications 2018, 54, 8873-8876. (DOI: 10.1039/c8cc04076a).
4. Reinhardt, C. R.; Hu, Q. H.; Bresnahan, C. G.; Hati, S.; Bhattacharyya, S., Cyclic Changes in Active Site Polarization and Dynamics Drive the “Ping-Pong” Kinetics in Nrh:Quinone Oxidoreductase 2: An Insight from QM/MM Simulations. ACS Catal. 2018, 8, 12015-12029. (DOI: 10.1021/acscatal.8b04193).
5. Hora, N. J.; Wahl, B. M.; Soares, C.; Lara, S. A.; Lanska, J. R.; Phillips, J. A., On the Interactions of Nitriles and Fluoro-Substituted Pyridines with Silicon Tetrafluoride: Computations and Thin Film IR Spectroscopy”, J. Molec. Struct. 2018, 1157, 679. (DOI:10.1016/j.molstruc.2017.12.039).
2017
1. Phillips, J. A.; Dansforth, D. A.; Hora, N. J.; Lanska, J. R.; Waller, A. W., Structural and Energetic Properties of Haloacetonitrile–BCl3 Complexes: Computations and Matrix-IR Spectroscopy. J. Phys. Chem. A 2017, 121, 9252-9261. (DOI: 10.1021/acs.jpca.7b09715).
2. Waller, A. W.; Weissa, N. M.; Decato, D. A.; Phillips, J. A., Structural and Energetic Properties of Nitrile – BX3Complexes: Substituent Effects and their Impact on Condensed-Phase Sensitivity. J. Molec. Struct. 2017, 1130, 984.
3. Phillips, J. A., Structural and Energetic Properties of Nitrile – BX3 Complexes: Substituent Effects and their Impact on Condensed-Phase Sensitivity. Theor. Chem. Accts. (Feature Article) 2017, 136, 16.
4. Mooneyham, A. E.; McDonnell, M. P.; Drucker, S., Cavity Ringdown Spectrum of 2‑Cyclohexen-1-one in the CO/Alkenyl CC Stretch Region of the S1(n, π*) − S0 Vibronic Band System, J. Phys. Chem. A 2017, 121, 2343.
2016
1. Hati, S.; Bhattacharyya, S., Incorporating Modeling and Simulations in Undergraduate Biophysical Chemistry Course to Promote Understanding of Structure-Dynamics-Function Relationships in Proteins. Biochem. Mol. Biol. Educ. 2016, 44, 140-159.
2. Weiss, N. M.; Waller, A. W.; Phillips, J. A., Infrared Spectrum of Ch3cn-Hcl in Solid Neon, and Modeling Matrix Effects in CH3CN-HCl and H3N-HCl. J. Molec. Struct. 2016, 1105, 341.
3. King, F. W., The Evaluation of Some Four-Electron Correlated Integrals with a Slater Basis Arising in Linear and Nonlinear R12 Theories. J. Phys. B-Atom. Mol. Opt. Phys. 2016, 49, 105001-105010.
4. Leong, C. H.; Porras, I.; King, F. W., Analysis of Atomic Integrals Involving Explicit Correlation Factors for the Three-Electron Case. I. Connection to the Hypergeometric Function. J. Math. Chem. 2016, 54, 1514-1552.
5. Reinhardt, C. R.; Jaglinski, T. C.; Kastenschmidt, A. M.; Song, E. H.; Gross, A. K.; Krause, A. J.; Gollmar, J. M.; Meise, K. J.; Stenerson, Z. S.; Weibel, T. J.; Dison, A.; Finnegan, M. R.; Griesi, D. S.; Heltne, M. D.; Hughes, T. G.; Hunt, C. D.; Jansen, K. A.; Xiong, A. H.; Hati, S.; Bhattacharyya, S., Insight into the Kinetics and Thermodynamics of the Hydride Transfer Reactions between Quinones and Lumiflavin: A Density Functional Theory Study. J. Mol. Model. 2016, 22, 199.
2015
1. Bresnahan, C. G.; Reinhardt, C. R.; Bartholow, T. G.; Rumpel, J. P.; North, M.; Bhattacharyya, S., Effect of Stacking Interactions on the Thermodynamics and Kinetics of Lumiflavin: A Study with Improved Density Functionals and Density Functional Tight-Binding Protocol. J. Phys. Chem. A 2015, 119, 172-182.
2. Dorner, M. E.; McMunn, R. D.; Bartholow, T. G.; Calhoon, B. E.; Conlon, M. R.; Dulli, J. M.; Fehling, S. C.; Fisher, C. R.; Hodgson, S. W.; Keenan, S. W.; Kruger, A. N.; Mabin, J. W.; Mazula, D. L.; Monte, C. A.; Olthafer, A.; Sexton, A. E.; Soderholm, B. R.; Strom, A. M.; Hati, S., Comparison of Intrinsic Dynamics of Cytochrome P450 Proteins Using Normal Mode Analysis. Protein Sci. 2015, 24, 1495-1507.
3. Wrass, J. P.; Sadowsky, D.; Bloomgren, K. M.; Cramer, C. J.; Phillips, J. A., Quantum Chemical and Matrix-Ir Characterization of Ch3cn-Bcl3: A Complex with Two Distinct Minima Along the B-N Bond Potential. Phys. Chem. Chem. Phys. 2014, 16, 16480-16491.
2014
4. Bartholow, T. G.; Sanford, B. L.; Cao, B.; Schmit, H. L.; Johnson, J. M.; Meitzner, J.; Bhattacharyya, S.; Musier-Forsyth, K.; Hati, S., Strictly Conserved Lysine of Prolyl-tRNA Synthetase Editing Domain Facilitates Binding and Positioning of Misacylated Trna(Pro.). Biochemistry 2014, 53, 1059-1068.
5. Strom, A. M.; Fehling, S. C.; Bhattacharyya, S.; Hati, S., Probing the Global and Local Dynamics of Aminoacyl-tRNA Synthetases Using All-Atom and Coarse-Grained Simulations. J. Mol. Model. 2014, 20, 2245.
6. Helminiak, H. M.; Knauf, R. R., Danforth, S. J.; Phillips, J. A. Structural and Energetic Properties of Acetonitrile – Group IV (A & B) Halide Complexes. J. Phys. Chem. A 2014, 118, 4266.
2013
1. Hlavacek, N. C.; McAnally, M. O.; Drucker, S., Lowest Triplet (n, p*) Electronic State of Acrolein: Determination of Structural Parameters by Cavity Ringdown Spectroscopy and Quantum-Chemical Calculations. J. Chem. Phys. 2013, 138, 064303.
2. McAnally, M. O.; Zabronsky, K. L.; Stupca, D. J.; Phillipson, K.; Pillsbury, N. R.; Drucker, S., Lowest Triplet (n, p*) State of 2-Cyclohexen-1-One: Characterization by Cavity Ringdown Spectroscopy and Quantum-Chemical Calculations. J. Chem. Phys. 2013, 139, 214311.
3. Johnson, J. M.; Sanford, B. L.; Strom, A. M.; Tadayon, S. N.; Lehman, B. P.; Zirbes, A. M.; Bhattacharyya, S.; Musier-Forsyth, K.; Hati, S., Multiple Pathways Promote Dynamical Coupling between Catalytic Domains in Escherichia Coli Prolyl-tRNA Synthetase. Biochemistry, 2013, 52, 4399-4412.
4. Buchberger, A. R.; Danforth, S. J.; Bloomgren, K. M.; Rohde, J. A.; Smith, E. L.; Gardener, C. C.; Phillips, J. A., Condensed-Phase Effects on the Structural Properties of FCH2CN-BF3 and ClCH2CN-BF3: A Matrix-Isolation and Computational Study. J. Phys. Chem. B 2013, 117, 11687-11696.
2012
1. Knauf, R. R.; Helminiak, H. M.; Wrass, J. P.; Gallert, T. M.; Phillips, J. A. Structural and Energetic Properties of Alkylfluoride – BF3 Complexes in the Gas Phase and Condensed-Phase Media: Computations and Matrix Infrared Spectroscopy. J. Phys. Org. Chem. 2012, 25, 493.
2. Sanford, B.; Cao, B.; Johnson, J. M.; Zimmerman, K.; Strom, A. M.; Mueller, R. M.; Bhattacharyya, S.; Musier-Forsyth, K.; Hati, S., Role of Coupled Dynamics in the Catalytic Activity of Prokaryotic-Like Prolyl-tRNA Synthetases. Biochemistry 2012, 51, 2146-2156.
Our recent studies
Understanding pattern and process in plant evolution
Dr. Nora Mitchell is using the Blugold Center for High Performance Computing to study evolution and hybridization in plants at the population and macroevolutionary scales. This work includes using population genomics software to analyze DNA data to understand population structure in sunflowers in the Midwest and phylogenetic software to understand larger patterns in plant evolution.
Excited State of Molecules using Quantum Chemistry
Triplet and Singlet (n,π*) Excited States of 4H-Pyran-4-one Characterized by Cavity Ringdown Spectroscopy and Quantum-Chemical Calculations
Contributors: Sessions, A. G.,* McDonnell, M. P.,* Christianson, D. A.,* Drucker, S.
Where it's published: J. Phys. Chem. A 2019, 123, 6269-6280
Abstract: The 4H-pyran-4-one (4PN) molecule serves as a model for investigating structural changes following π* ← n electronic excitation. We have recorded the cavity ringdown (CRD) absorption spectrum of 4PN vapor at room temperature, over the wavelength region from 350 to 370 nm. This spectral region includes the T1(n,π*) ← S0 band system as well as the low-energy portion of the S1(n,π*) ← S0 system. Aided by predictions from ab initio (equation-of-motion excitation energies with dynamical correlation incorporated at the level of coupled cluster singles doubles, EOM-EE-CCSD) and density functional theory (time-dependent density functional theory with PBE0 functional, TDPBE0) calculations, we have made vibronic assignments for about 30 features in the CRD spectrum, mostly T1(n,π*) ← S0 transitions. We have used these results to correct certain vibronic assignments appearing in the previous literature for both T1(n,π*) ← S0 and S1(n,π*) ← S0 band systems. We conclude that the lowest-energy carbonyl wagging fundamentals (ν27, in-plane and ν17, out-of-plane) undergo significant frequency drops (28 and 50%, respectively) upon T1(n,π*) ← S0 excitation and similar drops (29 and 39%, respectively) for S1(n,π*) ← S0 excitation. We find that vibrational modes involving the conjugated ring atoms undergo relatively small frequency changes upon π* ← n excitation, for both T1 and S1 states. We have used the present spectroscopic results and vibronic assignments to test the accuracy of computed excited-state frequencies for 4PN. This benchmarking process shows that the economical time-dependent density functional theory method is impressively accurate for certain (but not all) vibrational modes. The highly correlated EOM-EE-CCSD ab initio method is capable of making accurate frequency predictions, but the results, unexpectedly, depend sensitively on basis set family. This anomaly is traceable to a computed conical intersection between the T1(n,π*) and T2(π,π*) surfaces near the T1(n,π*) potential minimum. Relatively small errors in the location of the conical intersection lead to enhanced mixing of the two electronic states and incorrect T1(n,π*) vibrational frequencies when certain triple-ζ quality basis sets are used.
"Ping-pong" Enzyme Kinetics using Molecular Simulations
Cyclic Changes in Active Site Polarization and Dynamics Drive the “Ping-pong” Kinetics in NRH:Quinone Oxidoreductase 2: An Insight from QM/MM Simulations
Contributors: Reinhardt, C. R.,* Hu, Q. H.,* Bresnahan, C. G.,* Hati, S., and Bhattacharyya, S.
Where it's published: ACS Catal. 2018, 8, 12015-12029
Abstract: Quinone reductases belong to the family of flavin-dependent oxidoreductases. With the redox active cofactor, flavin adenine dinucleotide, quinone reductases are known to utilize a “ping-pong” kinetic mechanism during catalysis in which a hydride is bounced back and forth between flavin and its two substrates. However, the continuation of this catalytic cycle requires product displacement steps, where the product of one redox half-cycle is displaced by the substrate of the next half-cycle. Using improved hybrid quantum mechanical/molecular mechanical simulations, both the catalytic hydride transfer and the product displacement reactions were studied in NRH:quinone oxidoreductase 2. Initially, the self-consistent charge-density functional tight binding theory was used to describe the flavin ring and the substrate atoms, while embedded in the molecular mechanically treated solvated active site. Then, for each step of the catalytic cycle, a further improvement of energetics was made using density functional theory-based corrections. The present study showcases an integrated interplay of solvation, protonation, and protein matrix-induced polarization as the driving force behind the thermodynamic wheel of the “ping-pong” kinetics. Reported here is the first-principles model of the “ping-pong” kinetics that portrays how cyclic changes in the active site polarization and dynamics govern the oscillatory hydride transfer and product displacement in this enzyme.
Structure and Energetics of Molecular Complexes
Structural and Energetic Properties of Haloacetonitrile–BCl3 Complexes: Computations and Matrix-IR Spectroscopy
Contributor: Phillips, J. A., Dansforth, D. A.,* Hora, N. J.,* Lanska, J. R.,* Waller, A. W.*
Where it's published: J. Phys. Chem. A 2017, 121, 9252-9261
Abstract: The FCH2CN–BCl3 and ClCH2CN–BCl3 complexes were investigated by quantum-chemical computations and low-temperature, matrix-isolation-IR spectroscopy. Theory predicts two stable equilibrium structures, with distinctly different B–N distances, for both complexes. One set of structures, which correspond to the global energy minima, exhibit B–N distances of 1.610 and 1.604 Å for FCH2CN–BCl3 and ClCH2CN–BCl3, respectively (via M06-2X/aug-cc-pVTZ). The corresponding binding energies are 5.3 and 6.3 kcal/mol. For the metastable structures, the B–N distances are 2.870 and 2.865 Å for FCH2CN–BCl3 and ClCH2CN–BCl3, respectively, and the corresponding binding energies are 3.2 and 3.3 kcal/mol. Also, the barriers between these structures on the B–N distance potentials are 2.5 and 2.8 kcal/mol, respectively, relative to the secondary, long-bond minima. In addition, several IR bands of both FCH2CN–BCl3 and ClCH2CN–BCl3 were observed in nitrogen matrices, but the assigned bands are consistent with M06-2X predictions for the short-bond, minimum-energy structures. None of the observed IR bands could be assigned to the metastable, long-bond structures.