IU Indianapolis ScholarWorks :: Browsing by Subject "Thermodynamics"

Browsing by Subject "Thermodynamics"

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Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods
(American Chemical Society, 2022) Hayes, Ryan L.; Vilseck, Jonah Z.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of Medicine
Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.
Au nanoparticle assembly on cnts using flash induced solid-state dewetting
(2015-04-28) Kulkarni, Ameya; Ryu, Jong Eun; Agarwal, Mangilal; Xie, Jian; Cheng, Ruihua
Carbon Nanotubes (CNTs) are used extensively in various applications where substrate are required to be possessing higher surface area, porosity and electrical and thermal conductivity. Such properties can be enhanced to target a particular gas and biochemical for efficient detection when CNT matrix is functionalized with Nanoparticles (NPs). Conventional functionalization involves harsh oxidation repeated washing, filtration and sonication, which induce defects. The defects lead to hindered mobility of carriers, unwanted doping and also fragmentation of the CNTs in some cases. In this document we demonstrate functionalization of CNT with Au nanoparticles on a macro scale under dry and ambient condition using Xenon ash induced solid-state dewetting. A sputtered thin film was transformed into nanoparticles which were confirmed to be in a state of thermodynamic equilibrium. We worked on 3 nm, 6 nm, 9 nm, 15 nm, 30 nm initial thickness of thin films. Xenon ash parameters of energy, number of pulse, duration of pulse, duration of gap between consecutive pulses were optimized to achieve complete dewetting of Au thin films. 3 nm deposition was in the form of irregular nano-islands which were transformed into stable nanoparticles with a single shot of 10 J/cm2 of 2 ms duration. 6 nm and 9 nm deposition was in form of continues film which was also dewetted into stable nanoparticles with a single pulse but with an increased energy density of 20 J/cm2 and 35 J/cm2 respectively. In case of 15 nm and 30 nm deposition the thin film couldn't be dewetted with a maximum energy density of 50 J/cm2, it was observed that 3 and 4 pulses of 2 ms pulse duration and 2 ms gap duration with an energy density of 50 J/cm2 were required to completely dewet the thicker films. However irregularity was induced in the sizes of the NPs due to Ostwald ripening phenomenon which causes smaller particle within a critical difiusion length to combine and form a larger particle during or after dewetting process. For comparison, the Au thin films were also dewetted by a conventional process involving annealing of samples until the thin film was fully transformed into NPs and the size of NPs seized to grow. Scanning electron microscope (SEM) was used to characterize the samples. Thermodynamic stability of the particles was confirmed with statistical analyses of size distribution after every additional pulse.
Bioenergetic characterization of a shallow-sea hydrothermal vent system: Milos Island, Greece
(Public Library of Science, 2020-06-05) Lu, Guang-Sin; LaRowe, Douglas E.; Fike, David A.; Druschel, Gregory K.; Gilhooly, William P., III; Price, Roy E.; Amend, Jan P.; Earth and Environmental Sciences, School of Science
Shallow-sea hydrothermal systems, like their deep-sea and terrestrial counterparts, can serve as relatively accessible portals into the microbial ecology of subsurface environments. In this study, we determined the chemical composition of 47 sediment porewater samples along a transect from a diffuse shallow-sea hydrothermal vent to a non-thermal background area in Paleochori Bay, Milos Island, Greece. These geochemical data were combined with thermodynamic calculations to quantify potential sources of energy that may support in situ chemolithotrophy. The Gibbs energies (ΔGr) of 730 redox reactions involving 23 inorganic H-, O-, C-, N-, S-, Fe-, Mn-, and As-bearing compounds were calculated. Of these reactions, 379 were exergonic at one or more sampling locations. The greatest energy yields were from anaerobic CO oxidation with NO2- (-136 to -162 kJ/mol e-), followed by reactions in which the electron acceptor/donor pairs were O2/CO, NO3-/CO, and NO2-/H2S. When expressed as energy densities (where the concentration of the limiting reactant is taken into account), a different set of redox reactions are the most exergonic: in sediments affected by hydrothermal input, sulfide oxidation with a range of electron acceptors or nitrite reduction with different electron donors provide 85~245 J per kg of sediment, whereas in sediments less affected or unaffected by hydrothermal input, various S0 oxidation reactions and aerobic respiration reactions with several different electron donors are most energy-yielding (80~95 J per kg of sediment). A model that considers seawater mixing with hydrothermal fluids revealed that there is up to ~50 times more energy available for microorganisms that can use S0 or H2S as electron donors and NO2- or O2 as electron acceptors compared to other reactions. In addition to revealing likely metabolic pathways in the near-surface and subsurface mixing zones, thermodynamic calculations like these can help guide novel microbial cultivation efforts to isolate new species.
Coupled thermal-fluid analysis with flowpath-cavity interaction in a gas turbine engine
(2013-12) Fitzpatrick, John Nathan; Wasfy, Tamer; Nalim, M. Razi; Yu, Huidan (Whitney); Anwar, Sohel
This study seeks to improve the understanding of inlet conditions of a large rotor-stator cavity in a turbofan engine, often referred to as the drive cone cavity (DCC). The inlet flow is better understood through a higher fidelity computational fluid dynamics (CFD) modeling of the inlet to the cavity, and a coupled finite element (FE) thermal to CFD fluid analysis of the cavity in order to accurately predict engine component temperatures. Accurately predicting temperature distribution in the cavity is important because temperatures directly affect the material properties including Young's modulus, yield strength, fatigue strength, creep properties. All of these properties directly affect the life of critical engine components. In addition, temperatures cause thermal expansion which changes clearances and in turn affects engine efficiency. The DCC is fed from the last stage of the high pressure compressor. One of its primary functions is to purge the air over the rotor wall to prevent it from overheating. Aero-thermal conditions within the DCC cavity are particularly challenging to predict due to the complex air flow and high heat transfer in the rotating component. Thus, in order to accurately predict metal temperatures a two-way coupled CFD-FE analysis is needed. Historically, when the cavity airflow is modeled for engine design purposes, the inlet condition has been over-simplified for the CFD analysis which impacts the results, particularly in the region around the compressor disc rim. The inlet is typically simplified by circumferentially averaging the velocity field at the inlet to the cavity which removes the effect of pressure wakes from the upstream rotor blades. The way in which these non-axisymmetric flow characteristics affect metal temperatures is not well understood. In addition, a constant air temperature scaled from a previous analysis is used as the simplified cavity inlet air temperature. Therefore, the objectives of this study are: (a) model the DCC cavity with a more physically representative inlet condition while coupling the solid thermal analysis and compressible air flow analysis that includes the fluid velocity, pressure, and temperature fields; (b) run a coupled analysis whose boundary conditions come from computational models, rather than thermocouple data; (c) validate the model using available experimental data; and (d) based on the validation, determine if the model can be used to predict air inlet and metal temperatures for new engine geometries. Verification with experimental results showed that the coupled analysis with the 3D no-bolt CFD model with predictive boundary conditions, over-predicted the HP6 offtake temperature by 16k. The maximum error was an over-prediction of 50k while the average error was 17k. The predictive model with 3D bolts also predicted cavity temperatures with an average error of 17k. For the two CFD models with predicted boundary conditions, the case without bolts performed better than the case with bolts. This is due to the flow errors caused by placing stationary bolts in a rotating reference frame. Therefore it is recommended that this type of analysis only be attempted for drive cone cavities with no bolts or shielded bolts.
Experimental investigation on traversing hot jet ignition of lean hydrocarbon-air mixtures in a constant volume combustor
(2013-12) Chinnathambi, Prasanna; Nalim, M. Razi; Yu, Huidan (Whitney); Zhu, Likun; Anwar, Sohel
A constant-volume combustor is used to investigate the ignition initiated by a traversing jet of reactive hot gas, in support of combustion engine applications that include novel wave-rotor constant-volume combustion gas turbines and pre-chamber IC engines. The hot-jet ignition constant-volume combustor rig at the Combustion and Propulsion Research Laboratory at the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) was used for this study. Lean premixed combustible mixture in a rectangular cuboid constant-volume combustor is ignited by a hot-jet traversing at different fixed speeds. The hot jet is issued via a converging nozzle from a cylindrical pre-chamber where partially combusted products of combustion are produced by spark- igniting a rich ethylene-air mixture. The main constant-volume combustor (CVC) chamber uses methane-air, hydrogen-methane-air and ethylene-air mixtures in the lean equivalence ratio range of 0.8 to 0.4. Ignition delay times and ignitability of these combustible mixtures as affected by jet traverse speed, equivalence ratio, and fuel type are investigated in this study.
Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations
(Royal Society of Chemistry, 2022-10-27) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of Science
In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.
How to Sample Dozens of Substitutions per Site with λ Dynamics
(American Chemical Society, 2024) Hayes, Ryan L.; Cervantes, Luis F.; Abad Santos, Justin Cruz; Samadi, Amirmasoud; Vilseck, Jonah Z.; Brooks, Charles L., III; Biochemistry and Molecular Biology, School of Medicine
Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.
In silico λ-dynamics predicts protein binding specificities to modified RNAs
(Oxford University Press, 2025) Angelo, Murphy; Zhang, Wen; Vilseck, Jonah Z.; Aoki, Scott T.; Biochemistry and Molecular Biology, School of Medicine
RNA modifications shape gene expression through a variety of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
Molecular Recognition in a Diverse Set of Protein-Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations
(ACS Publications, 2013-10-28) Wang, Bo; Li, Liwei; Hurley, Thomas D.; Meroueh, Samy O.; Department of Biochemistry & Molecular Biology, School of Medicine
End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal•mol−1 highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10). But larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the non-polar and entropy components, rather than a better representation of the electrostatics. SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by pre-scoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the non-polar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.
Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization
(American Chemical Society, 2022) Vilseck, Jonah Z.; Cervantes, Luis F.; Hayes, Ryan L.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of Medicine
With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.

Browsing by Subject "Thermodynamics"

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