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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Stress-regulated Arabidopsis GAT2 is a low affinity γ-aminobutyric acid transporter

Abstract The four-carbon non-proteinogenic amino acid γ-aminobutyric acid (GABA) accumulates to high levels in plants in response to various abiotic and biotic stress stimuli, and plays a role in C:N balance, signaling, and as a transport regulator. Expression in Xenopus oocytes and voltage-clamping allowed the characterization of Arabidopsis GAT2 (At5g41800) as a low affinity GABA transporter with a K0.5GABA ~8 mM. l-Alanine and butylamine represented additional substrates. GABA-induced currents were strongly dependent on the membrane potential, reaching the highest affinity and highest transport rates at strongly negative membrane potentials. Mutation of Ser17, previously reported to be phosphorylated in planta, did not result in altered affinity. In a short-term stress experiment, AtGAT2 mRNA levels were up-regulated at low water potential and under osmotic stress (polyethylene glycol and mannitol). Furthermore, AtGAT2 promoter activity was detected in vascular tissues, maturating pollen, and the phloem unloading region of young seeds. Even though this suggested a role for AtGAT2 in long-distance transport and loading of sink organs, under the conditions tested neither AtGAT2-overexpressing plants, atgat2 or atgat1 T-DNA insertion lines, nor atgat1 atgat2 doubleknockout mutants differed from wild-type plants in growth on GABA, amino acid levels, or resistance to salt and osmotic stress.

Plant Sciences↗
DOE OSTI2022

On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

n–Doping of a Low–Electron–Affinity Polymer Used as an Electron–Transport Layer in Organic Light–Emitting Diodes

n-Doping electron-transport layers (ETLs) increases their conductivity and improves electron injection into organic light-emitting diodes (OLEDs). Because of the low electron affinity and large bandgaps of ETLs used in green and blue OLEDs, n-doping has been notoriously more difficult for these materials. In this work, n-doping of the polymer poly[(9,9-dioctylfluorene-2,7-diyl)-alt-(benzo[2,1,3]thiadiazol-4,7-diyl)] (F8BT) is demonstrated via solution processing, using the air-stable n-dopant (pentamethylcyclopentadienyl)(1,3,5-trimethylbenzene)ruthenium dimer [RuCp*Mes] 2 . Undoped and doped F8BT films are characterized using ultraviolet and inverse photoelectron spectroscopy. The ionization energy and electron affinity of the undoped F8BT are found to be 5.8 and 2.8 eV, respectively. Upon doping F8BT with [RuCp*Mes] 2 , the Fermi level shifts to within 0.25 eV of the F8BT lowest unoccupied molecular orbital, which is indicative of n-doping. Conductivity measurements reveal a four orders of magnitude increase in the conductivity upon doping and irradiation with ultraviolet light. Here, the [RuCp*Mes] 2 -doped F8BT films are incorporated as an ETL into phosphorescent green OLEDs, and the luminance is improved by three orders of magnitude when compared to identical devices with an undoped F8BT ETL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Computing the Relative Affinity of Chlorophylls a and b to Light-Harvesting Complex II

In plants and algae, the primary antenna protein bound to photosystem II is light-harvesting complex II (LHCII), a pigment–protein complex that binds eight chlorophyll (Chl) a molecules and six Chl b molecules. Chl a and Chl b differ only in that Chl a has a methyl group (–CH 3 ) on one of its pyrrole rings, while Chl b has a formyl group (–CHO) at that position. This blue-shifts the Chl b absorbance relative to Chl a . It is not known how the protein selectively binds the right Chl type at each site. Knowing the selection criteria would allow the design of light-harvesting complexes that bind different Chl types, modifying an organism to utilize the light of different wavelengths. The difference in the binding affinity of Chl a and Chl b in pea and spinach LHCII was calculated using multiconformation continuum electrostatics and free energy perturbation. Both methods have identified some Chl sites where the bound Chl type ( a or b ) has a significantly higher affinity, especially when the protein provides a hydrogen bond for the Chl b formyl group. However, the Chl a sites often have little calculated preference for one Chl type, so they are predicted to bind a mixture of Chl a and b . The electron density of the spinach LHCII was reanalyzed, which, however, confirmed that there is negligible Chl b in the Chl a -binding sites. Finally, it is suggested that the protein chooses the correct Chl type during folding, segregating the preferred Chl to the correct binding site.

chemical calculations↗
DOE OSTI2020

Reactivity descriptors in acid catalysis: acid strength, proton affinity and host–guest interactions

Brønsted acids mediate chemical transformations via proton transfer to bound species and interactions between the conjugate anion and bound cationic intermediates and transition states that are also stabilized by van der Waals forces within voids of molecular dimensions in inorganic hosts. This Feature Article describes the relevant descriptors of reactivity in terms of the properties of acids and molecules that determine their ability to donate and accept protons and to reorganize their respective charges to optimize their interactions at bound states. The deprotonation energy (DPE) of the acids and the protonation energy (Eprot) of the gaseous analogs of bound intermediates and transition states reflect their respective properties as species present at non-interacting distances. These properties accurately describe the reactivity of acids of a given type, such as polyoxometalates (POM) with a given type of addenda atom but different central atoms and heterosilicates, for different families of reactions. They do not fully capture, however, differences among acid types (e.g., Mo and W POM, heterosilicates, mineral acids) for diverse types of chemical transformations (e.g., elimination, isomerization, dimerization, condensation). The incompleteness of such descriptors reflects their inability to describe how protonated molecular species and conjugate anions restructure their respective charges when present as a binding pair at interacting distances. Such interaction energies represent electrostatic forces that depend on charge distributions in the cations and anions and the ability to reorganize the distributions to maximize the interactions. In the case of deprotonation, the electrostatic and charge reorganization components of DPE for various acids solely reflect the ability of the conjugate anion to accept and distribute the negative charge, a characteristic unique of each type of solid acid and specifically of the composition of its extended conjugate anion framework. The energy required to accept and rearrange the positive charge in bound intermediates and transition states reflects, in turn, their respective ability to recover the ionic and covalent components of DPE, the energy required to detach proton from conjugate anions. The DPE components and the recovery fractions together lead to a modified DPE, which captures only the part of DPE that remains unrecovered by the ion-pair interactions at bound intermediates and transition states, as the unifying descriptor for broad families of acids and reactions. The electrostatic and charge reorganization energies involved in these general descriptors are placed in historical context by assessing their connections to the heuristics of hard–soft acid–base displacements. Further development of these concepts requires benchmarking and extension of electrostatic and reorganization components of energies for a more diverse set of reaction types and acid families and advancement of methods for more efficient calculations of electrostatic interactions. Reactivity descriptors must also account for dispersive interactions between host cavities and guest molecules, requiring a framework analogous to the one described here for ion-pair interactions; these dispersive interactions depend on the fit between their shapes and sizes as well as their ‘‘structural stiffness’’ that determines the ability to modify the shapes of molecules and voids to minimize free energy. Entropy considerations and estimates of their dependence on properties of catalysts and molecules are also required for accurately determining Gibbs free energies that ultimately determine reaction rates.

Deshlahra, Prashant↗
DOE OSTI2021

Modulating the Electron Affinity of Small Bipyridyl Molecules on Single Gold Nanoparticles for Plasmon-Driven Electron Transfer

Developing controlled platforms for plasmon-driven chemistry is of great importance in catalytic reactions at the nanoscale. We report anion radical formation for five bipyridyl complexes of varying degrees of electron affinity utilizing optically fo-cused intraband (594 nm) and interband (532 nm) pump excitation of single gold nanoparticles. The surface-enhanced Raman scattering (SERS) of anion radicals for the five non-resonant adsorbed molecules 2,2’-bipyridine (22BPY), 4,4’-bipyridine (44BPY), trans-1,2-bis(4-pyridyl)ethylene (BPE), 1,2-bis(4-pyridyl)acetylene (BPA), and 1,2-bis(4-pyridyl)ethane (BPEt) were detected using localized surface-plasmon resonance (LSPR) excitation with 785 nm. The electron affinity of the five bipyridyl complexes were determined using electrochemistry. Molecules with low electron affinity experienced high-er instances of radical anion formation under a plasmon-coupled intraband electron transfer excitation (594 nm) whereas molecules with high electron affinity showed a preference for anion radical formation under direct interband electron trans-fer excitation (532 nm). The lowest unoccupied molecular orbital (LUMO) energy levels for low electron affinity surface-bound molecules (22BPY, BPEt) are on average ~0.43 eV higher for high electron affinity surface-bound molecules (BPA, BPE, 44BPY) as calculated using time-dependent density functional theory, elucidating the importance of plasmon coupling to energy levels that facilitate charge transfer pathways. We also show the ability to ‘activate’ high vs low electron affinity single nanoparticles with the choice of pump excitation wavelength. The findings show the complex interplay between molecular electron affinity, orbital overlap with the density of states of the plasmonic metal, and excitation energetics of the pump laser wavelength. Potential applications of this work include enhanced control over molecular scale catalysis, biosensor design, and solar energy capture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Target Affinity and Structural Analysis for a Selection of Norovirus Aptamers

Aptamers, single-stranded oligonucleotides that specifically bind a molecule with high affinity, are used as ligands in analytical and therapeutic applications. For the foodborne pathogen norovirus, multiple aptamers exist but have not been thoroughly characterized. Consequently, there is little research on aptamer-mediated assay development. This study characterized seven previously described norovirus aptamers for target affinity, structure, and potential use in extraction and detection assays. Norovirus-aptamer affinities were determined by filter retention assays using norovirus genotype (G) I.1, GI.7, GII.3, GII.4 New Orleans and GII.4 Sydney virus-like particles. Of the seven aptamers characterized, equilibrium dissociation constants for GI.7, GII.3, GII.4 New Orleans and GII.4 Sydney ranged from 71 ± 38 to 1777 ± 1021 nM. Four aptamers exhibited affinity to norovirus GII.4 strains; three aptamers additionally exhibited affinity toward GII.3 and GI.7. Aptamer affinity towards GI.1 was not observed. Aptamer structure analysis by circular dichroism (CD) spectroscopy showed that six aptamers exhibit B-DNA structure, and one aptamer displays parallel/antiparallel G-quadruplex hybrid structure. CD studies also showed that biotinylated aptamer structures were unchanged from non-biotinylated aptamers. Finally, norovirus aptamer assay feasibility was demonstrated in dot-blot and pull-down assays. This characterization of existing aptamers provides a knowledge base for future aptamer-based norovirus detection and extraction assay development and aptamer modification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗
DOE OSTI2021

The electron affinity of the uranium atom

The results of a combined experimental and computational study of the uranium atom are presented with the aim of determining its electron affinity. Experimentally, the electron affinity of uranium was measured via negative ion photoelectron spectroscopy of the uranium atomic anion, U – . Computationally, the electron affinities of both thorium and uranium were calculated by conducting relativistic coupled-cluster and multi-reference configuration interaction calculations. The experimentally determined value of the electron affinity of the uranium atom was determined to be 0.309 ± 0.025 eV. The computationally predicted electron affinity of uranium based on composite coupled cluster calculations and full four-component spin–orbit coupling was found to be 0.232 eV. Predominately due to a better convergence of the coupled cluster sequence for Th and Th – , the final calculated electron affinity of Th, 0.565 eV, was in much better agreement with the accurate experimental value of 0.608 eV. In both scenarios, the ground state of the anion corresponds to electron attachment to the 6d orbital.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Habitat Affinity of Riverine Dissolved Organic Matter Linked to Molecular Traits

Fluxes of organic matter across Earth habitats shape ecosystem function and carbon storage. Yet, it is challenging to predict howthe molecules are preferentially accumulated in specific habitats, that is, the habitat affinities, due to their diverse character-istics. Here, we develop an indicator of compositional-level habitat affinity for dissolved organic matter (DOM) by quantifyingthe aggregated affinities of individual molecules that are over- or under-represented relative to a reference habitat. Applyingthis indicator to 93 paired riverine water-sediment sites, we find that 65.1% of molecules show non-significant habitat affinitiesfor water or sediment and are thermodynamically favorable and susceptible to microbial degradation. Consistently, the habitataffinities of DOM assemblages decrease with lower molecular weights and recalcitrance for waters, and with higher thermody-namic favorability for sediments. These affinities are both reduced by elevated temperatures and anthropogenic pressures, suchas nitrogen loading and land-use change. Our projection of habitat affinities of global riverine DOM shows that regions withintense anthropogenic disturbances or warm climates, such as East Asia, Western Europe, and the Amazon, tend to exhibit loweraffinities. These findings suggest enhanced molecular homogenization between water and sediment habitats is associated withenvironmental changes and underscore the importance of habitat affinities for predicting the potential trajectories of organiccarbon across ecosystems

Cui, Yifan↗
DOE OSTI2023

Relationships between Water’s Structure and Solute Affinity at Polypeptoid Brush Surfaces

Excellent antifouling surfaces are generally thought to create a tightly bound layer of water that resists solute adsorption, and highly hydrophilic surfaces such as those with zwitterionic functionalities are of significant current interest as antifoulant strategies. Furthermore, despite significant proofs-of-concept, we still lack a fundamental understanding of how the nanoscopic structure of this hydration layer translates to reduced fouling, how surface chemistry can be tuned to achieve antifouling through hydration water, and why, in particular, zwitterionic surfaces seem so promising. Here, we use molecular dynamics simulations and free energy calculations to investigate the molecular relationships among surface chemistry, hydration water structure, and surface–solute affinity across a variety of surface-decorated chemistries. Specifically, we consider polypeptoid-decorated surfaces that display well-known experimental antifouling capabilities and that can be synthesized sequence specifically, with precise backbone positioning of, e.g., charged groups. Through simulations, we calculate the affinities of a range of small solutes to polypeptoid brush surfaces of varied side-chain chemistries. We then demonstrate that measures of the structure of surface hydration water in response to a particular surface chemistry signal solute–surface affinity; specifically, we find that zwitterionic chemistries produce solute–surface repulsion through highly coordinated hydration water while suppressing tetrahedral structuring around the solute, in contrast to uncharged surfaces that show solute–surface affinity. Based on the relationship of this structural perturbation to the affinity of small-molecule solutes, we propose a molecular mechanism by which zwitterionic surface chemistries enhance solute repulsion, with broader implications for the design of antifouling surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Using phage display for rational engineering of a higher-affinity humanized 3’ phosphohistidine-specific antibody

Abstract Histidine phosphorylation is a non-canonical post-translational modification (PTM), with 1-phosphohistidine (1-pHis) and 3-phosphohistidine (3-pHis) isoforms, that is understudied due to a lack of robust reagents, including high-affinity pHis-specific antibodies. Engineering pHis antibodies is challenging due to the labile nature of its phosphoramidate (P-N) bond. We developed a strategy for in vitro engineering of antibodies for the detection of native 3-pHis targets, in which the rabbit SC44-8 anti-3-pTza mAb is humanized into a scaffold (hSC44) that is suitable for phage display. Six unique Fab phage-displayed hSC44 scaffold libraries were screened for antibodies that bound 3-pHis with higher affinity and had specificity for 3-pHis versus 3-pTza. hSC44.20N32F L , the best engineered antibody, has ~10-fold higher affinity for 3-pHis than parental hSC44. Eleven new Fab structures, including the first antibody-pHis peptide structures, together with structural and quantum mechanical calculations, provided molecular insights into 3-pHis and 3-pTza discrimination by hSC44.20N32F L and the increased affinity obtained through engineering. We demonstrated the utility of these high-affinity 3-pHis-specific antibodies for the recognition of pHis proteins in mammalian cells by immunoblotting and immunofluorescence staining. Our work describes a general method for engineering labile PTM-specific antibodies and provides novel antibodies for investigating the role of 3-pHis in cell biology.

Martyn, Gregory D.↗
DOE OSTI2024

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Suriyakumar, Yasodhadevi↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Development of high-affinity nanobodies specific for Na V 1.4 and Na V 1.5 voltage-gated sodium channel isoforms

Voltage-gated sodium channels, Na V s, are responsible for the rapid rise of action potentials in excitable tissues. Na V channel mutations have been implicated in several human genetic diseases, such as hypokalemic periodic paralysis, myotonia, and long-QT and Brugada syndromes. Here, we generated high-affinity anti-NaV nanobodies (Nbs), Nb17 and Nb82, that recognize the Na V 1.4 (skeletal muscle) and Na V 1.5 (cardiac muscle) channel isoforms. These Nbs were raised in llama (Lama glama) and selected from a phage display library for high affinity to the C-terminal (CT) region of Na V 1.4. The Nbs were expressed in Escherichia coli, purified, and biophysically characterized. Development of high-affinity Nbs specifically targeting a given human Na V isoform has been challenging because they usually show undesired crossreactivity for different Na V isoforms. Our results show, however, that Nb17 and Nb82 recognize the CTNa V 1.4 or CTNa V 1.5 over other CTNav isoforms. Kinetic experiments by biolayer interferometry determined that Nb17 and Nb82 bind to the CTNa V 1.4 and CTNa V 1.5 with high affinity (K D ~ 40–60 nM). In addition, as proof of concept, we show that Nb82 could detect Na V 1.4 and Na V 1.5 channels in mammalian cells and tissues by Western blot. Furthermore, human embryonic kidney cells expressing holo Na V 1.5 channels demonstrated a robust FRET-binding efficiency for Nb17 and Nb82. Our work lays the foundation for developing Nbs as anti-Na V reagents to capture Na V s from cell lysates and as molecular visualization agents for Na V s.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Oxygen Affinity: The Missing Link Enabling Prediction of Proton Conductivities in Doped Barium Zirconates

Proton-conducting oxides, specifically doped barium zirconates, have garnered much attention as electrolytes for solid-state electrochemical devices operable at intermediate temperatures (400–600 °C). In chemical terms, hydration energy, E hyd , and proton–dopant association energy, E as , are two key parameters that determine whether an oxide exhibits fast proton conduction, but to date ab initio studies have for the most part studied each parameter separately, with no clear correlation with proton conductivity identified in either case. Here, we demonstrate that the oxygen affinity, E O.dopant , defined as the energy released when an oxide ion enters an oxygen vacancy close to a dopant atom, is the missing link between these two parameters and correlates well with experimental proton conductivities in doped barium zirconates. Ab initio calculations of point defects and their complexes in Sc-, In-, Lu-, Er-, Y-, Gd-, and Eu-doped barium zirconates are used to determine E hyd , E as , E O.dopant , and the hydrogen affinity, E H.host , of each system. These four energy terms are related by E hyd = E O.dopant + 2E H.host + 2E as . Complementary impedance spectroscopy measurements reveal that the stronger the calculated oxygen affinity of a system, the higher the proton conductivity at 350 °C. Although the proton trapping site is also an important factor, the results show that oxygen affinity is an excellent predictor of proton conductivity in these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Affinity of Nicotinoids to a Model Nicotinic Acetylcholine Receptor (nAChR) Binding Pocket in the Human Brain

The binding affinity of nicotinoids to the binding residues of the a4ß2 variant of the nicotinic acetylcholine receptor (nAChR) was identified as a strong predictor of the nicotinoid’s addictive character. Using ab-iniito calculations for model binding pockets of increasing size comprising of 3, 6, and 14 amino acids (3AA, 6AA, and 14AA) that are derived from the crystal structure, the differences in binding affinity of 6 nicotinoids, namely nicotine (NIC), nornicotine (NOR), anabasine (ANB), anatabine (ANT), myosmine (MYO), and cotinine (COT) were correlated to their previously reported doses required for increases in intracranial self-stimulation (ICSS) thresholds, a metric for their addictive function. By employing the many body decomposition, the differences in the binding affinities of the various nicotinoids could be attributed mainly to the proton exchange energy between the Pyridine and non-Pyridine rings of the nicotinoids and the interactions between them and a handful of proximal amino acids, namely Trp156, Trpß57, Tyr100, and Tyr204. Interactions between the guest nicotinoid and the amino acids of the binding pocket were found to be mainly classical in nature, except for those between the nicotinoid and Trp156. The larger pockets were found to model binding structures more accurately and predicted the addictive character of all nicotinoids while smaller models, which are more computationally feasible, would only predict the addictive character of nicotinoids that are similar to nicotine. Here, the present study identifies the binding affinity of the guest nicotinoid to the host binding pocket as a strong descriptor of the nicotinoid’s addiction potential and as such it can be employed as a fast screening technique for the potential addiction of nicotine analogs.

60 APPLIED LIFE SCIENCES↗