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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗

Subject-specific multi-scale modeling of the fate of inhaled aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

60 APPLIED LIFE SCIENCES↗

AlphaFold and Structural Mass Spectrometry Enable Interrogations on the Intrinsically Disordered Regions in Cyanobacterial Light-harvesting Complex Phycobilisome

Intrinsically disordered proteins/regions (IDPRs) are a very large and functionally important class of proteins that participate in weak multivalent interactions in protein complexes. They are recalcitrant for interrogations using X-ray crystallography and cryo-EM. The IDPRs observed at the interface of the photosynthetic pigment protein complexes (PPCs) remain much less clear, e.g., the major cyanobacterial light-harvesting complex (PBS) contains an unstructured PB-loop insertion in the phycocyanobilin domain (PB domain) of ApcE (the largest polypeptide in PBS). Here, a joint platform is built to probe such structural domains. This platform is characterized by two-round progressive justifications of in silico models by using the structural mass spectrometry data. First, the AlphaFold-generated 3D structure of the PB domain (containing PB-loop) was justified in the context of PBS. Second, docking the AlphaFold-generated ApcG (a ligand) into the first-step justified structure (a receptor). The final ligand-receptor complex was then subjected to a second-round justification, again, by using unequivocal isotopically-encoded cross-links identified in LC-MS/MS. This work reveals a full-length PB-loop structure modelled in the PBS basal cylinder, free from any spatial conflicts against the other subunits in PBS. The structure of PB domain highlights the close associations of the intrinsically disordered PB-loop with its binding partners in PBS, including ApcG, another IDPR. The PB-loop region involved in the binding of photosystem II (PSII) is also discussed in the context of excitation energy transfer regulation. Finally, this work calls attention to the highly disordered, yet interrogatable interface between the light-harvesting antenna complexes and the reaction centers.

59 BASIC BIOLOGICAL SCIENCES↗

State of the art, gaps, and prospects in fusion materials theory and modelling

Advancing the theory and simulation of materials for fusion applications remains a key component of global roadmaps aimed at delivering much-needed fusion power. Especially as the drive for commercial application increases, prototypes must be designed against radiation damage before the relevant experimental data can be collected and cost reductions that are possible by testing materials in silico become even more important. Here, we summarise the state of the art as it emerged during the 7 th Fusion Materials Theory & Modelling Workshop that took place in 2024, with the aim to highlight present gaps and future directions for the fusion materials modelling community. Of particular interest were the effects of transmutations, chemical complexity with the development of novel alloys and interatomic potentials, advancements in modelling high-dose microstructures, comparison with experimental data and multiscale models for structural assessment relying on high-performance computing and virtual reality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Debunking common myths in coastal circulation modeling

Despite tremendous progress in algorithm development, computational efficiency and transition into operations over the past two decades, coastal modeling still lacks scientific rigor due to proliferation of many ‘gray’ areas related to various modeling choices made by modelers. Here, in this paper, we propose some guiding principles for the modeling community to improve performance, and we also debunk commonly held myths that make the coastal modeling lack rigor. Using our own experience in developing seamless cross-scale unstructured-grid based models for the past two decades, we describe in unprecedented detail the end-to-end modeling process (i.e., from digital elevation models (DEMs) to mesh generation to post analysis), and demonstrate that defensible modeling is within reach for any end user by following three guiding principles: (1) Bathymetry is a first order forcing in coastal domains and thus should be respected in all aspects of modeling; (2) Oceanographic processes are driven across multiple spatial scales and so models should enable appropriate resolution as needed; and (3) Model assessment should focus on physical processes. Through qualitative and quantitative model assessments, we demonstrate the fundamental role played by bathymetry/topography as embedded in DEMs in making the results defensible, which is unfortunately glossed over in many modeling studies. Focusing on process-based assessment simplifies the calibration process. A major conclusion of this work is that model developers and operators should maximize the scientific rigor for in silico oceanography by avoiding some common pitfalls that rely on error compensation at the expense of representation of physical system processes. We present some best practice procedures for defensive and trustworthy numerical modeling.

54 ENVIRONMENTAL SCIENCES↗

An effective human uracil-DNA glycosylase inhibitor targets the open pre-catalytic active site conformation

Human uracil DNA-glycosylase (UDG) is the prototypic and first identified DNA glycosylase with a vital role in removing deaminated cytosine and incorporated uracil and 5-fluorouracil (5-FU) from DNA. UDG depletion sensitizes cells to high APOBEC3B deaminase and to pemetrexed (PEM) and floxuridine (5-FdU), which are toxic to tumor cells through incorporation of uracil and 5-FU into DNA. To identify small-molecule UDG inhibitors for pre-clinical evaluation, we optimized biochemical screening of a selected diversity collection of >3,000 small-molecules. We found aurintricarboxylic acid (ATA) as an inhibitor of purified UDG at an initial calculated IC 50 < 100 nM. Subsequent enzymatic assays confirmed effective ATA inhibition but with an IC50 of 700 nM and showed direct binding to the human UDG with a K D of <700 nM. ATA displays preferential, dose-dependent binding to purified human UDG compared to human 8-oxoguanine DNA glycosylase. ATA did not bind uracil-containing DNA at these concentrations. Yet, combined crystal structure and in silico docking results unveil ATA interactions with the DNA binding channel and uracil-binding pocket in an open, destabilized UDG conformation. Biologically relevant ATA inhibition of UDG was measured in cell lysates from human DLD1 colon cancer cells and in MCF-7 breast cancer cells using a host cell reactivation assay. Collective findings provide proof-of-principle for development of an ATA-based chemotype and “door stopper” strategy targeting inhibitor binding to a destabilized, open pre-catalytic glycosylase conformation that prevents active site closing for functional DNA binding and nucleotide flipping needed to excise altered bases in DNA.

59 BASIC BIOLOGICAL SCIENCES↗

Extraction of valuable chemicals from food waste via computational solvent screening and experiments

About 1.3 billion tons of global food production end up in landfills and composting, leading to significant anthropogenic greenhouse gas (GHG) emissions. Extracting antioxidant and antimicrobial chemicals (flavonoids, phenolic acids, etc.) from food waste is an economically lucrative valorization strategy but is hindered by efficient solvent selection. Here we perform in silico high throughput screening to identify high solubility solvents for key phenolics and reveal more than 100+ higher-performing solvents than the traditional ethanol and methanol. Solubilities of nine shortlisted solvents are measured and found in reasonable agreement with model predictions. Analysis of the Conductor like Screening Model for Real Solvents (COSMO-RS) σ-profiles and Hansen Solubility Parameters reveals that polarity and hydrogen bonding make dimethylformamide (DMF) an excellent single solvent. We showcase the replacement of high-solubility toxic solvents with green mixtures and demonstrate the approach to potato peel waste. As a result, our work provides a blueprint for solvent selection and generates new insights into extraction from food waste.

09 BIOMASS FUELS↗

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. In conclusion, our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

HIV-1 dimerization initiation site↗

Building a predictive model for polycyclic aromatic hydrocarbon dosimetry in organotypically cultured human bronchial epithelial cells using benzo[ a ]pyrene

The airway epithelium is a primary route of exposure for inhaled toxicants, and organotypic culture models represent an important advancement for toxicity testing compared to simple in vitro models that may lack metabolic capability and multicellular structure/communication associated with the bronchial epithelium in vivo. A quantitative understanding of chemical dosimetry is key for interpreting and extrapolating study results; however, dosimetry is understudied in organotypic models limiting ability to predict toxicity. We developed a dosimetry model for primary human bronchial epithelial cells (HBECs) cultured at the air-liquid interface (ALI) using benzo[a]pyrene (BAP), a representative polycyclic aromatic hydrocarbon. Dose and time course evaluation of metabolite formation and enzyme activity and expression were utilized to parameterize a cellular dosimetry model to improve the utility of ALI-HBECs for assessing chemical risk. Dosimetry analysis demonstrated absorption of BAP into cells and an increase in Phase 1 and 2 metabolites over time that correlated with regulation of metabolizing enzymes. BAP was cleared from cells by 48 h after exposure, and the primary metabolites generated in ALI-HBECs were BAP-3-phenol, BAP-4,5-dihydrodiol, BAP-7,8-dihydrodiol, BAP-9,10-dihydrodiol, BAP-7,8,9,10-tetrol, BAP-3-phenol-glucuronide, BAP-4,5-dihydrodiol-glucuronide, and BAP-9,10-dihydrodiol-glucuronide. The resulting dosimetry model described BAP and 7,8-dihydrodiol toxicokinetics in ALI-HBECs and suggested active excretion of 7,8-dihydrodiol. Overall, this study demonstrates metabolic competency of ALI-HBECs for BAP metabolism, demonstrates the usefulness of complex in vitro systems for human-relevant toxicity data, and exhibits how in silico models can be utilized for understanding the dosimetry of test compounds to aid in in vitro to human extrapolation of toxicity data for risk assessments.

Benzo[a]pyrene↗

MEP pathway products allosterically promote monomerization of deoxy-D-xylulose-5-phosphate synthase to feedback-regulate their supply

Isoprenoids are a very large and diverse family of metabolites required by all living organisms. All isoprenoids derive from the double-bond isomers isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are produced by the methylerythritol 4-phosphate (MEP) pathway in bacteria and plant plastids. It has been reported that IPP and DMAPP feedback-regulate the activity of deoxyxylulose 5-phosphate synthase (DXS), a dimeric enzyme that catalyzes the main flux-controlling step of the MEP pathway. Here we provide experimental insights into the underlying mechanism. Isothermal titration calorimetry and dynamic light scattering approaches showed that IPP and DMAPP can allosterically bind to DXS in vitro, causing a size shift. In silico ligand binding site analysis and docking calculations identified a potential allosteric site in the contact region between the two monomers of the active DXS dimer. Modulation of IPP and DMAPP contents in vivo followed by immunoblot analyses confirmed that high IPP/DMAPP levels resulted in monomerization and eventual aggregation of the enzyme in bacterial and plant cells. Loss of the enzymatically active dimeric conformation allows a fast and reversible reduction of DXS activity in response to a sudden increase or decrease in IPP/DMAPP supply, whereas aggregation and subsequent removal of monomers that would otherwise be available for dimerization appears to be a more drastic response in the case of persistent IPP/DMAPP overabundance (e.g., by a blockage in their conversion to downstream isoprenoids). Our results represent an important step toward understanding the regulation of the MEP pathway and rational design of biotechnological endeavors aimed at increasing isoprenoid contents in microbial and plant systems.

59 BASIC BIOLOGICAL SCIENCES↗

microRNAs (miR 9, 124, 155 and 224) transdifferentiate mouse macrophages to neurons

Highlights: • miR 9, miR 124 and miR155 could reprogramme macrophages to mature neurons. • miR-induced neuronal reprogramming involves stem cell-like intermediate phenotype. • miR 9, miR 124 and miR155 could also reprogramme CD133+ stem cells to mature neurons. Development is an irreversible process of differentiating the undifferentiated cells to functional cells. Brain development involves generation of cells with varied phenotype and functions, which is limited during adulthood, stress, damage/degeneration. Cellular reprogramming makes differentiation reversible process with reprogramming somatic/stem cells to alternative fate with/without stem cells. Exogenously expressed transcription factors or small molecule inhibitors have driven reprogramming of stem/somatic cells to neurons providing alternative approach for pre-clinical/clinical testing and therapeutics. Here in, we report a novel approach of microRNA (miR)- induced trans-differentiation of macrophages (CD11b high) to induced neuronal cells (iNCs) (neuronal markershigh- Nestin, Nurr1, Map2, NSE, Tubb3 and Mash1) without exogenous use of transcription factors. miR 9, 124, 155 and 224 successfully transdifferentiated macrophages to neurons with transient stem cell-like phenotype. We report trans differentiation efficacy 18% and 21% with miR 124 and miR 155. in silico(String 10.0, miR gator, mESAdb, TargetScan 7.0) and experimental analysis indicate that the reprogramming involves alteration of pluripotencygenes like Oct4, Sox2, Klf4, Nanog and pluripotency miR, miR 302. iNCs also shifted to G0 phase indicating manipulation of cell cycle by these miRs. Further, CD133+ intermediate cells obtained during current protocol could be differentiated to iNCs using miRs. The syanpsin{sup +} neurons were functionally active and displayed intracellular Ca{sup +2} evoke on activation. miRs could also transdifferentiate bone marrow-derived macrophages and peripheral blood mononuclear cells to neuronal cells. The current protocol could be employed for direct in vivo reprogramming of macrophages to neurons without teratoma formation for transplantation and clinical studies.

60 APPLIED LIFE SCIENCES↗

G protein nucleolar 3 promotes Non-Hodgkin lymphoma progression by activating the Wnt/β-catenin signaling pathway

G protein nucleolar 3 (GNL3), which acts as an oncoprotein in various carcinomas, is associated with tumor progression; however, little is known regarding GNL3 function in non-Hodgkin lymphoma (NHL). In this study, we first used in silico analysis to determine associations between GNL3 and diffuse large B-cell lymphoma (DLBCL). We then examined the effect of GNL3 on NHL progression, including cell proliferation, apoptosis, and cell cycle progression, and determined its underlying molecular mechanism using in vitro lymphoma cell lines and in vivo mouse xenograft models. We found that GNL3 mRNA levels were markedly higher in DLBCL tissues than in normal tissues, with these higher levels associated with poor prognosis. Additionally, GNL3 overexpression promoted NHL cell proliferation and cell cycle progression and reduced apoptosis in vitro, and enhanced tumorigenesis in an in vivo xenograft model. Moreover, we found that GNL3 upregulated the levels of Wnt/β-catenin signaling pathway-related factors and downstream target genes, whereas the opposite result was observed in GNL3-silenced cells. Furthermore, a rescue experiment using a Wnt/β-catenin inhibitor (XAV939) confirmed that GNL3 promotes NHL progression by activating the Wnt/β-catenin signaling pathway. These findings demonstrated that GNL3 functions as an oncogenic driver in NHL via the Wnt/β-catenin pathway.

60 APPLIED LIFE SCIENCES↗

Discovery, characterization, and application of chromosomal integration sites for stable heterologous gene expression in Rhodotorula toruloides

Rhodotorula toruloides is a non-model, oleaginous yeast uniquely suited to produce acetyl-CoA-derived chemicals. However, the lack of well-characterized genomic integration sites has impeded the metabolic engineering of this organism. Here we report a set of computationally predicted and experimentally validated chromosomal integration sites in R. toruloides. We first implemented an in silico platform by integrating essential gene information and transcriptomic data to identify candidate sites that meet stringent criteria. We then conducted a full experimental characterization of these sites, assessing integration efficiency, gene expression levels, impact on cell growth, and long-term expression stability. Among the identified sites, 12 exhibited integration efficiencies of 50% or higher, making them sufficient for most metabolic engineering applications. Using selected high-efficiency sites, we achieved simultaneous double and triple integrations and efficiently integrated long functional pathways (up to 14.7 kb). Additionally, we developed a new inducible marker recycling system that allows multiple rounds of integration at our characterized sites. Here, we validated this system by performing five sequential rounds of GFP integration and three sequential rounds of MaFAR integration for fatty alcohol production, demonstrating, for the first time, precise gene copy number tuning in R. toruloides. These characterized integration sites should significantly advance metabolic engineering efforts and future genetic tool development in R. toruloides.

59 BASIC BIOLOGICAL SCIENCES↗

Hydrogen-Bonded Organic Frameworks: A Rising Class of Porous Molecular Materials

Hydrogen-bonded organic frameworks (HOFs) are a class of porous molecular materials that rely on the assembly of organic building blocks by means of hydrogen-bonding interactions to form two-dimensional (2D) and three-dimensional (3D) crystalline networks. The reversible nature of the hydrogen-bond formation endows HOFs with the attributes of solution processability and simple regeneration. High-quality single crystals of HOFs can be grown easily for unambiguous superstructure determination by single-crystal X-ray diffraction, which is crucial for the elucidation of superstructure–property relationships. During the past decade, considerable progress has been achieved in realizing stable HOFs with permanent porosities by focusing on the design of molecular building blocks in order to introduce rigidity, auxiliary [π···π] interactions, and interpenetration of their frameworks to sustain the extended networks. The applications of HOFs are far-reaching, spanning catalysis, energy, and biomedical products as well as the storage and separation of fine chemicals. In this paper, we, first of all, provide an overview of the chronological development of HOFs, starting from the seminal work by Marsh and Duchamp in 1969 on the crystal superstructure of the hydrogen-bonded networks of trimesic acid. We identify the development of novel hydrogen-bonding motifs such as diaminotriazine (DTA), the introduction of the concept of molecular tectonics, and the establishment of permanent porosity in HOFs as being some of the milestones, which incentivized the current burgeoning research endeavors on developing HOFs as multifunctional materials. This Account is focused primarily on surveying the strategies for constructing porous 3D HOFs based on organic building blocks with peripheral carboxyl groups. These strategies are presented in the following categories: (1) the polycatenation of 2D networks by trigonal building blocks to form global 3D frameworks, (2) the utilization of building blocks with 3D geometries—tetrahedral and trigonal prismatic—that are predisposed to form 3D networks, and (3) the docking by shape-fitting of geometrically labile building blocks. We emphasize how the molecular geometry of the building blocks plays an important role in modulating the superstructures of extended frameworks so as to address specific applications. Recognizing that the in silico design of HOFs is the ultimate goal of researchers in this field, we also discuss the recent advances in superstructure prediction that lead to the formation of porous supramolecular crystals and assess the complications in implementing computational methods for HOFs with complex superstructures. We hope this Account will inspire the development of new supramolecular designs and creative approaches to crystal engineering that aid and abet the assembly of multifunctional HOFs with customizable properties.

36 MATERIALS SCIENCE↗

Steric and Electronic Influence of Excited-State Decay in Cu(I) MLCT Chromophores

For the past eleven years, a dedicated effort in our research group focused on fundamentally advancing the photophysical properties of cuprous bis-phenanthroline-based metal-to-ligand charge transfer (MLCT) excited states. We rationalized that by gaining control over the numerous factors limiting the more widespread use of Cu I MLCT photosensitizers, they would be readily adopted in numerous light-activated applications given the earth-abundance of copper and the extensive library of 1,10-phenanthrolines developed over the last century. Significant progress has been achieved by recognizing valuable structure-property concepts developed by other researchers in tandem with detailed ultrafast and conventional time-scale investigations, in-silico-inspired molecular designs to predict spectroscopic properties, and applying novel synthetic methodologies. Ultimately, we achieved a plateau in exerting cooperative steric influence to control Cu I MLCT excited state decay. This led to combining sterics with π-conjugation and/or inductive electronic effects to further exert control over molecular photophysical properties. The lessons gleaned from our studies of homoleptic complexes were recently extended to heteroleptic bis(phenanthrolines) featuring enhanced visible light absorption properties and long-lived room-temperature photoluminescence. Furthermore, this Account navigates the reader through our intellectual journey of decision-making, molecular and experimental design, and data interpretation in parallel with appropriate background information related to the quantitative characterization of molecular photophysics using Cu I MLCT chromophores as prototypical examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring the impacts of conformer selection methods on ion mobility collision cross section predictions

The prediction of structure dependent molecular properties, such as collision cross sections as measured using ion mobility spectrometry, are crucially dependent on the selection of the correct population of molecular conformers. Here, we report an in-depth evaluation of multiple conformation selection techniques, including simple averaging, Boltzmann weighting, lowest energy selection, low energy threshold reductions, and similarity reduction. Generating 50,000 conformers each for 18 molecules, we used the In Silico Chemical Library Engine (ISiCLE) to calculate the collision cross sections for the entire dataset. First, we employed Monte Carlo simulations to understand the variability between conformer structures as generated using simulated annealing. Then we employed Monte Carlo simulations to the aforementioned conformer selection techniques applied on the simulated molecular property—the ion mobility collision cross section. Based on our analyses, we found Boltzmann weighting to be a good tradeoff between precision and theoretical accuracy. Combining multiple techniques revealed that energy thresholds and root-mean-squared deviation-based similarity reductions can save considerable computational expense while maintaining property prediction accuracy. Molecular dynamic conformer generation tools like AMBER can continue to generate new lowest energy conformers even after tens of thousands of generations, decreasing precision between runs. This reduced precision can be ameliorated and theoretical accuracy increased by running density functional theory geometry optimization on carefully selected conformers.

Nielson, Felicity F.↗

CFM-ID 4.0: More Accurate ESI MS/MS Spectral Prediction and Compound Identification

In the field of metabolomics, mass spectrometry (MS) is the method most commonly used for identifying and annotating metabolites. As this typically involves matching a given MS spectrum against an experimentally acquired reference spectral library, this approach is limited by the coverage and size of such libraries (which typically number in the thousands). These experimental libraries can be greatly extended by predicting the MS spectra of known chemical structures (which number in the millions) to create computational reference spectral libraries. To facilitate the generation of predicted spectral reference libraries we developed CFM-ID, a computer program that can accurately predict ESI-MS/MS spectrum for a given compound structure. CFM-ID is one of the best-performing methods for compound-to-mass-spectrum prediction, and also one of the top tools for in silico mass-spectrum-to-compound identification. This work improves CFM-ID’s ability to predict ESI-MS/MS spectra from compounds by: (1) learning parameters from features based on the molecular topology, (2) adding a new approach to ring cleavage that models such cleavage as a sequence of simple chemical bond dissociations and (3) expanding its hand-written rule-based predictor to cover more chemical classes, including acylcarnitines, acylcholines, flavonols, flavones, flavanones, and flavonoid glycosides. We demonstrate that this new version of CFM-ID (version 4.0) is significantly more accurate than previous CFM-ID versions, in terms of both EI-MS/MS spectral prediction and compound identification. CFM-ID 4.0 is available at http://cfmid4.wishartlab.com/ as a webservice and docker images can be downloaded at https://hub.docker.com/r/wishartlab/cfmid

Wang, Fei↗