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19F NMR of pentafluorophenylalanine in proteins: slow ring flips and allosteric effects
Gottfried Otting (Australian National University, Australia)
Abstract: Using genetic encoding, single phenylalanine residues were replaced by pentafluorophenylalanine. The 19F-NMR spectrum shows that the C6F5 group rotates much more slowly than the phenyl rings of canonical phenylalanine residues. Crystal structures show minimal structural perturbation. Two applications stand out: (a) The five 19F-NMR resonances of a slowly rotating C6F5 group enable simple measurements of the aromatic ring flips even in big proteins (> 40 kDa) without isotope labelling. The ring flip rates are a measure of local protein malleability and change in response to ligand binding. (b) The 19F chemical shifts are very sensitive reporters of allosteric effects.
7 responses to “19F NMR of pentafluorophenylalanine in proteins: slow ring flips and allosteric effects”
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Beautiful work ! Is this higher rigidity of F5F an electrostatic effect or does it have something to do with the molecular weight? Would lower fluorination degrees, e.g. F3F result in faster rotation again ?
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You refer to the rate of ring flip as a proxy measure for local structure malleability; if ring flip rate is largely managed by steric hinderance, is the flipping motion a convolution of an unhindered flipping rate (that of a water exposed F5F), an overall “breathing” motion in the protein and long-lived motions that effect local structure? I’m assuming a fairly low Kd for antigen binding, but is there also a convolution of lindwidth broadening in the bound form as a result of increased overall tumbling time?
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Q: Is this higher rigidity of F5F an electrostatic effect or does it have something to do with the molecular weight?
A: We think it’s neither. Just the slightly increased bulkiness of the pentafluorophenyl ring versus a phenyl ring increases the severity of steric clashes during the ring flip, especially with backbone atoms of the F5F amino acid.
Q: Would lower fluorination degrees, e.g. F3F result in faster rotation
again ?A: Yes, because there’ll be fewer steric clashes.
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Q: You refer to the rate of ring flip as a proxy measure for local
structure malleability; if ring flip rate is largely managed by steric
hinderance, is the flipping motion a convolution of an unhindered
flipping rate (that of a water exposed F5F), an overall “breathing”
motion in the protein and long-lived motions that effect local
structure?A: Ring rotation is fast for a solvent-exposed F5F residue. When packed into the hydrophobic core of a protein, the space necessary for ring rotation can be provided by global breathing or local conformational fluctuations of nearby residues – they just need to get out of the way temporarily to allow a ring rotation. In a cluster of Phe residues, we observed very different ring flip rates, suggesting that local structural fluctuations play a bigger role than global breathing.
Q: I’m assuming a fairly low Kd for antigen binding, but is
there also a convolution of linewidth broadening in the bound form as
a result of increased overall tumbling time?A: The Kd is very low (nanomolar) and the increased molecular weight does increase the linewidths. The 19F spin in the para position of the F5F ring is insensitive to the ring flip rate and can be used as an internal reference of the linewidth.
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Very nice presentation and interesting work!
I was wondering at what field you have acquired the spectra and what kind of excitation you applied to irradiate the whole 19F spectral window (> then 30 ppm for the protein construct). Are the experiments including any shaped pulses?
Which is the biological implication of the rigidification of the antigen upon interaction with the nanobody? is this a common event?
Thank you very much in advance -
Nice work. Since the cis-Pd species is still evolving even at 0 °C, how did you rule out kinetic bias in the ^31P DOSY coefficients from concentration changes during the gradient series?
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Q: I was wondering at what field you have acquired the spectra and what
kind of excitation you applied to irradiate the whole 19F spectral
window (> then 30 ppm for the protein construct). Are the experiments
including any shaped pulses?A: Mostly using a conventional 400 MHz 2-channel NMR spectrometer, sometimes a 500 MHz instrument in Melbourne, which has a cryoprobe optimised for 19F NMR. The 90 deg. 19F-pulse is about 18 microseconds. The off-resonance effects are tolerable. Shaped pulses would lose sensitivity, as the T2 relaxation in big proteins is fast.
Q: Which is the biological implication of the rigidification of the
antigen upon interaction with the nanobody? is this a common event?A: In the picture of the antigen-antibody interaction as a lock-and-key event, rigidification of the complex should be a common outcome. It could well prevent the antigen from binding to other proteins, if its structure is locked into a conformation that doesn’t fit.
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19F-NMR applied to understand the beta-lactone inhibitory effect on OXA-143
Denize Favaro (CUNY – Advanced Science Research Center, USA)
LinkedIn: @DenizeFavaro, X: @DenizeFavaro
Abstract: In 2018, a mechanism distinct from the formation of the inactive hydrolyzed β-lactam was described for 1-methyl-carbapenem hydrolysis by OXA-48, OXA-10, and OXA-23, leading to β-lactone formation. Previous studies have demonstrated that the S configuration at β-lactone C-2 can weakly inhibit these enzymes, and that the lactone-to-hydrolyzed product ratio can vary depending on the residues surrounding the active site. In this study, we used 1D-1H, 15N/1H-TROSY, and 1D-19F Nuclear Magnetic Resonance methods to demonstrate that OXA-143 can also hydrolyze the lactone product, resulting in lactone/hydrolyzed meropenem ratios that are highly dependent on the enzyme/antibiotic ratio, consistent with a reversible covalent inhibition mechanism.
Furthermore, using the cysteine variant OXA-143(D224C) labeled with 3-bromo-1,1,1-trifluoropropan-2-one (BTFA) and 19F-NMR, we identified the enzyme’s conformational states when bound to the substrate and the (S)- lactone, without the need for physical separation of the products. Additionally, the hMER binds weakly to the active site after the lactone is fully hydrolyzed.
Finally, the only mutant to show relevant differences in lactone-to-hydrolyzed product ratio was R261S – almost no lactone formation.Leave a Reply
One response to “19F-NMR applied to understand the beta-lactone inhibitory effect on OXA-143”
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Dear Denize, thank you for sharing this work.
The R261S variant produced almost no β-lactone. Since Arg261 helps position the carbapenem carboxylate in the OXA-143 active site, do you interpret this result as Arg261 controlling the geometry of the acyl-enzyme intermediate required for lactone formation, rather than simply affecting meropenem binding? Which of your 1H product profiles or protein-observed 19F/TROSY results best distinguishes altered reaction chemistry from altered binding or conformational populations?
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A non-metallic organic photocatalyst, graphitic carbon nitride modified in situ via a polyethylene terephthalate (PET) degradation process is characterized by 13C CP/MAS NMR.
MURALI VENKATA BASAVANAG UNNAMATLA (UNIVERSIDAD AUTONOMA DEL ESTADO DE MEXICO, México)
LinkedIn: Dr. Murali Venkata Basavanag Unnamatla, Bluesky: @mulli85.bsky.social
Abstract: A sustainable process was developed to produce a metal-free photocatalyst from recycled PET waste. Through controlled alkaline hydrolysis, the PET was converted into terephthalic acid, which was chemically coupled to graphitic carbon nitride (g-C₃N₄) via amide bonds. Structural characterization techniques (FTIR, SEM, and 13C CPMAS-NMR) confirmed the synthesis of the modified material with the expected properties. The final photocatalyst achieved a remarkable efficiency of 90%, demonstrating the potential to integrate green chemistry and the chemical recycling of polymers into the synthesis of advanced materials for environmental applications.
Leave a Reply
5 responses to “A non-metallic organic photocatalyst, graphitic carbon nitride modified in situ via a polyethylene terephthalate (PET) degradation process is characterized by 13C CP/MAS NMR.”
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Hi,
Very interesting work!
Have you considered maybe measuring 15N-NMR to directly see the amide bonds? Would that help with your research? -
Thank you very much , we have already analyzed with nitrogen also for checking the amide nitrogen but i didnt mentioned here in this presentation .
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Dear Murali,
I had a question about your carbon NMR experiments. How quantitative are the carbon signal intensities? Have you explored multiple cross-polarization (multi-CP) experiments to improve the quantitative accuracy?
Thank you.
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Hello, very interesting work!
Do you have an explanation why the incorporation of the terephthalic acid moiety increases the photocatalytic performance of the gCN despite altering the framework of the polymer? Have you investigated the dynamics of the charge carriers through some optical spectroscopy methods as well?
Thank you! -
Thank you very much for your comment @Arianna Actis ,still we didnt have experimental evidence regarding this ,we are under process of obtaining optical details , but as you said we are altering the GCN , with covalent funcionlization , it may enhance ghe existing features this statement we suported by previous literature methods
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Accelerated Drug Discovery Using 19F-MRI
Dilara Faderl (KIT, Germany)
Abstract: Magnetic resonance imaging (MRI) combines the principles of nuclear magnetic resonance (NMR) with spatial encoding, enabling the spatially resolved detection of molecular interactions across diverse physical and chemical environments. In particular, MRI can encode contrast based on nuclear relaxation properties (transverse and longitudinal relaxation), making it a versatile tool for studying molecular processes. However, extracting such information is inherently associated with long acquisition times, as repeated signal averaging and additional phase-encoding steps are often required. Therefore, parallelization and miniaturization are essential for improving efficiency in both data acquisition and sample handling.
In this work, we exploited ^19F MRI for high-throughput ligand screening. ^19F MRI offers unique advantages because fluorine nuclei provide intrinsic chemical selectivity and negligible biological background, enabling direct spatial mapping of fluorinated reporter ligands without the need for additional spectroscopic encoding. By combining sample parallelization with compressed sensing and paramagnetic enhancement strategies, we screened 61 non-fluorinated samples within a total measurement time of 55 minutes, corresponding to only 54 seconds per sample. This approach accelerated ligand screening compared to conventional NMR methods, reducing the acquisition time from approximately 20 hours to 1 hour. In addition to high-throughput sample analysis, the method provides a quantitative approach for determining the binding strength of unknown drug candidates.
Leave a Reply
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Beyond the Biopsy: Comparative Metabolomic Profiling of Saliva v/s Tissue in Oral cancer Establishes Saliva as a Non-Invasive Biomarkers for Early Detection through Machine learning & Deep learning
Rahul Yadav (Banaras Hindu University, India)
LinkedIn: Rahul Yadav; X: @ryadav0089
Abstract: Oral cancer, a major global health concern due to its frequent late-stage diagnosis and poor prognosis. In India, around 77,000 new cases and 52,000 deaths are reported annually, which is approximately one-fourth of global incidences. Oral Submucous Fibrosis (OSMF), is a precancerous condition that elevates the risk of Oral Squamous Cell Carcinoma (OSCC) development due to tobacco, areca nut, alcohol, HPV, and poor oral hygiene. Traditional diagnostic methods, including biopsies and advanced imaging, and not easily accessible. Therefore, saliva-based biomarkers offer a non-invasive, affordable alternative for early disease detection and monitoring.
Recent advancements in Nuclear Magnetic Resonance (NMR)-based metabolomics combined with deep learning show promising potential for identifying metabolic alterations associated with oral cancer. NMR-based metabolomic profiling can detect distinct metabolome changes in saliva, enabling differentiation between OSCC patients, OSMF patients, and healthy individuals. Through Statistical analysis along with Machine learning and deep learning identifying metabolic patterns and potential biomarkers for Oral cancer and survivor. Our findings through NMR-based metabolomics uncovered set of metabolic signatures in saliva linked to oral cancer progression, from OSMF to OSCC and also explore Tissue metabolite in OSCC patients compare these metabolites from saliva which preferred non-invasive strategies to Early Detection of OSCC. Salivary biomarkers could revolutionize early diagnosis, facilitate personalized therapeutic interventions, and enhance prognostic evaluation in oral cancer management. Future research involving larger patient with multiple cohorts and integrated multi-omics and deep learning strategies will be crucial to validate these results and drive advancements in precision oncology.Leave a Reply
4 responses to “Beyond the Biopsy: Comparative Metabolomic Profiling of Saliva v/s Tissue in Oral cancer Establishes Saliva as a Non-Invasive Biomarkers for Early Detection through Machine learning & Deep learning”
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Thank you very much for the nice presentation!
I was wondering if in your analysis you have also evaluated the presence of large molecular weight components (such as proteins) as a possible biomarker.
Did you highlight any differences between different sex and ages? -
1. Thank you for showing interest in my work. Presence of protein components (amino acid) but not intact proteins or other high-molecular-weight components as biomarkers because we used the CPMG (Carr–Purcell–Meiboom–Gill) pulse sequence, which selectively suppresses broad resonances from macromolecules such as proteins and lipoproteins. Our analysis focused on low-molecular-weight metabolites. Therefore, although we identified several free amino acids (such as tryptophan and other amino acid metabolites), these represent products of amino acid metabolism rather than intact proteins and these free amino acids are classified as metabolites and are commonly analyzed in metabolomics studies. Consequently, our findings should be interpreted as reflecting alterations in metabolic pathways rather than changes in protein abundance.
2. Thank you for insightful comment. We did not perform a dedicated age or sex-stratified metabolomic analysis because the study was designed to compare disease groups rather than gender specific. In addition, the cohort was predominantly male, limiting the statistical power for sex-based comparisons. Future studies with larger and more balanced cohorts will be necessary to investigate the influence of age and sex on the metabolomic profile.
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Very nice work! I’m curious about your ROC curves. Firstly, is it a mistake that the false positive rate goes from 0 to 1 rather than 1 to 0? If this is the case, the Tryptophan and Adipic acid seem to have a steep change in positive rate indicating the need for a stricter cutoff value, but their circulating metabolite levels are quite different. For Tryptophan, the differences between control and patient appear as if they would generate a clear distinction requiring a more modest cutoff value.
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Thank you very much for your insightful comments and for carefully examining our ROC curves.
No, it is not a mistake at x-axis in our ROC curves represents the false positive rate (FPR = 1 − specificity), which is conventionally plotted from 0 to 1. Therefore, the axis orientation in our figure is correct and follows the standard ROC representation. We understand the possible confusion, as some software or publications display specificity on the x-axis, which decreases from 1 to 0. Both representations convey the same diagnostic information but use different axis definitions.
Regarding Tryptophan and Adipic acid, although both metabolites exhibit high diagnostic performance, their ROC curves differ because the distribution and overlap of metabolite concentrations between healthy controls and oral cancer patients are different. Tryptophan shows a clearer separation between the two groups, resulting in a wider range of thresholds that maintain high sensitivity and specificity. In contrast, Adipic acid achieves its optimal discrimination over a narrower threshold range, producing a steeper ROC curve. Therefore, the shape of the ROC curve is determined not only by the magnitude of differences in metabolite concentrations but also by the distribution and overlap of individual sample values. These differences reflect the underlying data distribution rather than an error in the ROC analysis. The optimal cutoff values were determined objectively using the Youden Index, which identifies the threshold that maximizes the combined sensitivity and specificity, rather than by visual inspection of the metabolite distributions.
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Circulating metabolomic changes in Lennox-Gastaut syndrome: correlation with clinico-radiological severity
Aditi Pandey (Centre of BioMedical Research, India)
Abstract: Lennox-Gastaut syndrome (LGS) is an epileptic encephalopathy characterized by multiple types of seizures typically occurring between 1 and 7 years of age, cognitive impairment and characteristic electroencephalographic abnormalities. There is no definite cure for this condition; the seizures can be managed to some extent through medical, dietary and sometimes surgical interventions. LGS is also frequently refractory to anti-seizure medication (ASM).
We report NMR-based metabolomic profile in LGS and its association with clinical parameters. Children between 2-18 years were included based on clinical and EEG diagnostic criteria. Detailed neurological examinations, frequency and type of seizures, EEG changes, cranial MRI and NMR-based serum metabolomic profile were measured. Twenty-six LGS patients and 11 healthy matched controls were included. The median age of the patients was 6 (range 2-17) years, and 19 were males.
Spectra were recorded on 800 MHz NMR spectrometer and eight metabolites namely lactate, glucose, glutamate, pyruvate, glutamine, glycine, citrate and creatinine were crucial for discrimination of LGS from the controls, among which glutamate was upregulated and citrate, pyruvate, and glutamine were downregulated in LGS. Glutamate was associated with developmental quotient (r = -0.48) and pyruvate with focal seizures (r = 0.47) and cystic encephalomalacia on cranial MRI (p = 0.02). NMR metabolomic profile including glutamate, glutamine, glycine, glucose, pyruvate, lactate, citrate and creatinine can discriminate LGS from the controls. In view of significant perturbations in glutamate, the effect of anti-glutamatergic ASM may be explored in controlling seizures and brain damage.Leave a Reply
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Coherent Spin Dynamics and Nanoscale Sensing using Nitrogen Vacancy Centers in Diamond
Roshan Kumar (Indian Institute of Science Education and Research Bhopal, India)
Abstract: Advancing nanoscale quantum sensing requires robust control over internal nuclear spin baths and high-fidelity detection of external molecular targets. In this work, we characterized the coherent spin dynamics and magnetic sensing capabilities of shallow-implanted (≈ 10−15 nm) Nitrogen-Vacancy (NV) centers housed in waveguiding diamond nanopillars. We utilized optically detected magnetic resonance (ODMR), electron spin echo envelope modulation (ESEEM), and XY16 dynamical decoupling under varying static magnetic fields to probe both internal and external local spin environments.
Approaching the excited-state level anti-crossing (ESLAC) near 60 mT, we observed a contrast asymmetry between the 130 MHz C-13 hyperfine transitions, suggesting the onset of macroscopic C-13 dynamic nuclear polarization (DNP).
Additionally, we mapped the internal coupled spin dynamics, resolving the distinct Larmor and hyperfine modulation frequencies of the I = 1/2 N-15 nucleus and the nearby C-13 bath. To push the sensing volume beyond the diamond lattice, we applied the XY-16 dynamical decoupling protocol to filter nanotesla-scale AC magnetic fields generated by surface contamination layers. By measuring the fundamental and third-harmonic resonance intervals across two different bias B-fields, we detected decoherence features consistent with external proton spins on surface, yielding an extracted average gyromagnetic ratio of 41.7 − 42.0 MHz/T. These findings confirms that the nanopillar geometry helps mitigate the traditionally low signal-to-noise ratio of bulk single NV centers. Ultimately, this work highlights the potential of the shallow NV nanopillar platform as a sensitive and robust probe for future nanoscale external nuclear magnetic resonance (NMR) spectroscopy.Leave a Reply
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Compact mobile NMR for physical materials-science of polymers, foods, and porous rocks
Dr. J. Beau W. Webber (Lab-Tools Ltd. Ltd. (nano-science), UK)
LinkedIn: J. Beau W. Webber
Abstract: We have developed highly compact benchtop/mobile Time-domain NMR Spectrometers for physical materials-science of polymers, foods, and porous rocks.
Pore-size distributions :
Gibbs–Thomson equation for the melting point depression, Tm, for a small isolated spherical crystal, of diameter x, in its own liquid, may be expressed as [1] :Delta T_m=T_m^infty-T_mleft(xright)=frac{4sigma_{sl}T_m^infty}{xDelta H_frho_s
Using this Cryoporometric technique, we have measured pore-volume and pore size distribution on 10 samples of North Sea sandstone porous rock, from the National Geological Repository at the British Geological Survey (UKRI).
Quantity and viscosity of bulk and the as-recovered liquids :
We have also developed a novel technique for determining quantified viscosity, using NMR T1ρ [2], and measured the quantity and viscosity of the as-recovered liquids in the porous rocks.
References :
1. Nuclear Magnetic Resonance Cryoporometry J. Mitchell, J. Beau W. Webber and J.H. Strange. Physics Reports, 461, 1-36, 2008. DOI: 10.1016/j.physrep.2008.02.001
2. Quantified Measurements of Viscosity in The Bulk and In Pores, Using NMR Spin-lattice Relaxation in The Rotating Frame J. Beau W. Webber, Philip M. Singer, Dave M. Pickup. Quantitative NMR Journal, Vol. 1 No. 1 (2026): Volume 1, Issue 1 https://qnmrjournal.com/index.php/qNMR/article/view/8/
3. NMR spectrometers that go places others can’t. J. Beau W. Webber, David Pickup. Nat Rev Chem (2026). DOI: 10.1038/s41570-026-00851-6Leave a Reply
2 responses to “Compact mobile NMR for physical materials-science of polymers, foods, and porous rocks”
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Thank you for your video and for presenting this very interesting compact NMR system.
I have one question: Is this device compatible with both 3 mm and 5 mm NMR tubes?
Thank you.
Best regards,
Hadi Loutfi -
Dear Dr. J. Beau W. Webber,
I see that the instrument fits in a desk. Thank you for the presentation. I have some questions regarding the methodology and its potential applications. First, to what extent do the pore-size distributions obtained using NMR cryoporometry agree with those determined by other established pore characterization techniques? Additionally, what is the smallest pore size that can be measured with confidence using your benchtop NMR system? With respect to the viscosity measurements, how sensitive are the results to variations in temperature and magnetic field strength, and how are these factors accounted for during the measurement process? Finally, do you foresee this technique being integrated into routine core analysis workflows within the petroleum industry, and if so, what do you consider to be the primary challenges to its broader adoption?
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Diabetes-Associated Metabolic Reprogramming in Early-Stage Chronic Kidney Disease: An NMR Metabolomics Approach
UPASNA GUPTA (Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India)
LinkedIn: Upasna Gupta, X: @Upasnagupta30
Abstract: Type 2 diabetes mellitus (T2DM) exacerbates the progression of chronic kidney disease (CKD) by intensifying metabolic and oxidative stress, however the underlying biochemical mechanisms remain incompletely understood in early stage CKD. This study aimed to delineate early metabolic alterations in CKD and to investigate diabetes-associated metabolic reprogramming using serum NMR-based metabolomics. A cohort of 100 early-stage CKD patients was analyzed using multivariate and univariate statistical approaches combined with pathway enrichment analysis. Distinct metabolic profiles clearly separated diabetic CKD from non-diabetic CKD. Notably, decreased levels of citrate, serine, and methionine indicated impaired tricarboxylic acid (TCA) cycle activity and disrupted one-carbon metabolism, consistent with a shift in systemic energy handling. Pathway analysis further confirmed significant perturbations in amino acid metabolism and central carbon metabolism, collectively suggesting diabetes-driven metabolic reprogramming in early CKD. These findings identify potential early biomarkers and provide mechanistic insight into diabetes-associated metabolic reprogramming, offering avenues for improved risk stratification and precision therapeutic strategies.
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5 responses to “Diabetes-Associated Metabolic Reprogramming in Early-Stage Chronic Kidney Disease: An NMR Metabolomics Approach”
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Hi,
Very interesting work. I was wondering, do you think there are other metabolites that could be good markers but that are not visible because of low concentration? -
Hi Daphna,
Thank you for your interesting question. Yes, it is certainly possible that additional metabolites with biomarker potential exist but were not detected because they are present at very low concentrations.
In our study, we applied robust statistical approaches, including FDR-adjusted p-values, fold change, ROC-AUC, sensitivity, specificity, and cross-validation, to identify metabolites with strong statistical significance and biological relevance. These stringent criteria ensured that the reported biomarkers were both reliable and biologically meaningful. However, metabolites present at very low concentrations or exhibiting subtle changes may not have been identified in the current analysis.
Future studies involving larger and more diverse cohorts, complementary analytical platforms such as LC-MS/MS, and targeted validation will help identify additional low-abundance metabolites and further evaluate their potential as biomarkers for the early detection and progression of early-stage CKD associated with diabetes.
Thank you again for your thoughtful comment and interest in our work
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Interesting work. Since serum 1H NMR gives steady-state metabolite levels, what supports attributing the lower citrate, serine, and methionine to TCA-cycle and one-carbon reprogramming rather than altered renal handling or broader effects of diabetes?
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Thank you for the question. That’s a really important point. Serum 1H NMR measures steady-state metabolite levels, so based on our data alone, we cannot determine whether the lower citrate, serine, and methionine levels reflect metabolic reprogramming, altered renal handling, or broader effects of diabetes. Our interpretation is based on pathway analysis and supported by previous studies showing alterations in the energy, amino acid and one-carbon metabolism in diabetic kidney disease. Therefore, we consider these findings to suggest potential involvement of these pathways rather than direct evidence of metabolic reprogramming.
Further studies, such as isotope-tracing experiments or tissue-specific metabolomics, would be needed to confirm the underlying mechanisms.
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Thank you for the question. That’s a really important point. Serum 1H
NMR measures steady-state metabolite levels, so based on our data
alone, we cannot determine whether the lower citrate, serine, and
methionine levels reflect metabolic reprogramming, altered renal
handling, or broader effects of diabetes. Our interpretation is based
on pathway analysis and supported by previous studies showing
alterations in the energy, amino acid and one-carbon metabolism in
diabetic kidney disease. Therefore, we consider these findings to
suggest potential involvement of these pathways rather than direct
evidence of metabolic reprogramming.Further studies, such as isotope-tracing experiments or
tissue-specific metabolomics, would be needed to confirm the
underlying mechanisms.
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Evaluation of an 80 MHz Benchtop NMR System for Spin Lattice Relaxation (T₁) Measurements in Coffee Extracts
Mailinda Ayu Hana Margareta (Universitas Negeri Malang, Indonesia)
Abstract: The increasing demand for rapid and cost-effective analytical methods for coffee quality assessment has highlighted the need for accessible techniques capable of probing molecular behavior in complex coffee matrices. Although high field Nuclear Magnetic Resonance (NMR) spectroscopy is the standard approach for spin lattice relaxation (T₁) measurements, its widespread use remains limited by high acquisition costs and specialized infrastructure requirements. This study evaluates the capability of an 80 MHz Benchtop NMR system to perform T₁ measurements directly in coffee extracts. Building upon an established relaxation delay (d1) optimization protocol, inversion recovery experiments were conducted, and relaxation curves were analyzed using nonlinear fitting to determine T₁ values for resolved proton resonances in the coffee extracts. The results demonstrate that the Benchtop NMR system successfully generated reliable relaxation curves and accurately determined distinct T₁ values for multiple proton resonances within the complex coffee matrix. The reproducibility of the fitting results indicates that the 80 MHz Benchtop NMR possesses sufficient sensitivity and stability for molecular relaxation studies despite its relatively low magnetic field strength. These findings demonstrate the feasibility of employing Benchtop NMR as an accessible analytical platform for routine T₁ measurements in coffee extracts and support its potential application in molecular characterization and quality assessment within the coffee industry.
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3 responses to “Evaluation of an 80 MHz Benchtop NMR System for Spin Lattice Relaxation (T₁) Measurements in Coffee Extracts”
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Thank you very much for your presentation and for sharing these very interesting results.
I have a question regarding the T₁ fitting. Could you explain how you assessed the reproducibility of the fitting results?
Thank you.
Best regards,
Hadi Loutfi -
Dear Mailinda,
Thank you for your presentation. It is clear and aligns with your written abstract. I have some questions regarding your work:
In your presentation, you showed a table of ¹H NMR resonances together with their T₁ relaxation times. How reliable are these measured values? For the T₁ analysis, did you determine signal intensities from peak heights or integrated peak areas? Since some of the peaks appear to overlap, how reliable are the individual integrations if used for analysis? If overlapping peaks introduced uncertainty, could you comment on the estimated errors or confidence intervals associated with the reported T₁ values?
How did you account for imperfections in the applied pulses during the T₁ analysis? Could such pulse imperfections influence the accuracy of the measured relaxation times? Did you consider using a saturation recovery experiment instead of the inversion recovery method? If so, how did the two approaches compare in terms of accuracy and practicality?
Your results indicate that the measured T₁ values can be grouped into several time ranges, for example, below 1 s, between 1 and 2 s, and between 2 and 3 s. Do these distinct relaxation domains suggest the presence of different classes of molecules within the coffee extract, or is the extract still dominated by a single major molecular component?
You mentioned the reproducibility of your measurements. How did you evaluate the reproducibility and accuracy of the benchtop NMR results? Would you expect the measured T₁ values to remain the same if the experiments were performed on a high-field NMR spectrometer, or would they change with magnetic field strength? If differences are expected, what are the primary factors responsible for those changes?
What challenges or limitations did you encounter during sample preparation for the NMR measurements? In particular, how did you select the solvent for dissolving the coffee extract for analysis on the 80 MHz benchtop NMR system? How do solvent composition and sample viscosity influence the observed relaxation behavior? What do you consider the main limitations of your sample preparation protocol?
What was the rationale for choosing an 80 MHz benchtop NMR spectrometer instead of a conventional high-field instrument for this study? In which applications would a high-field NMR spectrometer still be the preferred option? Do you consider the spectral resolution of the 80 MHz benchtop system sufficient for routine industrial analysis of complex mixtures such as coffee extracts?
Finally, based on your findings, what future research directions or practical applications do you foresee for this work?
Thank you. I look forward to hearing your responses.
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Thank you for your comments on my presentation, here are some of my answers to your comments.
1. T1 Fitting Reproducibility, Integration Method, and Error Estimation
We used integrated peak areas (or peak heights, depending on your research) from the (H)-NMR spectrum for T1 analysis. For isolated peaks, the integration method provides high accuracy regarding magnetization changes.
On an 80 MHz benchtop NMR system, signal overlap is inherently challenging. To minimize uncertainty in overlapping peaks, we applied peak deconvolution, selecting a very specific integration region within the undisturbed peak region.
Fitting quality was assessed using a non-linear least squares algorithm (Levenberg-Marquardt) with a 95% confidence level. Peaks with high uncertainty (relative error >10-20%) or non-convergent fitting states were eliminated from the analysis.
Reproducibility was evaluated by repeating measurements on independent samples n=3 [or 5] times. The Relative Standard Deviation (%RSD) value for T1 at the main peak is shown to be below 5%, demonstrating the robustness of this method.
2. Pulse Imperfections & Method Selection
The 180° pulse imperfection (due to the non-uniformity of the B1 field) can affect the accuracy of T1 when using the ideal 2-parameter equation. To address this, we use a 3-parameter fitting equation:
I(t)=I_0×[1-a⋅exp(-t/T_1 ) ]
Where the variable a (inversion factor) acts as a free parameter (its value ranges from 1.8-1.95) that effectively absorbs and corrects the 180° pulse imperfection.
Comparison of Inversion Recovery (IR) vs. Saturation Recovery (SR):
Inversion Recovery (IR): Has a much wider signal dynamic range (from -I0 to +I0, a factor of 2), thus providing high precision and accuracy for determining absolute T_1 values. The downside is that it requires a relatively long waiting time (recycle delay d1≥5T1).
Saturation Recovery (SR): More practical and faster because it does not require a long d1 between scans, making it ideal for routine screening in industry. However, its dynamic range is smaller (0 to +I0), resulting in a slightly lower Signal-to-Noise Ratio (SNR) than IR.
3. Interpretation of T1 Relaxation Domains
Coffee extract is a complex mixture, but the dominant NMR signals originate from several key metabolites: Caffeine, Chlorogenic Acid (CGA), Trigonelline, Acetic/Formic Acid, and Saccharides. These domains do not represent a single molecule, but rather reflect the local mobility of the various functional groups of key coffee metabolites.
4. Benchtop NMR Evaluation & Magnetic Field Dependence (B0)
The reliability of the benchtop was evaluated through intra-day and inter-day precision tests on a standard caffeine quality control (QC). Regarding changes in T1 values at high magnetic fields, the answer is likely yes; T1 values definitely change when measured on a high-field spectrometer (e.g., 400 or 600 MHz). Based on BPP (Bloembergen-Purcell-Pound) theory, the relaxation rate depends on the Larmor frequency (ω0 = γB0). At higher magnetic fields (greater ω0), the spectral density component J(ω_0) for small-to-medium molecules decreases, so T_1 values generally become longer (increase) at high fields compared to the 80 MHz benchtop. Furthermore, the Chemical Shift Anisotropy (CSA) relaxation mechanism becomes more dominant at high fields (proportional to B0^2).
5. Sample Preparation, Solvent, and Viscosity
Solvent Selection: Coffee was extracted using D2O (Deuterium Oxide) to completely dissolve polar metabolites, provide a deuterium-lock signal, and eliminate the giant water (H2O) signal that can mask sample peaks.
Effect of Viscosity & Concentration: High sample viscosity slows down molecular motion (increases the correlation time, τc), which directly shifts the T1 profile. To maintain standardization, we strictly controlled the sample concentration (e.g., X mg/mL) and kept the measurement temperature constant at 25°C.
Protocol Limitations: The main limitations were the inability to dissolve non-polar components (such as coffee lipids/diterpenes) in D2O, as well as the overlap of residual water (HOD) peaks in the 4.7-4.8 ppm region.
6. Rationale for Choosing 80 MHz Benchtop NMR vs. 80 MHz Benchtop NMR. High Field
Reasons for Choosing an 80 MHz Benchtop:
Economical & Practical: No need for liquid refrigerant (helium/liquid nitrogen), very low maintenance costs, and a compact footprint.
Industrial Relevance: Ideal for on-site quality control in coffee processing plants or routine testing laboratories.
When Is High Field Still Necessary? High field (400–800 MHz) is still needed for the elucidation of new molecular structures, untargeted metabolomics analysis, and the separation of highly overlapping signals at trace concentrations.
Is It Sufficient for Routine Industrial Analysis?? Yes, it’s quite sufficient. For routine analysis of key coffee metabolites (Caffeine, CGA, Trigonelline), 80 MHz resolution combined with T1 readings is sufficient for rapid sample differentiation and fingerprinting.
7. Future Research Directions & Practical Applications
Industrial Applications: Rapid authentication of coffee varieties (Arabica vs. Robusta based on the Trigonelline/Caffeine ratio and T1 profile), detection of coffee counterfeiting, and real-time monitoring of roasting degree on the production line.
Method Development: Combining benchtop T1 relaxation profile data with chemometric analysis (such as PCA/PLS) for non-destructive mapping of coffee flavor profiles.
Thank you
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Exploring Clinico-Metabolomics Approach for Predictive Screening of Gestational Diabetes Mellitus (GDM) During the First Trimester
Pragati Gupta (Centre of Biomedical Research Institute Lucknow, India)
LinkedIn: Pragati Gupta, X: @praggupta856
Abstract: Gestational diabetes mellitus (GDM) is a major pregnancy-associated metabolic disorder characterized by glucose intolerance first recognized during pregnancy. Conventional diagnosis using oral glucose tolerance test (OGTT) is typically performed during the second trimester, limiting opportunities for early intervention . Our study aimed to investigate whether first-trimester serum metabolomics combined with clinical profiling could enable early predictive screening of GDM. In this prospective observational study, pregnant women recruited during the first trimester (7–13+6 weeks) were followed until routine OGTT screening at 24–28 weeks of gestation. Based on subsequent diagnosis, subjects were categorized into pre-GDM and non-GDM groups. Serum metabolic profiling was performed using high-field 800 MHz 1H NMR spectroscopy followed by multivariate statistical analysis including sparse Partial Least Squares Discriminant Analysis (sPLS-DA), receiver operating characteristic (ROC) analysis, and pathway interpretation. Distinct metabolic clustering between pre-GDM and non-GDM subjects was observed in the sPLS-DA model, indicating early metabolic perturbations preceding clinical GDM diagnosis. Several metabolites associated with glucose metabolism, branched-chain amino acid metabolism, energy metabolism, and gluconeogenesis exhibited significant alterations in pre-GDM subjects. Key discriminatory metabolites included glucose, alanine, valine, glutamine, histidine, and myo-inositol, with ROC analysis demonstrating strong diagnostic performance. Additionally, reduced alanine-to-glucose ratio suggested potential dysregulation of the hepatic alanine–glucose cycle and altered gluconeogenic metabolism during early pregnancy.
Overall, the study demonstrates that NMR-based clinico-metabolomics can identify early metabolic signatures predictive of GDM before conventional clinical diagnosis. These findings highlight the potential utility of metabolomics-assisted risk stratification as a non-invasive approach for early screening and timely intervention in high-risk pregnancies.Leave a Reply
One response to “Exploring Clinico-Metabolomics Approach for Predictive Screening of Gestational Diabetes Mellitus (GDM) During the First Trimester”
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Very interesting work.
Since both alanine and glucose were elevated, did the alanine-to-glucose ratio improve prediction beyond glucose alone, or was it mainly used to support the interpretation of altered glucose-alanine cycling?
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Exploring spin dynamics of liquid sample in ZULF NMR
Mansi Tarani (Tata Institute of Fundamental Research Hyderabad, India)
LinkedIn: Mansi Tarani
Abstract: NMR is a powerful technique to investigate the structure and dynamics of molecules by manipulating nuclear spins. In the zero- to ultra-low-field (ZULF) regime (≤ few tens of μT), spin dynamics are dominated by spin-spin interactions rather than Zeeman interactions. In this regime, the Larmor frequency lies in the range from Hz to a few kHz; consequently, spins can be manipulated using external DC magnetic fields. As pickup coils are less sensitive to such low frequencies, detection is done using highly sensitive magnetometers. In this work, we use commercial magnetometer (QuSpin) along with a home-built atomic magnetometer (AM) (sensitivity ~ 1pT/√Hz, dynamic range ~ 20μT, bandwidth ~ 24kHz and response time ~ 200 μs), which enables detection of system response in the range of a few kHz with short transverse relaxation times. We explored NMR spectra of different liquid samples over a magnetic field range from 50nT to 10μT. As the magnetic field strength increased, the spin coherence time decreased due to increasing field inhomogeneity across the sample. To refocus the signal, we used CPMG pulse sequence and further enhanced SNR by the introduction of phase cycling, which eliminated the correlated noise across successive scans. We also investigated the spin-spin (T2) and spin–lattice (T1) relaxation times. We observed an increase in (T2) time compared to high-field NMR because various dephasing effects become weaker in this regime. This work demonstrates the potential of ZULF NMR for precision relaxation studies and provides a pathway toward portable, low-cost spectroscopic and sensing applications.
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One response to “Exploring spin dynamics of liquid sample in ZULF NMR”
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Dear Mansi,
These are promising results!
May I ask you for some specifics about the increase in T2 at lower fields, please? Which nucleus was it, how large was the increase, and what field was the “high-field” NMR?Thank you!
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