Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas.

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (1H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

Combining Solid-State NMR with Structural and Biophysical Techniques to Design Challenging Protein-Drug Conjugates.

Several protein-drug conjugates are currently being used in cancer therapy. These conjugates rely on cytotoxic organic compounds that are covalently attached to the carrier proteins or that interact with them via non-covalent interactions. Human transthyretin (TTR), a physiological protein, has already been identified as a possible carrier protein for the delivery of cytotoxic drugs. Here we show the structure-guided development of a new stable cytotoxic molecule based on a known strong binder of TTR and a well-established anticancer drug. This example is used to demonstrate the importance of the integration of multiple biophysical and structural techniques, encompassing microscale thermophoresis, X-ray crystallography and NMR. In particular, we show that solid-state NMR has the ability to reveal effects caused by ligand binding which are more easily relatable to structural and dynamical alterations that impact the stability of macromolecular complexes.

Novel Nanogels Loaded with Mn(II) Chelates as Effective and Biologically Stable MRI Probes.

Here it is described nanogels (NG) based on a chitosan matrix, which are covalently stabilized by a bisamide derivative of Mn-t-CDTA (t-CDTA = trans-1,2-diaminocyclohexane-N,N,N',N'-tetraacetic acid). the Mn(II) complex acts both as a contrast medium and as a cross-linking agent. These nanogels are proposed as an alternative to the less stable paramagnetic nanogels obtained by electrostatic interactions between the polymeric matrix and paramagnetic Gd(III) chelates. The present novel nanogels show: i) relaxivity values seven times higher than that of typical monohydrated Mn(II) chelates at the clinical fields, thanks to the combination of a restricted mobility of the complex with a fast exchange of the metal-bound water molecule; ii) high stability of the formulation over time at pH 5 and under physiological conditions, thus excluding metal leaking or particles aggregation; iii) good extravasation and accumulation, with a maximum contrast achieved at 24 h post-injection in mice bearing subcutaneous breast cancer tumor; iv) high T1 contrast (1 T) in the tumor 24 h post-injection. These improved properties pave the way for the use of these paramagnetic nanogels as promising magnetic resonance imaging (MRI) probes for in vitro and in vivo preclinical applications.

Exploration of Blood Metabolite Signatures of Colorectal Cancer and Polyposis through Integrated Statistical and Network Analysis.

Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients.

BACKGROUND: We previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population. METHODS: Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves. RESULTS: Serum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk. CONCLUSIONS: This analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments.

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using 1H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the 1H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

An omics approach to study trace metals in sera of hemodialysis patients treated with erythropoiesis stimulating agents.

Hemodialysis (HD) represents a life-sustaining treatment for patients with end-stage renal disease. However, it is associated with several complications, including anemia. Erythropoiesis-stimulating agents (ESAs) are often administered to HD patients with renal anemia, but a relevant proportion of them fail to respond to the therapy. Since trace metals are involved in several biological processes and their blood levels can be altered by HD, we study the possible association between serum trace metal concentrations and ratios with the administration and response to ESA. For this study, data and sample information of 110 HD patients were downloaded from the UC San Diego Metabolomics Workbench public repository (PR000565). The blood serum levels (and ratios) of antimony, cadmium, copper, manganese, molybdenum, nickel, selenium, tin, and zinc were studied applying an omics statistical approach. The Random Forest model was able to discriminate between HD-dependent patients treated and not treated with ESAs, with an accuracy of 71.7% (95% CI 71.5-71.9%). Logistic regression analysis identifies alterations of Mn, Mo, Cd, Sn, and several of their ratios as characteristic of patients treated with ESAs. Moreover, patients with scarce response to ESAs were shown to be characterized by reduced Mn to Ni and Mn to Sb ratios. In conclusion, our results show that trace metals, in particular manganese, play a role in the mechanisms underlying the human response to ESAs, and if further confirmed, the re-equilibration of their physiological levels could contribute to a better management of HD patients, hopefully reducing their morbidity and mortality.

The early reduction of left ventricular mass after sleeve gastrectomy depends on the fall of branched-chain amino acid circulating levels.

INTRODUCTION: Body-mass index is a major determinant of left-ventricular-mass (LVM). Bariatric-metabolic surgery (BMS) reduces cardiovascular mortality. Its mechanism of action, however, often encompasses a weight-dependent effect. In this translational study, we aimed at investigating the mechanisms by which BMS leads to LVM reduction and functional improvement. METHODS: Twenty patients (45.2 ± 8.5years) were studied with echocardiography at baseline and at 1,6,12 and 48 months after sleeve-gastrectomy (SG). Ten Wistar rats aged 10-weeks received high-fat diet ad libitum for 10 weeks before and 4 weeks after SG or sham-operation. An oral-glucose-tolerance-test was performed to measure whole-body insulin-sensitivity. Plasma metabolomics was analysed in both human and rodent samples. RNA quantitative Real-Time PCR and western blots were performed in rodent heart biopsies. The best-fitted partial-least-square discriminant-analysis model was used to explore the variable importance in the projection score of all metabolites. FINDINGS: Echocardiographic LVM (-12%,-23%,-28% and -43% at 1,6,12 and 48 months, respectively) and epicardial fat decreased overtime after SG in humans while insulin-sensitivity improved. In rats, SG significantly reduced LVM and epicardial fat, enhanced ejection-fraction and improved insulin-sensitivity compared to sham-operation. Metabolomics showed a progressive decline of plasma branched-chain amino-acids (BCAA), alanine, lactate, 3-OH-butyrate, acetoacetate, creatine and creatinine levels in both humans and rodents. Hearts of SG rats had a more efficient BCAA, glucose and fatty-acid metabolism and insulin signaling than sham-operation. BCAAs in cardiomyocyte culture-medium stimulated lipogenic gene transcription and reduced mRNA levels of key mitochondrial ß-oxidation enzymes promoting lipid droplet accumulation and glycolysis. INTERPRETATION: After SG a prompt and sustained decrease of the LVM, epicardial fat and insulin resistance was found. Animal and in vitro studies showed that SG improves cardiac BCAA metabolism with consequent amelioration of fat oxidation and insulin signaling translating into decreased intra-myocytic fat accumulation and reduced lipotoxicity. FUNDING: This work was supported by the University of Rome Sapienza.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps.

BACKGROUND: Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors. AIM: To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP). METHODS: Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index. RESULTS: NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified. CONCLUSION: In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting.

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan-Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

Prediagnostic circulating metabolites in female breast cancer cases with low and high mammographic breast density.

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We designed a matched case-case study in the EPIC Florence cohort, to evaluate possible associations between the pre-diagnostic metabolomic profile and the risk of BC in high- versus low-MBD women who developed BC during the follow-up. A case-case design with 100 low-MBD (MBD ≤ 25%) and 100 high-MDB BC cases (MBD > 50%) was performed. Matching variables included age, year and type of mammographic examination. 1H NMR metabolomic spectra were available for 87 complete case-case sets. The conditional logistic analyses showed an inverse association between serum levels of alanine, leucine, tyrosine, valine, lactic acid, pyruvic acid, triglycerides lipid main fraction and 11 VLDL lipid subfractions and high-MBD cases. Acetic acid was directly associated with high-MBD cases. In models adjusted for confounding variables, tyrosine remained inversely associated with high-MBD cases while 3 VLDL subfractions of free cholesterol emerged as directly associated with high-MBD cases. A pathway analysis showed that the "phenylalanine, tyrosine and tryptophan pathway" emerged and persisted after applying the FDR procedure. The supervised OPLS-DA analysis revealed a slight but significant separation between high- and low-MBD cases. This case-case study suggested a possible role for pre-diagnostic levels of tyrosine in modulating the risk of BC in high- versus low-MBD women. Moreover, some differences emerged in the pre-diagnostic concentration of other metabolites as well in the metabolomic fingerprints among the two groups of patients.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer.

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient's unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

CXCR4 antagonism sensitizes cancer cells to novel indole-based MDM2/4 inhibitors in glioblastoma multiforme.

Glioblastoma Multiforme (GBM) is a highly invasive primary brain tumour characterized by chemo- and radio-resistance and poor overall survival. GBM can present an aberrant functionality of p53, caused by the overexpression of the murine double minute 2 protein (MDM2) and its analogue MDM4, which may influence the response to conventional therapies. Moreover, tumour resistance/invasiveness has been recently attributed to an overexpression of the chemokine receptor CXCR4, identified as a pivotal mediator of glioma neovascularization. Notably, CXCR4 and MDM2-4 cooperate in promoting tumour invasion and progression. Although CXCR4 actively promotes MDM2 activation leading to p53 inactivation, MDM2-4 knockdown induces the downregulation of CXCR4 gene transcription. Our study aimed to assess if the CXCR4 signal blockade could enhance glioma cells' sensitivity to the inhibition of the p53-MDMs axis. Rationally designed inhibitors of MDM2/4 were combined with the CXCR4 antagonist, AMD3100, in human GBM cells and GBM stem-like cells (neurospheres), which are crucial for tumour recurrence and chemotherapy resistance. The dual MDM2/4 inhibitor RS3594 and the CXCR4 antagonist AMD3100 reduced GBM cell invasiveness and migration in single-agent treatment and mainly in combination. AMD3100 sensitized GBM cells to the antiproliferative activity of RS3594. It is noteworthy that these two compounds present synergic effects on cancer stem components: RS3594 inhibited the growth and formation of neurospheres, AMD3100 induced differentiation of neurospheres while enhancing RS3594 effectiveness preventing their proliferation/clonogenicity. These results confirm that blocking CXCR4/MDM2/4 represents a valuable strategy to reduce GBM proliferation and invasiveness, acting on the stem cell component too.

A Quantum Chemistry View on Two Archetypical Paramagnetic Pentacoordinate Nickel(II) Complexes Offers a Fresh Look on Their NMR Spectra.

Quantum chemical methods for calculating paramagnetic NMR observables are becoming increasingly accessible and are being included in the inorganic chemistry practice. Here, we test the performance of these methods in the prediction of proton hyperfine shifts of two archetypical high-spin pentacoordinate nickel(II) complexes (NiSAL-MeDPT and NiSAL-HDPT), which, for a variety of reasons, turned out to be perfectly suited to challenge the predictions to the finest level of detail. For NiSAL-MeDPT, new NMR experiments yield an assignment that perfectly matches the calculations. The slightly different hyperfine shifts from the two "halves" of the molecules related by a pseudo-C2 axis, which are experimentally divided into two well-defined spin systems, are also straightforwardly distinguished by the calculations. In the case of NiSAL-HDPT, for which no X-ray structure is available, the quality of the calculations allowed us to refine its structure using as a starting template the structure of NiSAL-MeDPT.

Interhelical interactions within the STIM1 CC1 domain modulate CRAC channel activation.

The calcium release activated calcium channel is activated by the endoplasmic reticulum-resident calcium sensor protein STIM1. On activation, STIM1 C terminus changes from an inactive, tight to an active, extended conformation. A coiled-coil clamp involving the CC1 and CC3 domains is essential in controlling STIM1 activation, with CC1 as the key entity. The nuclear magnetic resonance-derived solution structure of the CC1 domain represents a three-helix bundle stabilized by interhelical contacts, which are absent in the Stormorken disease-related STIM1 R304W mutant. Two interhelical sites between the CC1α1 and CC1α2 helices are key in controlling STIM1 activation, affecting the balance between tight and extended conformations. Nuclear magnetic resonance-directed mutations within these interhelical interactions restore the physiological, store-dependent activation behavior of the gain-of-function STIM1 R304W mutant. This study reveals the functional impact of interhelical interactions within the CC1 domain for modifying the CC1-CC3 clamp strength to control the activation of STIM1.

Metabolomics to Assess Response to Immune Checkpoint Inhibitors in Patients with Non-Small-Cell Lung Cancer.

In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive "collective" biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

Single Peptide Backbone Surrogate Mutations to Regulate Angiotensin GPCR Subtype Selectivity.

Mutating the side-chains of amino acids in a peptide ligand, with unnatural amino acids, aiming to mitigate its short half-life is an established approach. However, it is hypothesized that mutating specific backbone peptide bonds with bioisosters can be exploited not only to enhance the proteolytic stability of parent peptides, but also to tune its receptor subtype selectivity. Towards this end, four [Y]6 -Angiotensin II analogues are synthesized where amide bonds have been replaced by 1,4-disubstituted 1,2,3-triazole isosteres in four different backbone locations. All the analogues possessed enhanced stability in human plasma in comparison with the parent peptide, whereas only two of them achieved enhanced AT2 R/AT1 R subtype selectivity. This diversification has been studied through 2D NMR spectroscopy and unveiled a putative more structured microenvironment for the two selective ligands accompanied with increased number of NOE cross-peaks. The most potent analogue, compound 2, has been explored regarding its neurotrophic potential and resulted in an enhanced neurite growth with respect to the established agent C21.

HOPPI-NMR: Hot-Peptide-Based Screening Assay for Inhibitors of Protein-Protein Interactions by NMR.

Protein-protein interactions (PPIs) contribute to the onset and/or progression of several diseases, especially cancer, and this discovery has paved the way for considering disruption of the PPIs as an attractive anti-tumor strategy. In this regard, simple and efficient biophysical methods for detecting the interaction of the inhibitors with the protein counterpart are still in high demand. Herein, we describe a convenient NMR method for the screening of putative PPI inhibitors based on the use of "hot peptides" (HOPPI-NMR). As a case study, HOPPI-NMR was successful applied to the well-known p53/MDM2 system. Our outcomes highlight the main advantages of the method, including the use of a small amount of unlabeled proteins, the minimization of the risk of protein aggregation, and the ability to identify weak binders. The last leaves open the possibility for application of HOPPI-NMR in tandem with fragment-based drug discovery as a valid strategy for the identification of novel chemotypes acting as PPI inhibitors.

The Photocatalyzed Thiol-ene reaction: A New Tag to Yield Fast, Selective and reversible Paramagnetic Tagging of Proteins.

Paramagnetic restraints have been used in biomolecular NMR for the last three decades to elucidate and refine biomolecular structures, but also to characterize protein-ligand interactions. A common technique to generate such restraints in proteins, which do not naturally contain a (paramagnetic) metal, consists in the attachment to the protein of a lanthanide-binding-tag (LBT). In order to design such LBTs, it is important to consider the efficiency and stability of the conjugation, the geometry of the complex (conformational exchanges and coordination) and the chemical inertness of the ligand. Here we describe a photo-catalyzed thiol-ene reaction for the cysteine-selective paramagnetic tagging of proteins. As a model, we designed an LBT with a vinyl-pyridine moiety which was used to attach our tag to the protein GB1 in fast and irreversible fashion. Our tag T1 yields magnetic susceptibility tensors of significant size with different lanthanides and has been characterized using NMR and relaxometry measurements.