An introduction to biological NMR spectroscopy

Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices

Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.

Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks

Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict 1H and 13C chemical shifts, CSTShift, that combines atomic features with DFT-calculated shielding tensor descriptors, capturing both isotropic and anisotropic shielding effects. Utilizing the NMRShiftDB2 data set and conducting DFT optimization and GIAO calculations at the B3LYP/6-31G(d) level, we prepared the NMRShiftDB2-DFT data set of high-quality 3D structures and shielding tensors with corresponding experimentally measured 1H and 13C chemical shifts. The developed CSTShift models achieve the state-of-the-art prediction performance on both the NMRShiftDB2-DFT test data set and external CHESHIRE data set. Further case studies on identifying correct structures from two groups of constitutional isomers show its capability for structure assignment and elucidation. The source code and data are accessible at https://yzhang.hpc.nyu.edu/IMA.

Comparing Multifunctional Viral and Eukaryotic Proteins for Generating Scission Necks in Membranes

Deterministic formation of membrane scission necks by protein machinery with multiplexed functions is critical in biology. A microbial example is M2 viroporin, a proton pump from the influenza A virus that is multiplexed with membrane remodeling activity to induce budding and scission in the host membrane during viral maturation. In comparison, the dynamin family constitutes a class of eukaryotic proteins implicated in mitochondrial fission, as well as various budding and endocytosis pathways. In the case of Dnm1, the mitochondrial fission protein in yeast, the membrane remodeling activity is multiplexed with mechanoenzyme activity to create fission necks. It is not clear why these functions are combined in these scission processes, which occur in drastically different compositions and solution conditions. In general, direct experimental access to changing neck sizes induced by individual proteins or peptide fragments is challenging due to the nanoscale dimensions and influence of thermal fluctuations. Here, we use a mechanical model to estimate the size of scission necks by leveraging small-angle X-ray scattering structural data of protein-lipid systems under different conditions. The influence of interfacial tension, lipid composition, and membrane budding morphology on the size of the induced scission necks is systematically investigated using our data and molecular dynamic simulations. We find that the M2 budding protein from the influenza A virus has robust pH-dependent membrane activity that induces nanoscopic necks within the range of spontaneous hemifission for a broad range of lipid compositions. In contrast, the sizes of scission necks generated by mitochondrial fission proteins strongly depend on lipid composition, which suggests a role for mechanical constriction.

A Novel Low-Frequency Electromagnetic Active Inertial Sensor for Drug Detection

The purpose of this paper is to demonstrate a new discovery regarding the interaction between materials and very low radio frequencies. Specifically, we observed a feedback response on an inertia active sensor when specific frequencies (around 2-4 kHz) are used to irradiate targeted pharmaceutical samples like aspirin or paracetamol drugs. The characteristics of this phenomenon, such as excitation and relaxation time, the relation between deceleration and a material's quantity, and signal amplitude, are presented and analyzed. Although the underlying physics of this phenomenon is not yet known, we have shown that it has potential applications in remote identification of compounds, detection, and location sensing, as well as identifying substances that exist in plants without the need for any processing. This method is fast, accurate, low-cost, non-destructive, and non-invasive, making it a valuable area for further research that could yield spectacular results in the future.

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

Ligand Steric Profile Tunes the Reactivity of Indium Phosphide Clusters

Indium phosphide quantum dots have become an industrially relevant material for solid-state lighting and wide color gamut displays. The synthesis of indium phosphide quantum dots from indium carboxylates and tris(trimethylsilyl)phosphine (P(SiMe3)3) is understood to proceed through the formation of magic-sized clusters, with In37P20(O2CR)51 being the key isolable intermediate. The reactivity of the In37P20(O2CR)51 cluster is a vital parameter in controlling the conversion to quantum dots. Herein, we report structural perturbations of In37P20(O2CR)51 clusters induced by tuning the steric properties of a series of substituted phenylacetate ligands. This approach allows for control over reactivity with P(SiMe3)3, where meta-substituents enhance the susceptibility to ligand displacement, and para-substituents hinder phosphine diffusion to the core. Thermolysis studies show that with complete cluster dissolution, steric profile can modulate the nucleation period, resulting in a nanocrystal size dependence on ligand steric profile. The enhanced stability from ligand engineering also allows for the isolation and structural characterization by single-crystal X-ray diffraction of a new III-V magic-sized cluster with the formula In26P13(O2CR)39. This intermediate precedes the In37P20(O2CR)51 cluster on the InP QD reaction coordinate. The physical and electronic structure of this cluster are analyzed, providing new insight into previously unrecognized relationships between II-VI and III-V materials and the discrete growth of III-V cluster intermediates.

Comparing multifunctional viral and eukaryotic proteins for generating scission necks in membranes

Deterministic formation of membrane scission necks by protein machinery with multiplexed functions is critical in biology. A microbial example is the M2 viroporin, a proton pump from the influenza A virus which is multiplexed with membrane remodeling activity to induce budding and scission in the host membrane during viral maturation. In comparison, the dynamin family constitutes a class of eukaryotic proteins implicated in mitochondrial fission, as well as various budding and endocytosis pathways. In the case of Dnm1, the mitochondrial fission protein in yeast, the membrane remodeling activity is multiplexed with mechanoenzyme activity to create fission necks. It is not clear why these functions are combined in these scission processes, which occur in drastically different compositions and solution conditions. In general, direct experimental access to changing neck sizes induced by individual proteins or peptide fragments is challenging due to the nanoscale dimensions and influence of thermal fluctuations. Here, we use a mechanical model to estimate the size of scission necks by leveraging Small-Angle X-ray Scattering (SAXS) structural data of protein-lipid systems under different conditions. The influence of interfacial tension, lipid composition, and membrane budding morphology on the size of the induced scission necks is systematically investigated using our data and molecular dynamic simulations. We find that the M2 budding protein from the influenza A virus has robust pH-dependent membrane activity that induces nanoscopic necks within the range of spontaneous hemi-fission for a broad range of lipid compositions. In contrast, the sizes of scission necks generated by mitochondrial fission proteins strongly depend on lipid composition, which suggests a role for mechanical constriction.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Nuclear spin effects in biological processes

Traditionally, nuclear spin is not considered to affect biological processes. Recently, this has changed as isotopic fractionation that deviates from classical mass dependence was reported both in vitro and in vivo. In these cases, the isotopic effect correlates with the nuclear magnetic spin. Here, we show nuclear spin effects using stable oxygen isotopes (16O, 17O, and 18O) in two separate setups: an artificial dioxygen production system and biological aquaporin channels in cells. We observe that oxygen dynamics in chiral environments (in particular its transport) depend on nuclear spin, suggesting future applications for controlled isotope separation to be used, for instance, in NMR. To demonstrate the mechanism behind our findings, we formulate theoretical models based on a nuclear-spin-enhanced switch between electronic spin states. Accounting for the role of nuclear spin in biology can provide insights into the role of quantum effects in living systems and help inspire the development of future biotechnology solutions.

The Structural Dynamics, Complexity of Interactions, and Functions in Cancer of Multi-SAM Containing Proteins

SAM domains are crucial mediators of diverse interactions, including those important for tumorigenesis or metastasis of cancers, and thus SAM domains can be attractive targets for developing cancer therapies. This review aims to explore the literature, especially on the recent findings of the structural dynamics, regulation, and functions of SAM domains in proteins containing more than one SAM (multi-SAM containing proteins, MSCPs). The topics here include how intrinsic disorder of some SAMs and an additional SAM domain in MSCPs increase the complexity of their interactions and oligomerization arrangements. Many similarities exist among these MSCPs, including their effects on cancer cell adhesion, migration, and metastasis. In addition, they are all involved in some types of receptor-mediated signaling and neurology-related functions or diseases, although the specific receptors and functions vary. This review also provides a simple outline of methods for studying protein domains, which may help non-structural biologists to reach out and build new collaborations to study their favorite protein domains/regions. Overall, this review aims to provide representative examples of various scenarios that may provide clues to better understand the roles of SAM domains and MSCPs in cancer in general.

Accurate detection of enzymatic degradation processes of gelatin-alginate microcapsule by 1H NMR spectroscopy: Probing biodegradation mechanism and kinetics

With an increase in the severity of environmental pollution caused by microbeads, the development of biodegradable microcapsules that can be applied in diverse fields has attracted significant attention. The degradation processes are directly related to biodegradable microcapsule creation with high stability and persistence. In this study, biodegradable microcapsules are synthesized via a complex coacervation approach using gelatin and alginate as the capsule main wall materials; additionally, enzyme-induced decomposition mechanisms are proposed by observing spectral changes in proton nuclear magnetic resonance (¹H NMR) analyses. Additional analytical techniques confirm the chemical structure, morphology, and size distribution of the synthesized capsules; these uniform spherical microcapsules are 20-30 μm in size and possess a smooth surface. In addition to characterization, the microcapsules were exposed to targeted enzymes to investigate enzymatic effects using short-term and long-term degradation kinetics. Close inspection reveals that determination of the degradation rate constant of the major components in the capsule is feasible, and suggests two types of 4-stage degradation mechanisms that are enzyme-specific. These investigations demonstrate that capsule degradation can be explored in detail using ¹H NMR spectroscopy to provide a viable strategy for monitoring degradation properties in the development of new biodegradable polymers.

Metabolomic profiling of overnight peritoneal dialysis effluents predicts the peritoneal equilibration test type

This study primarily aimed to evaluate whether peritoneal equilibration test (PET) results can be predicted through the metabolomic analysis of overnight peritoneal dialysis (PD) effluents. From a total of 125 patients, overnight PD effluents on the day of the first PET after PD initiation were analyzed. A modified 4.25% dextrose PET was performed, and the PET type was categorized according to the dialysate-to-plasma creatinine ratio at the 4-h dwell time during the PET as follows: high, high average, low average, or low transporter. Nuclear magnetic resonance (NMR)-based metabolomics was used to analyze the effluents and identify the metabolites. The predictive performances derived from the orthogonal projection to latent structure discriminant analysis (OPLS-DA) modeling of the NMR spectrum were estimated by calculating the area under the curve (AUC) using receiver operating characteristic curve analysis. The OPLS-DA score plot indicated significant metabolite differences between high and low PET types. The relative concentrations of alanine and creatinine were greater in the high transporter type than in the low transporter type. The relative concentrations of glucose and lactate were greater in the low transporter type than in the high transporter type. The AUC of a composite of four metabolites was 0.975 in distinguish between high and low PET types. Measured PET results correlated well with the total NMR metabolic profile of overnight PD effluents.

Recent advances in microfluidics-based bioNMR analysis

Nuclear magnetic resonance (NMR) has been used in a variety of fields due to its powerful analytical capability. To facilitate biochemical NMR (bioNMR) analysis for samples with a limited mass, a number of integrated systems have been developed by coupling microfluidics and NMR. However, there are few review papers that summarize the recent advances in the development of microfluidics-based NMR (μNMR) systems. Herein, we review the advancements in μNMR systems built on high-field commercial instruments and low-field compact platforms. Specifically, μNMR platforms with three types of typical microcoils settled in the high-field NMR instruments will be discussed, followed by summarizing compact NMR systems and their applications in biomedical point-of-care testing. Finally, a conclusion and future prospects in the field of μNMR were given.

Conformational Dynamics of Phytoglobin BvPgb1.2 from Beta vulgaris ssp. vulgaris

Plant hemoglobins, often referred to as phytoglobins, play important roles in abiotic stress tolerance. Several essential small physiological metabolites can be bound to these heme proteins. In addition, phytoglobins can catalyze a range of different oxidative reactions in vivo. These proteins are often oligomeric, but the degree and relevance of subunit interactions are largely unknown. In this study, we delineate which residues are involved in dimer formation of a sugar beet phytoglobin type 1.2 (BvPgb1.2) using NMR relaxation experiments. E. coli cells harboring a phytoglobin expression vector were cultivated in isotope-labeled (²H, ¹³C and ¹⁵N) M9 medium. The triple-labeled protein was purified to homogeneity using two chromatographic steps. Two forms of BvPgb1.2 were examined, the oxy-form and the more stable cyanide-form. Using three-dimensional triple-resonance NMR experiments, sequence-specific assignments for CN-bound BvPgb1.2 were achieved for 137 backbone amide cross-peaks in the ¹H-¹⁵N TROSY spectrum, which amounts to 83% of the total number of 165 expected cross-peaks. A large proportion of the non-assigned residues are located in α-helixes G and H, which are proposed to be involved in protein dimerization. Such knowledge around dimer formation will be instrumental for developing a better understanding of phytoglobins' roles in planta.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

The Importance of the Density Functional Theory Exchange-Correlation Hartree-Fock Term in Magnetic Resonance: Application to an Aqueous Environment

Within the framework of Density Functional Theory (DFT), the relevance of the term Hartree-Fock exchange (HFE) for a variety of molecular properties is a critical point. For this reason, we spend efforts to understand these relationships in nuclear magnetic resonance (NMR) parameters in a water solvent. This work takes advantage of the appropriate aug-cc-pVTZ-J basis set and the Minnesota family of DFT methods, which consider different portions of HFE contributions. With regard to solvent participation, the results are based on a sequential Monte Carlo/Quantum Mechanics procedure, which builds the structures of the liquid under realistic thermodynamic conditions. Compared to the accurate results of second-order polarization propagator approximation (SOPPA) and experimental data, all NMR parameters show a huge dependence on the size of the HFE contribution. For instance, the inclusion of this term in ¹J_OH and ²J_HH indirect spin-spin couplings does vary with 49.661 and 25.459 Hz, respectively. The M06-HF method accounts for 100% of HFE and better matches the σ_O and σ_H shielding constants. On the other hand, ¹J_OH and ²J_HH demand a medium contribution (54% of HFE), the best description being associated with the M06-2X method. Thus, the dependence varies regarding the phenomenology of the property in focus and the order for independent treatments. For elements that participate in hydrogen bonds simultaneously as donor and acceptor actors, the results indicate that explicit solvent molecules must be considered in the quantum mechanical calculations for better modeling of paramagnetic shielding constants.

Cholesterol and M2 Rendezvous in Budding and Scission of Influenza A Virus

The cholesterol of the host cell plasma membrane and viral M2 protein plays a crucial role in multiple stages of infection and replication of the influenza A virus. Cholesterol is required for the formation of heterogeneous membrane microdomains (or rafts) in the budozone of the host cell that serves as assembly sites for the viral components. The raft microstructures act as scaffolds for several proteins. Cholesterol may further contribute to the mechanical forces necessary for membrane scission in the last stage of budding and help to maintain the stability of the virus envelope. The M2 protein has been shown to cause membrane scission in model systems by promoting the formation of curved lipid bilayer structures that, in turn, can lead to membrane vesicles budding off or scission intermediates. Membrane remodeling by M2 is intimately linked with cholesterol as it affects local lipid composition, fluidity, and stability of the membrane. Thus, both cholesterol and M2 protein contribute to the efficient and proper release of newly formed influenza viruses from the virus-infected cells.

Predicting molecular properties of α-synuclein using force fields for intrinsically disordered proteins

Independent force field validation is an essential practice to keep track of developments and for performing meaningful Molecular Dynamics simulations. In this work, atomistic force fields for intrinsically disordered proteins (IDP) are tested by simulating the archetypical IDP α-synuclein in solution for 2.5 μs. Four combinations of protein and water force fields were tested: ff19SB/OPC, ff19SB/TIP4P-D, ff03CMAP/TIP4P-D, and a99SB-disp/TIP4P-disp, with four independent repeat simulations for each combination. We compare our simulations to the results of a 73 μs simulation using the a99SB-disp/TIP4P-disp combination, provided by D. E. Shaw Research. From the trajectories, we predict a range of experimental observations of α-synuclein and compare them to literature data. This includes protein radius of gyration and hydration, intramolecular distances, NMR chemical shifts, and ³ J-couplings. Both ff19SB/TIP4P-D and a99SB-disp/TIP4P-disp produce extended conformational ensembles of α-synuclein that agree well with experimental radius of gyration and intramolecular distances while a99SB-disp/TIP4P-disp reproduces a balanced α-synuclein secondary structure content. It was found that ff19SB/OPC and ff03CMAP/TIP4P-D produce overly compact conformational ensembles and show discrepancies in the secondary structure content compared to the experimental data.

Detecting and Characterizing Interactions of Metabolites with Proteins by Saturation Transfer Difference Nuclear Magnetic Resonance (STD NMR) Spectroscopy

Saturation transfer difference (STD) nuclear magnetic resonance (NMR) spectroscopy is an established technique for detecting and characterizing the binding of small molecules, such as metabolites, to biological macromolecules like proteins and nucleic acids. STD NMR allows detection of binding in complex mixtures of potential ligands, which is often used for library screening in the pharmaceutical industry but may also be beneficial for binding studies with metabolite mixtures. The nature of the ligand is normally restricted to small molecules in terms of NMR spectroscopy, and the size of the macromolecule on the other side should be larger than 10-15 kDa. This technique is especially applicable to detecting binders of intermediate to low affinity with the dissociation constant (K_D) above 1 μM. In this chapter, we focus on recent developments and the applications of STD NMR to studying interactions of natural products and metabolites, in particular. The reader is also referred to excellent reviews of the field and the literature cited therein. This chapter also provides a detailed experimental protocol for performing the STD NMR measurement based on the example of the subunit A of the Na⁺-transporting NADH/ubiquinone oxidoreductase (Na⁺-NQR) from V. cholerae interacting with its natural quinone substrate and inhibitors.

Biochemistry and pathophysiology of the Transient Potential Receptor Vanilloid 6 (TRPV6) calcium channel

TRPV6 is a Transient Receptor Potential Vanilloid (TRPV) cation channel with high selectivity for Ca²⁺ ions. First identified in 1999 in a search for the gene which mediates intestinal Ca²⁺ absorption, its far more extensive repertoire as a guardian of intracellular Ca²⁺ has since become apparent. Studies on TRPV6-deficient mice demonstrated additional important roles in placental Ca²⁺ transport, fetal bone development and male fertility. The first reports of inherited deficiency in newborn babies appeared in 2018, revealing its physiological importance in humans. There is currently strong evidence that TRPV6 also contributes to the pathogenesis of some common cancers. The recently reported association of TRPV6 deficiency with non-alcoholic chronic pancreatitis suggests a role in normal pancreatic function. Over time and with greater awareness of TRPV6, other disease-associations are likely to emerge. Powerful analytical tools have provided invaluable insights into the structure and operation of TRPV6. Its roles in Ca²⁺ signaling and carcinogenesis, and the use of channel inhibitors in cancer treatment are being intensively investigated. This review first briefly describes the biochemistry and physiology of the channel, and analytical methods used to investigate these. The focus subsequently shifts to the clinical disorders associated with abnormal expression and the underlying pathophysiology. The aims of this review are to increase awareness of this channel, and to draw together findings from a wide range of sources which may help to formulate new ideas for further studies.

Omics as a Tool to Help Determine the Effectiveness of Supplements

There has been considerable interest in dietary supplements in the last two decades. Companies are releasing new specifics at an alarming pace, while dietary supplements are one of the less-studied substances released for public consumption. However, access to state-of-the-art and high-throughput techniques, such as the ones used in omics, make it possible to check the impact of a substance on human transcriptome or proteome and provide answers to whether its use is reasonable and beneficial. In this review, the main domains of omics are briefly introduced. The review focuses on the three most widely used omics techniques: NGS, LC-MS, NMR, and their usefulness in studying dietary supplements. Examples of studies are described for some of the most commonly supplemented substances, such as vitamins: D, E, A, and plant extracts: resveratrol, green tea, ginseng, and curcumin extract. Techniques used in omics have proven to be useful in studying dietary supplements. NGS techniques are helpful in identifying pathways that change upon supplementation and determining polymorphisms or conditions that qualify for the necessity of a given supplementation. LC-MS techniques are used to establish the serum content of supplemented a compound and its effects on metabolites. Both LC-MS and NMR help establish the actual composition of a compound, its primary and secondary metabolites, and its potential toxicity. Moreover, NMR techniques determine what conditions affect the effectiveness of supplementation.

Dimers of D76N-β2-microglobulin display potent antiamyloid aggregation activity

Self-association of WT β_2-microglobulin (WT-β₂m) into amyloid fibrils is associated with the disorder dialysis related amyloidosis. In the familial variant D76N-β₂m, the single amino acid substitution enhances the aggregation propensity of the protein dramatically and gives rise to a disorder that is independent of renal dysfunction. Numerous biophysical and structural studies on WT- and D76N-β₂m have been performed in order to better understand the structure and dynamics of the native proteins and their different potentials to aggregate into amyloid. However, the structural properties of transient D76N-β₂m oligomers and their role(s) in assembly remained uncharted. Here, we have utilized NMR methods, combined with photo-induced crosslinking, to detect, trap, and structurally characterize transient dimers of D76N-β₂m. We show that the crosslinked D76N-β₂m dimers have different structures from those previously characterized for the on-pathway dimers of ΔN6-β₂m and are unable to assemble into amyloid. Instead, the crosslinked D76N-β₂m dimers are potent inhibitors of amyloid formation, preventing primary nucleation and elongation/secondary nucleation when added in substoichiometric amounts with D76N-β₂m monomers. The results highlight the specificity of early protein-protein interactions in amyloid formation and show how mapping these interfaces can inform new strategies to inhibit amyloid assembly.

Tools shaping drug discovery and development

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

A two-step screening to optimize the signal response of an auto-fluorescent protein-based biosensor

Auto-fluorescent protein (AFP)-based biosensors transduce the structural change in their embedded recognition modules induced by recognition/reaction events to fluorescence signal changes of AFP. The lack of detailed structural information on the recognition module often makes it difficult to optimize AFP-based biosensors. To enhance the signal response derived from detecting the putative structural change in the nitric oxide (NO)-sensing segment of transient receptor potential canonical 5 (TRPC5) fused to enhanced green fluorescent protein (EGFP), EGFP-TRPC5, a facile two-step screening strategy, in silico first and in vitro second, was applied to variants of EGFP-TRPC5 deletion-mutated within the recognition module. In in silico screening, the structural changes of the recognition modules were evaluated as root-mean-square-deviation (RMSD) values, and 10 candidates were efficiently selected from 47 derivatives. Through in vitro screening, four mutants were identified that showed a larger change in signal response than the parent EGFP-TRPC5. One mutant in particular, 551-575, showed four times larger change upon reaction with NO and H₂O₂. Furthermore, mutant 551-575 also showed a signal response upon reaction with H₂O₂ in mammalian HEK293 cells, indicating that the mutant has the potential to be applied as a biosensor for cell measurement. Therefore, this two-step screening method effectively allows the selection of AFP-based biosensors with sufficiently enhanced signal responses for application in mammalian cells.

A two-step screening procedure allows optimization of the optical response of an auto-fluorescent protein-based biosensor for nitric oxide without structural information.

Development of a Therapeutic Peptide for Cachexia Suggests a Platform Approach for Drug-like Peptides

During the development of a melanocortin (MC) peptide drug to treat the condition of cachexia (a hypermetabolic state producing lean body mass wasting), we were confronted with the need for peptide transport across the blood-brain barrier (BBB): the MC-4 receptors (MC4Rs) for metabolic rate control are located in the hypothalamus, i.e., behind the BBB. Using the term "peptides with BBB transport", we screened the medical literature like a peptide library. This revealed numerous "hits"-peptides with BBB transport and/or oral activity. We noted several features common to most peptides in this class, including a dipeptide sequence of nonpolar residues, primary structure cyclization (whole or partial), and a Pro-aromatic motif usually within the cyclized region. Based on this, we designed an MC4R antagonist peptide, TCMCB07, that successfully treated many forms of cachexia. As part of our pharmacokinetic characterization of TCMCB07, we discovered that hepatobiliary extraction from blood accounted for a majority of the circulating peptide's excretion. Further screening of the literature revealed that TCMCB07 is a member of a long-forgotten peptide class, showing active transport by a multi-specific bile salt carrier. Bile salt transport peptides have predictable pharmacokinetics, including BBB transport, but rapid hepatic clearance inhibited their development as drugs. TCMCB07 shares the general characteristics of the bile salt peptide class but with a much longer half-life of hours, not minutes. A change in its C-terminal amino acid sequence slows hepatic clearance. This modification is transferable to other peptides in this class, suggesting a platform approach for producing drug-like peptides.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Nuclear Magnetic Resonance Treatment Accelerates the Regeneration of Dorsal Root Ganglion Neurons in vitro

Functional recovery from peripheral nerve injuries depends on a multitude of factors. Schwann cells (SCs) are key players in the regenerative process as they develop repair-specific functions to promote axon regrowth. However, chronically denervated SCs lose their repair phenotype, which is considered as a main reason for regeneration failure. Previous studies reported a modulatory effect of low nuclear magnetic resonance therapy (NMRT) on cell proliferation and gene expression. To provide first insight into a possible effect of NMRT on cells involved in peripheral nerve regeneration, this study investigated whether NMRT is able to influence the cellular behavior of primary SC and dorsal root ganglion (DRG) neuron cultures in vitro. The effect of NMRT on rat SCs was evaluated by comparing the morphology, purity, proliferation rate, and expression levels of (repair) SC associated genes between NMRT treated and untreated SC cultures. In addition, the influence of (1) NMRT and (2) medium obtained from NMRT treated SC cultures on rat DRG neuron regeneration was examined by analyzing neurite outgrowth and the neuronal differentiation status. Our results showed that NMRT stimulated the proliferation of SCs without changing their morphology, purity, or expression of (repair) SC associated markers. Furthermore, NMRT promoted DRG neuron regeneration shown by an increased cell survival, enhanced neurite network formation, and progressed neuronal differentiation status. Furthermore, the medium of NMRT treated SC cultures was sufficient to support DRG neuron survival and neurite outgrowth. These findings demonstrate a beneficial impact of NMRT on DRG neuron survival and neurite formation, which is primarily mediated via SC stimulation. Our data suggest that NMRT could be suitable as a non-invasive auxiliary treatment option for peripheral nerve injuries and encourage future studies that investigate the effect of NMRT in a physiological context.

Solid-State NMR: Methods for Biological Solids

In the last two decades, solid-state nuclear magnetic resonance (ssNMR) spectroscopy has transformed from a spectroscopic technique investigating small molecules and industrial polymers to a potent tool decrypting structure and underlying dynamics of complex biological systems, such as membrane proteins, fibrils, and assemblies, in near-physiological environments and temperatures. This transformation can be ascribed to improvements in hardware design, sample preparation, pulsed methods, isotope labeling strategies, resolution, and sensitivity. The fundamental engagement between nuclear spins and radio-frequency pulses in the presence of a strong static magnetic field is identical between solution and ssNMR, but the experimental procedures vastly differ because of the absence of molecular tumbling in solids. This review discusses routinely employed state-of-the-art static and MAS pulsed NMR methods relevant for biological samples with rotational correlation times exceeding 100's of nanoseconds. Recent developments in signal filtering approaches, proton methodologies, and multiple acquisition techniques to boost sensitivity and speed up data acquisition at fast MAS are also discussed. Several examples of protein structures (globular, membrane, fibrils, and assemblies) solved with ssNMR spectroscopy have been considered. We also discuss integrated approaches to structurally characterize challenging biological systems and some newly emanating subdisciplines in ssNMR spectroscopy.

Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins

Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves the integration of biophysical experiments and computational models. In this review, we discuss current approaches to determine conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Production of mannosylerythritol lipids: biosynthesis, multi-omics approaches, and commercial exploitation

Compilation of resources regarding MEL biosynthesis, key production parameters; available omics resources and current commercial applications, for smut fungi known to produce MELs.

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics

MOTIVATION

The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes.

RESULTS

Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure-function paradigm toward a protein structure-surface(s)-function paradigm.

AVAILABILITYAND IMPLEMENTATION

All data are available online at http://datasetmachat.drugdesign.fr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The lipopolysaccharide-transporter complex LptB2FG also displays adenylate kinase activity in vitro dependent on the binding partners LptC/LptA

Lipopolysaccharide (LPS) is an essential glycolipid that covers the surface of gram-negative bacteria. The transport of LPS involves a dedicated seven-protein transporter system called the lipopolysaccharide transport system (Lpt) machinery that physically spans the entire cell envelope. The LptB₂FG complex is an ABC transporter that hydrolyzes ATP to extract LPS from the inner membrane for transport to the outer membrane. Here, we extracted LptB₂FG directly from the inner membrane with its original lipid environment using styrene-maleic acid polymers. We found that styrene-maleic acid polymers–LptB₂FG in nanodiscs display not only ATPase activity but also a previously uncharacterized adenylate kinase (AK) activity, as it catalyzed phosphotransfer between two ADP molecules to generate ATP and AMP. The ATPase and AK activities of LptB₂FG were both stimulated by the interaction on the periplasmic side with the periplasmic LPS transport proteins LptC and LptA and inhibited by the presence of the LptC transmembrane helix. We determined that the isolated ATPase module (LptB) had weak AK activity in the absence of transmembrane proteins LptF and LptG, and one mutation in LptB that weakens its affinity for ADP led to AK activity similar to that of fully assembled complex. Thus, we conclude that LptB₂FG is capable of producing ATP from ADP, depending on the assembly of the Lpt bridge, and that this AK activity might be important to ensure efficient LPS transport in the fully assembled Lpt system.

Unravelling the Structure of the Tetrahedral Metal-Binding Site in METP3 through an Experimental and Computational Approach

Understanding the structural determinants for metal ion coordination in metalloproteins is a fundamental issue for designing metal binding sites with predetermined geometry and activity. In order to achieve this, we report in this paper the design, synthesis and metal binding properties of METP3, a homodimer made up of a small peptide, which self assembles in the presence of tetrahedrally coordinating metal ions. METP3 was obtained through a redesign approach, starting from the previously developed METP molecule. The undecapeptide sequence of METP, which dimerizes to house a Cys₄ tetrahedral binding site, was redesigned in order to accommodate a Cys₂His₂ site. The binding properties of METP3 were determined toward different metal ions. Successful assembly of METP3 with Co(II), Zn(II) and Cd(II), in the expected 2:1 stoichiometry and tetrahedral geometry was proven by UV-visible spectroscopy. CD measurements on both the free and metal-bound forms revealed that the metal coordination drives the peptide chain to fold into a turned conformation. Finally, NMR data of the Zn(II)-METP3 complex, together with a retrostructural analysis of the Cys-X-X-His motif in metalloproteins, allowed us to define the model structure. All the results establish the suitability of the short METP sequence for accommodating tetrahedral metal binding sites, regardless of the first coordination ligands.

Overview of Methods for Purification and Characterization of Metalloproteins

Metalloproteins make up one third of all proteins and perform some of the most essential reactions on earth. The unique properties of the metal ions within these proteins, and in particular of redox-active metal ions, enables the use of a number of characterization techniques. It also necessitates unique considerations in terms of purification and characterization. In this overview, we describe the considerations and methods used for metalloprotein purification and characterization. © 2021 Wiley Periodicals LLC.

Techniques for Detection of Clinical Used Heparins

Heparins and sulfated polysaccharides have been recognized as effective clinical anticoagulants for several decades. Heparins exhibit heterogeneity depending on the sources. Meanwhile, the adverse effect in the clinical uses and the adulteration of oversulfated chondroitin sulfate (OSCS) in heparins develop additional attention to analyze the purity of heparins. This review starts with the description of the classification, anticoagulant mechanism, clinical application of heparins and focuses on the existing methods of heparin analysis and detection including traditional detection methods, as well as new methods using fluorescence or gold nanomaterials as probes. The in-depth understanding of these techniques for the analysis of heparins will lay a foundation for the further development of novel methods for the detection of heparins.

Recent Advances in Biocatalysis with Chemical Modification and Expanded Amino Acid Alphabet

The two main strategies for enzyme engineering, directed evolution and rational design, have found widespread applications in improving the intrinsic activities of proteins. Although numerous advances have been achieved using these ground-breaking methods, the limited chemical diversity of the biopolymers, restricted to the 20 canonical amino acids, hampers creation of novel enzymes that Nature has never made thus far. To address this, much research has been devoted to expanding the protein sequence space via chemical modifications and/or incorporation of noncanonical amino acids (ncAAs). This review provides a balanced discussion and critical evaluation of the applications, recent advances, and technical breakthroughs in biocatalysis for three approaches: (i) chemical modification of cAAs, (ii) incorporation of ncAAs, and (iii) chemical modification of incorporated ncAAs. Furthermore, the applications of these approaches and the result on the functional properties and mechanistic study of the enzymes are extensively reviewed. We also discuss the design of artificial enzymes and directed evolution strategies for enzymes with ncAAs incorporated. Finally, we discuss the current challenges and future perspectives for biocatalysis using the expanded amino acid alphabet.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Probing the Interactions of Porphyrins with Macromolecules Using NMR Spectroscopy Techniques

Porphyrinic compounds are widespread in nature and play key roles in biological processes such as oxygen transport in blood, enzymatic redox reactions or photosynthesis. In addition, both naturally derived as well as synthetic porphyrinic compounds are extensively explored for biomedical and technical applications such as photodynamic therapy (PDT) or photovoltaic systems, respectively. Their unique electronic structures and photophysical properties make this class of compounds so interesting for the multiple functions encountered. It is therefore not surprising that optical methods are typically the prevalent analytical tool applied in characterization and processes involving porphyrinic compounds. However, a wealth of complementary information can be obtained from NMR spectroscopic techniques. Based on the advantage of providing structural and dynamic information with atomic resolution simultaneously, NMR spectroscopy is a powerful method for studying molecular interactions between porphyrinic compounds and macromolecules. Such interactions are of special interest in medical applications of porphyrinic photosensitizers that are mostly combined with macromolecular carrier systems. The macromolecular surrounding typically stabilizes the encapsulated drug and may also modify its physical properties. Moreover, the interaction with macromolecular physiological components needs to be explored to understand and control mechanisms of action and therapeutic efficacy. This review focuses on such non-covalent interactions of porphyrinic drugs with synthetic polymers as well as with biomolecules such as phospholipids or proteins. A brief introduction into various NMR spectroscopic techniques is given including chemical shift perturbation methods, NOE enhancement spectroscopy, relaxation time measurements and diffusion-ordered spectroscopy. How these NMR tools are used to address porphyrin-macromolecule interactions with respect to their function in biomedical applications is the central point of the current review.

Tautomeric preference in lumazines, deazalumazines, isoalloxazines and pyrimidines and its effect on the reactivity of alkyl groups

In lumazines, deazalumazines and pyrimidines, there are extremely low-energy "rare" tautomers (<2.3 kcal/mol), this fact perfectly explains the observed mobility of usually "non-labile" protons of methyl groups in such systems. In general, the dependence of tautomeric preference on structure correlates well with experimental findings. Thus, the activity of alkyl groups during biological transformations may be due to the presence of the corresponding thermodynamically stable tautomers.

Structure of S. pombe telomerase protein Pof8 C-terminal domain is an xRRM conserved among LARP7 proteins

La-related proteins 7 (LARP7) are a class of RNA chaperones that bind the 3' ends of RNA and are constitutively associated with their specific target RNAs. In metazoa, Larp7 binds to the long non-coding 7SK RNA as a core component of the 7SK RNP, a major regulator of eukaryotic transcription. In the ciliate Tetrahymena the LARP7 protein p65 is a component of telomerase, an essential ribonucleoprotein complex that maintains the telomeric DNA at eukaryotic chromosome ends. p65 is important for the ordered assembly of telomerase RNA (TER) with telomerase reverse transcriptase. Unexpectedly, Schizosaccharomyces pombe Pof8 was recently identified as a LARP7 protein and a core component of fission yeast telomerase essential for biogenesis. LARP7 proteins have a conserved N-terminal La motif and RRM1 (La module) and C-terminal RRM2 with specific RNA substrate recognition attributed to RRM2, first structurally characterized in p65 as an atypical RRM named xRRM. Here we present the X-ray crystal structure and NMR studies of S. pombe Pof8 RRM2. Sequence and structure comparison of Pof8 RRM2 to p65 and human Larp7 xRRMs reveals conserved features for RNA binding with the main variability in the length of the non-canonical helix α3. This study shows that Pof8 has conserved xRRM features, providing insight into TER recognition and the defining characteristics of the xRRM.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

Magnesium: The overlooked electrolyte in blood cancers?

Magnesium is an important element that has essential roles in the regulation of cell growth, division, and differentiation. Mounting evidence in the literature suggests an association between hypomagnesemia and all-cause mortality. In addition, epidemiologic studies have demonstrated that a diet poor in magnesium increases the risk of developing cancer, highlighting its importance in the field of hematology and oncology. In solid malignancies, hypomagnesemia at diagnosis portends a worse prognosis. However, little is known about prognosis in patients with hypomagnesemia and blood cancers in general; lymphoma more specifically. Hypomagnesemia has been associated with a higher viral load of the Epstein Barr virus, a virus associated with a multitude of hematologic malignancies. The role of magnesium in the immune system has been further elucidated in studies of patients with a rare primary immunodeficiency known as XMEN disease (X-linked immunodeficiency with Magnesium defect, Epstein-Barr virus (EBV) infection, and Neoplasia disease). These patients have a mutation in the MAGT1 gene, which codes for a magnesium transporter. The mutation leads to impaired T cell activation and an increased risk of developing hematologic malignancies. In this review we discuss the relevance of magnesium as an electrolyte, current measurement techniques, and the known data related to cause and prognosis of blood cancers. The goal is to use these data to stimulate additional high-quality and well powered studies to further investigate the role of magnesium in preventing cancer and improving outcomes of patients with malignancy and concomitant magnesium deficiency.

Atomic Force Microscopy: The Characterisation of Amyloid Protein Structure in Pathology

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Proteins are versatile macromolecules that perform a variety of functions and participate in virtually all cellular processes. The functionality of a protein greatly depends on its structure and alterations may result in the development of diseases. Most well-known of these are protein misfolding disorders, which include Alzheimer’s and Parkinson’s diseases as well as type 2 diabetes mellitus, where soluble proteins transition into insoluble amyloid fibrils. Atomic Force Microscopy (AFM) is capable of providing a topographical map of the protein and/or its aggregates, as well as probing the nanomechanical properties of a sample. Moreover, AFM requires relatively simple sample preparation, which presents the possibility of combining this technique with other research modalities, such as confocal laser scanning microscopy, Raman spectroscopy and stimulated emission depletion microscopy. In this review, the basic principles of AFM are discussed, followed by a brief overview of how it has been applied in biological research. Finally, we focus specifically on its use as a characterisation method to study protein structure at the nanoscale in pathophysiological conditions, considering both molecules implicated in disease pathogenesis and the plasma protein fibrinogen. In conclusion, AFM is a userfriendly tool that supplies multi-parametric data, rendering it a most valuable technique.

Efficient prediction of nucleus independent chemical shifts for polycyclic aromatic hydrocarbons

Nuclear Magnetic Resonance (NMR) is one of the most powerful experimental techniques to characterize the structure of molecules and confined liquids. Nevertheless, the complexity of the systems under investigation usually requires complementary computational studies to interpret the NMR results. In this work we focus on polycyclic aromatic hydrocarbons (PAHs), an important class of organic molecules which have been commonly used as simple analogues for the spectroscopic properties of more complex systems, such as porous disordered carbons. We use Density Functional Theory (DFT) to calculate 13C chemical shifts and Nucleus Independent Chemical Shifts (NICS) for 34 PAHs. The results show a clear molecular size dependence of the two quantities, as well as the convergence of the 13C NMR shifts towards the values observed for graphene. We then present two computationally cheap models for the prediction of NICS in simple PAHs. We show that while a simple dipolar model fails to produce accurate values, a perturbative tight-binding approach can be successfully applied for the prediction of NICS in this series of molecules, including some non-planar ones containing 5- and 7-membered rings. This model, one to two orders of magnitude faster than DFT calculations, is very promising and can be further refined in order to study more complex systems.

Intramolecular mode coupling of the isotopomers of water: a non-scalar charge density-derived perspective

Left: The BCP trajectories T(s) for H₂O for the bending (Q1) mode, the axes labels of the trajectory T(s). The green spheres correspond to the bond critical point (BCPs). Right: The corresponding T(s) for H₂O for the symmetric-stretch (Q2) mode.