NMR Line Shapes and Multi-State Binding Equilibria

Biological function of proteins relies on conformational transitions and binding of specific ligands. Protein-ligand interactions are thermodynamically and kinetically coupled to conformational changes in protein structures as conceptualized by the models of pre-existing equilibria and induced fit. NMR spectroscopy is particularly sensitive to complex ligand-binding modes—NMR line-shape analysis can provide for thermodynamic and kinetic constants of ligand-binding equilibria with the site-specific resolution. However, broad use of line shape analysis is hampered by complexity of NMR line shapes in multi-state systems. To facilitate interpretation of such spectral patterns, I computationally explored systems where isomerization or dimerization of a protein (receptor) molecule is coupled to binding of a ligand. Through an extensive analysis of multiple exchange regimes for a family of three-state models, I identified signature features to guide an NMR experimentalist in recognizing specific interaction mechanisms. Results also show that distinct multistate models may produce very similar spectral patterns. I also discussed aggregation of a receptor as a possible source of spurious three-state line shapes and provided specific suggestions for complementary experiments that can ensure reliable mechanistic insight

13C-Methyl isocyanide as an NMR probe for cytochrome P450 active site

The cytochromes P450 (CYPs) play a central role in many biologically important oxidation reactions, including the metabolism of drugs and other xenobiotic compounds. Because they are often assayed as both drug targets and anti-targets, any tools that provide: (a) confirmation of active site binding and (b) structural data, would be of great utility, especially if data could be obtained in reasonably high throughput. To this end, we have developed an analog of the promiscuous heme ligand, cyanide,with a 13CH3-reporter attached. This 13C-methyl isocyanide ligand binds to bacterial (P450cam) and membrane-bound mammalian (CYP2B4) CYPs. It can be used in a rapid 1D experiment to identify binders, and provides a qualitative measure of structural changes in the active site

Archiving and disseminating integrative structure models.

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts

Letter to the Editor: 1H, 15N, and 13C chemical shift assignments of the resuscitation promoting factor domain of Rv1009 from Mycobacterium tuberculosis

International audienceNo abstract availabl

Dynamic nuclear polarization and spin-diffusion in non-conducting solids

There has been much renewed interest in dynamic nuclear polarization (DNP), particularly in the context of solid state biomolecular NMR and more recently dissolution DNP techniques for liquids. This paper reviews the role of spin diffusion in polarizing nuclear spins and discusses the role of the spin diffusion barrier, before going on to discuss some recent results.Comment: submitted to Applied Magnetic Resonance. The article should appear in a special issue that is being published in connection with the DNP Symposium help in Nottingham in August 200

Biomolecular NMR at 1.2 GHz

The development of new superconducting ceramic materials, which maintain the superconductivity at very intense magnetic fields, has prompted the development of a new generation of highly homogeneous high field magnets that has trespassed the magnetic field attainable with the previous generation of instruments. But how can biomolecular NMR benefit from this? In this work, we review a few of the notable applications that, we expect, will be blooming thanks to this newly available technology.Comment: 17 pages, 13 figure

MAS NMR detection of hydrogen bonds for protein secondary structure characterization

Hydrogen bonds are essential for protein structure and function, making experimental access to long-range interactions between amide protons and heteroatoms invaluable. Here we show that measuring distance restraints involving backbone hydrogen atoms and carbonyl- or α-carbons enables the identification of secondary structure elements based on hydrogen bonds, provides long-range contacts and validates spectral assignments. To this end, we apply specifically tailored, proton-detected 3D (H)NCOH and (H)NCAH experiments under fast magic angle spinning (MAS) conditions to microcrystalline samples of SH3 and GB1. We observe through-space, semi-quantitative correlations between protein backbone carbon atoms and multiple amide protons, enabling us to determine hydrogen bonding patterns and thus to identify β-sheet topologies and α-helices in proteins. Our approach shows the value of fast MAS and suggests new routes in probing both secondary structure and the role of functionally-relevant protons in all targets of solid-state MAS NMR

Developing a scoring function for NMR structure-based assignments using machine learning

Determining the assignment of signals received from the ex- periments (peaks) to speci_c nuclei of the target molecule in Nuclear Magnetic Resonance (NMR1) spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structure- based assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP [1] is a tool which utilizes a scoring function with a binary integer programming (BIP) model to perform the assignments. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classi_ca- tion problem. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs o_er mixed results