The contribution of electrostatics to hydrogen exchange in the unfolded protein state

Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities

Over the last 40 years nuclear magnetic resonance (NMR) spectroscopy has established itself as one of the most versatile techniques for the characterization of biomolecules, especially proteins. Given the molecular size limitations of NMR together with recent advances in cryo-electron microscopy and artificial intelligence-assisted protein structure prediction, the bright future of NMR in structural biology has been put into question. In this mini review we argue the contrary. We discuss the unique opportunities solution NMR offers to the protein chemist that distinguish it from all other experimental or computational methods, and how it can benefit from machine learning.

Mechanistic insights into the aggregation pathway of the patient-derived immunoglobulin light chain variable domain protein FOR005

Systemic antibody light chain (AL) amyloidosis is characterized by deposition of amyloid fibrils. Prior to fibril formation, soluble oligomeric AL protein has a direct cytotoxic effect on cardiomyocytes. We focus on the patient derived λ-III AL variable domain FOR005 which is mutated at five positions with respect to the closest germline protein. Using solution-state NMR spectroscopy, we follow the individual steps involved in protein misfolding from the native to the amyloid fibril state. Unfavorable mutations in the complementary determining regions introduce a strain in the native protein structure which yields partial unfolding. Driven by electrostatic interactions, the protein converts into a high molecular weight, oligomeric, molten globule. The high local concentration of aggregation prone regions in the oligomer finally catalyzes the conversion into fibrils. The topology is determined by balanced electrostatic interactions in the fibril core implying a 180° rotational switch of the beta-sheets around the conserved disulfide bond.

Solvent paramagnetic relaxation enhancement as a versatile method for studying structure and dynamics of biomolecular systems

Solvent paramagnetic relaxation enhancement (sPRE) is a versatile nuclear magnetic resonance (NMR)-based method that allows characterization of the structure and dynamics of biomolecular systems through providing quantitative experimental information on solvent accessibility of NMR-active nuclei. Addition of soluble paramagnetic probes to the solution of a biomolecule leads to paramagnetic relaxation enhancement in a concentration-dependent manner. Here we review recent progress in the sPRE-based characterization of structural and dynamic properties of biomolecules and their complexes, and aim to deliver a comprehensive illustration of a growing number of applications of the method to various biological systems. We discuss the physical principles of sPRE measurements and provide an overview of available co-solute paramagnetic probes. We then explore how sPRE, in combination with complementary biophysical techniques, can further advance biomolecular structure determination, identification of interaction surfaces within protein complexes, and probing of conformational changes and low-population transient states, as well as deliver insights into weak, nonspecific, and transient interactions between proteins and co-solutes. In addition, we present examples of how the incorporation of solvent paramagnetic probes can improve the sensitivity of NMR experiments and discuss the prospects of applying sPRE to NMR metabolomics, drug discovery, and the study of intrinsically disordered proteins.

The Ni(II)-Binding Activity of the Intrinsically Disordered Region of Human NDRG1, a Protein Involved in Cancer Development

Nickel exposure is associated with tumors of the respiratory tract such as lung and nasal cancers, acting through still-uncharacterized mechanisms. Understanding the molecular basis of nickel-induced carcinogenesis requires unraveling the mode and the effects of Ni(II) binding to its intracellular targets. A possible Ni(II)-binding protein and a potential focus for cancer treatment is hNDRG1, a protein induced by Ni(II) through the hypoxia response pathway, whose expression correlates with higher cancer aggressiveness and resistance to chemotherapy in lung tissue. The protein sequence contains a unique C-terminal sequence of 83 residues (hNDRG1*C), featuring a three-times-repeated decapeptide, involved in metal binding, lipid interaction and post-translational phosphorylation. In the present work, the biochemical and biophysical characterization of unmodified hNDRG1*C was performed. Bioinformatic analysis assigned it to the family of the intrinsically disordered regions and the absence of secondary and tertiary structure was experimentally proven by circular dichroism and NMR. Isothermal titration calorimetry revealed the occurrence of a Ni(II)-binding event with micromolar affinity. Detailed information on the Ni(II)-binding site and on the residues involved was obtained in an extensive NMR study, revealing an octahedral paramagnetic metal coordination that does not cause any major change of the protein backbone, which is coherent with CD analysis. hNDRG1*C was found in a monomeric form by light-scattering experiments, while the full-length hNDRG1 monomer was found in equilibrium between the dimer and tetramer, both in solution and in human cell lines. The results are the first essential step for understanding the cellular function of hNDRG1*C at the molecular level, with potential future applications to clarify its role and the role of Ni(II) in cancer development.

Glycation of α-synuclein hampers its binding to synaptic-like vesicles and its driving effect on their fusion

Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders affecting the worldwide population. One of its hallmarks is the intraneuronal accumulation of insoluble Lewy bodies (LBs), which cause the death of dopaminergic neurons. α-Synuclein (αS) is the main component of these LBs and in them, it commonly contains non-enzymatic post-translational modifications, such as those resulting from its reaction with reactive carbonyl species arising as side products of the intraneuronal glycolysis (mainly methylglyoxal). Consequently, lysines of the αS found in LBs of diabetic individuals are usually carboxyethylated. A precise comprehension of the effect of Nε-(carboxyethyl)lysine (CEL) on the aggregation of αS and on its physiological function becomes crucial to fully understand the molecular mechanisms underlying the development of diabetes-induced PD. Consequently, we have here used a synthetic αS where all its Lys have been replaced by CEL moieties (αS-CEL), and we have studied how these modifications could impact on the neurotransmission mechanism. This study allows us to describe how the non-enzymatic glycosylation (glycation) affects the function of a protein like αS, involved in the pathogenesis of PD. CEL decreases the ability of αS to bind micelles, although the micelle-bound fraction of αS-CEL still displays an α-helical fold resembling that of the lipid-bound αS. However, CEL completely abolishes the affinity of αS towards synaptic-like vesicles and, consequently, it hampers its physiological function as a catalyst of the clustering and the fusion of the synaptic vesicles.

Generating Ensembles of Dynamic Misfolding Proteins

The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β2-microgloblulin (β2m) can be captured and investigated.

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.

Protein Electrostatics Investigated through Paramagnetic NMR for Nonpolar Groups

Experimental validation of theoretical models for protein electrostatics remains rare. Recently, we have developed a paramagnetic NMR-based method for de novo determination of effective near-surface electrostatic potentials, which allows for straightforward examination of electrostatic models for biomolecules. In the current work, we expand this method and demonstrate that effective near-surface electrostatic potentials can readily be determined from ¹H paramagnetic relaxation enhancement (PRE) data for protein C_αH and CH₃ groups. The experimental data were compared with those predicted from the Poisson-Boltzmann theory. The impact of structural dynamics on the effective near-surface electrostatic potentials was also assessed. The agreement between the experimental and theoretical data was particularly good for methyl ¹H nuclei. Compared to the conventional pK_a-based validation, our paramagnetic NMR-based approach can provide a far larger number of experimental data that can directly be used to examine the validity of theoretical electrostatic models for proteins.

The C-terminal tail of α-synuclein protects against aggregate replication but is critical for oligomerization

Aggregation of the 140-residue protein α-synuclein (αSN) is a key factor in the etiology of Parkinson’s disease. Although the intensely anionic C-terminal domain (CTD) of αSN does not form part of the amyloid core region or affect membrane binding ability, truncation or reduction of charges in the CTD promotes fibrillation through as yet unknown mechanisms. Here, we study stepwise truncated CTDs and identify a threshold region around residue 121; constructs shorter than this dramatically increase their fibrillation tendency. Remarkably, these effects persist even when as little as 10% of the truncated variant is mixed with the full-length protein. Increased fibrillation can be explained by a substantial increase in self-replication, most likely via fragmentation. Paradoxically, truncation also suppresses toxic oligomer formation, and oligomers that can be formed by chemical modification show reduced membrane affinity and cytotoxicity. These remarkable changes correlate to the loss of negative electrostatic potential in the CTD and highlight a double-edged electrostatic safety guard.

Farzadfard et al. present a comprehensive analysis of a range of C-terminal truncations of aSN, linking the importance of high C-terminus charge for decreased fibrillation rates. The ability to formation oligomers, to disrupt synthetic vesicles and cell toxicity was reduced with truncated aSN, aiding in understanding of the intramolecular interactions of aSN which promote/inhibit aggregation.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

13C Direct Detected NMR for Challenging Systems

Thanks to recent improvements in NMR spectrometer hardware and pulse sequence design, modern ¹³C NMR has become a useful tool for biomolecular applications. The complete assignment of a protein can be accomplished by using ¹³C detected multinuclear experiments and it can provide unique information relevant for the study of a variety of different biomolecules including paramagnetic proteins and intrinsically disordered proteins. A wide range of NMR observables can be measured, concurring to the structural and dynamic characterization of a protein in isolation, as part of a larger complex, or even inside a living cell. We present the different properties of ¹³C with respect to ¹H, which provide the rationale for the experiments developed and their application, the technical aspects that need to be faced, and the many experimental variants designed to address different cases. Application areas where these experiments successfully complement proton NMR are also described.