NMR tools to detect protein allostery.

Allostery is a fundamental mechanism of cellular homeostasis by intra-protein communication between distinct functional sites. It is an internal process of proteins to steer interactions not only with each other but also with other biomolecules such as ligands, lipids, and nucleic acids. In addition, allosteric regulation is particularly important in enzymatic activities. A major challenge in structural and molecular biology today is unraveling allosteric sites in proteins, to elucidate the detailed mechanism of allostery and the development of allosteric drugs. Here we summarize the recently developed tools and approaches which enable the elucidation of regulatory hotspots and correlated motion in biomolecules, focusing primarily on solution-state nuclear magnetic resonance spectroscopy (NMR). These tools open an avenue towards a rational understanding of the mechanism of allostery and provide essential information for the design of allosteric drugs.

Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins.

Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes. Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.

Cell-to-cell transmitted alpha-synuclein recapitulates experimental Parkinson's disease.

Parkinson's disease is characterized by a progressive accumulation of alpha-Synuclein (αSyn) neuronal inclusions called Lewy bodies in the nervous system. Lewy bodies can arise from the cell-to-cell propagation of αSyn, which can occur via sequential steps of secretion and uptake. Here, by fusing a removable short signal peptide to the N-terminus of αSyn, we developed a novel mouse model with enhanced αSyn secretion and cell-to-cell transmission. Expression of the secreted αSyn in the mouse brain was under the control of a novel hybrid promoter in combination with adeno-associated virus serotype 9 (AAV9). This combination of promoter and viral vector induced a robust expression in neurons but not in the glia of injected mice. Biochemical characterization of the secreted αSyn revealed that, in cultured cells, this protein is released to the extracellular milieu via conventional secretion. The released αSyn is then internalized and processed by acceptor cells via the endosome-lysosome pathway indicating that the secreted αSyn is cell-to-cell transmitted. The secreted αSyn is aggregation-prone and amyloidogenic, and when expressed in the brain of wild-type non-transgenic mice, it induces a Parkinson's disease-like phenotype that includes a robust αSyn pathology in the substantia nigra, neuronal loss, neuroinflammation, and motor deficits, all the key features of experimental animal models of Parkinson's disease. In summary, a novel animal model of Parkinson's disease based on enhanced cell-to-cell transmission of αSyn was developed. The neuron-produced cell-to-cell transmitted αSyn triggers all phenotypic features of experimental Parkinson's disease in mice.

The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis.

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA.

Chemical shift transfer (CST) is a well-established technique in NMR spectroscopy that utilizes the chemical shift assignment of one protein (source) to identify chemical shifts of another (target). Given similarity between source and target systems (e.g., using homologs), CST allows the chemical shifts of the target system to be assigned using a limited amount of experimental data. In this study, we propose a deep-learning based workflow, ARTINA-CST, that automates this procedure, allowing CST to be carried out within minutes or hours of computational time and strictly without any human supervision. We characterize the efficacy of our method using three distinct synthetic and experimental datasets, demonstrating its effectiveness and robustness even when substantial differences exist between the source and target proteins. With its potential applications spanning a wide range of NMR projects, including drug discovery and protein interaction studies, ARTINA-CST is anticipated to be a valuable method that facilitates research in the field.

An Analysis of Nucleotide-Amyloid Interactions Reveals Selective Binding to Codon-Sized RNA.

Interactions between RNA and proteins are the cornerstone of many important biological processes from transcription and translation to gene regulation, yet little is known about the ancient origin of said interactions. We hypothesized that peptide amyloids played a role in the origin of life and that their repetitive structure lends itself to building interfaces with other polymers through avidity. Here, we report that short RNA with a minimum length of three nucleotides binds in a sequence-dependent manner to peptide amyloids. The 3'-5' linked RNA backbone appears to be well-suited to support these interactions, with the phosphodiester backbone and nucleobases both contributing to the affinity. Sequence-specific RNA-peptide interactions of the kind identified here may provide a path to understanding one of the great mysteries rooted in the origin of life: the origin of the genetic code.

Nanoscale insights into the local structural rearrangements of amyloid-ß induced by bexarotene.

A better understanding of the abnormal protein aggregation and the effect of anti-aggregation agents on the fibrillation pathways and the secondary structure of aggregates can determine strategies for the early treatment of dementia. Herein, we present a combination of experimental and theoretical studies providing new insights into the influence of the anti-aggregation drug bexarotene on the secondary structure of individual amyloid-ß aggregates and its primary aggregation. The molecular rearrangements and the spatial distribution of ß-sheets within individual aggregates were monitored at the nanoscale with infrared nanospectroscopy. We observed that bexarotene limits the parallel ß-sheets formation, known to be highly abundant in fibrils at later phases of the amyloid-ß aggregation composed of in-register cross-ß structure. Moreover, we applied molecular dynamics to provide molecular-level insights into the investigated system. Both theoretical and experimental results revealed that bexarotene slows down the protein aggregation process via steric effects, largely prohibiting the antiparallel to parallel ß-sheet rearrangement. We also found that bexarotene interacts not only via the single hydrogen bond formation with the peptide backbone but also with the amino acid side residue via a hydrophobic effect. The studied model of the drug-amyloid-ß interaction contributes to a better understanding of the inhibition mechanism of the amyloid-ß aggregation by the small molecule drugs. However, our nanoscale findings need to meet in vivo research requiring different analytical approaches.

Fragment Screening and Fast Micromolar Detection on a Benchtop NMR Spectrometer Boosted by Photoinduced Hyperpolarization.

Fragment-based drug design is a well-established strategy for rational drug design, with nuclear magnetic resonance (NMR) on high-field spectrometers as the method of reference for screening and hit validation. However, high-field NMR spectrometers are not only expensive, but require specialized maintenance, dedicated space, and depend on liquid helium cooling which became critical over the recurring global helium shortages. We propose an alternative to high-field NMR screening by applying the recently developed approach of fragment screening by photoinduced hyperpolarized NMR on a cryogen-free 80 MHz benchtop NMR spectrometer yielding signal enhancements of up to three orders in magnitude. It is demonstrated that it is possible to discover new hits and kick-off drug design using a benchtop NMR spectrometer at low micromolar concentrations of both protein and ligand. The approach presented performs at higher speed than state-of-the-art high-field NMR approaches while exhibiting a limit of detection in the nanomolar range. Photoinduced hyperpolarization is known to be inexpensive and simple to be implemented, which aligns greatly with the philosophy of benchtop NMR spectrometers. These findings open the way for the use of benchtop NMR in near-physiological conditions for drug design and further life science applications.

Ultrafast Fragment Screening Using Photo-Hyperpolarized (CIDNP) NMR.

While nuclear magnetic resonance (NMR) is regarded as a reference in fragment-based drug design, its implementation in a high-throughput manner is limited by its lack of sensitivity resulting in long acquisition times and high micromolar sample concentrations. Several hyperpolarization approaches could, in principle, improve the sensitivity of NMR also in drug research. However, photochemically induced dynamic nuclear polarization (photo-CIDNP) is the only method that is directly applicable in aqueous solution and agile for scalable implementation using off-the-shelf hardware. With the use of photo-CIDNP, this work demonstrates the detection of weak binders in the millimolar affinity range using low micromolar concentrations down to 5 µM of ligand and 2 µM of target, thereby exploiting the photo-CIDNP-induced polarization twice: (i) increasing the signal-to-noise by one to two orders in magnitude and (ii) polarization-only of the free non-bound molecule allowing identification of binding by polarization quenching, yielding another factor of hundred in time when compared with standard techniques. The interaction detection was performed with single-scan NMR experiments of a duration of 2 to 5 s. Taking advantage of the readiness of photo-CIDNP setup implementation, an automated flow-through platform was designed to screen samples at a screening rate of 1500 samples per day. Furthermore, a 212 compounds photo-CIDNP fragment library is presented, opening an avenue toward a comprehensive fragment-based screening method.

NMRtist: an online platform for automated biomolecular NMR spectra analysis.

SUMMARY: We present NMRtist, an online platform that combines deep learning, large-scale optimization and cloud computing to automate protein NMR spectra analysis. Our website provides virtual storage for NMR spectra deposition together with a set of applications designed for automated peak picking, chemical shift assignment and protein structure determination. The system can be used by non-experts and allows protein assignments and structures to be determined within hours after the measurements, strictly without any human intervention. AVAILABILITY AND IMPLEMENTATION: NMRtist is freely available to non-commercial users at https://nmrtist.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Efficient Method of Expression and Purification of Amyloid-Beta (Aß1-42) Peptide from E. coli.

Amyloid-beta (Aß) aggregation into soluble oligomers and fibril formation are associated with Alzheimer's disease (AD) pathogenesis. Aß1-42 is the major form of the Aß peptide present in neuritic plaques and shown to be neurotoxic both in vivo and in vitro. However, understanding the mechanism of its toxicity, aggregation, and other biochemical properties is limited because of its difficult production (recombinant or synthetic) and irreproducibility issues attributed to batch-to-batch preparation differences. Chemically synthetic Aß1-42 is now well established, but it always introduces up to 5% D-isomers along with its L-isomeric form, and thus it is not fruitful for biochemical/structural studies. Here, we optimized an efficient published method for expression and purification of Aß1-42 upon overexpression in Escherichia coli (E. coli) that provides a satisfactory yield as well as minimizes the variability between batch preparations. With the present protocol, ~7-8 mg/liter of unlabeled peptide and ~3.5-4 mg/liter for 13C,15N-labeled (double-labeled) Aß1-42 were obtained.

Multi-Dimensional Structure and Dynamics Landscape of Proteins in Mammalian Cells Revealed by In-Cell NMR.

Governing function, half-life and subcellular localization, the 3D structure and dynamics of proteins are in nature constantly changing in a tightly regulated manner to fulfill the physiological and adaptive requirements of the cells. To find evidence for this hypothesis, we applied in-cell NMR to three folded model proteins and propose that the splitting of cross peaks constitutes an atomic fingerprint of distinct structural states that arise from multiple target binding co-existing inside mammalian cells. These structural states change upon protein loss of function or subcellular localisation into distinct cell compartments. In addition to peak splitting, we observed NMR signal intensity attenuations indicative of transient interactions with other molecules and dynamics on the microsecond to millisecond time scale.

Protein structure determination in human cells by in-cell NMR and a reporter system to optimize protein delivery or transexpression.

Most experimental methods for structural biology proceed in vitro and therefore the contribution of the intracellular environment on protein structure and dynamics is absent. Studying proteins at atomic resolution in living mammalian cells has been elusive due to the lack of methodologies. In-cell nuclear magnetic resonance spectroscopy (in-cell NMR) is an emerging technique with the power to do so. Here, we improved current methods of in-cell NMR by the development of a reporter system that allows monitoring the delivery of exogenous proteins into mammalian cells, a process that we called here "transexpression". The reporter system was used to develop an efficient protocol for in-cell NMR which enables spectral acquisition with higher quality for both disordered and folded proteins. With this method, the 3D atomic resolution structure of the model protein GB1 in human cells was determined with a backbone root-mean-square deviation (RMSD) of 1.1 Å.

Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA.

Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.

Atomic resolution protein allostery from the multi-state structure of a PDZ domain.

Recent methodological advances in solution NMR allow the determination of multi-state protein structures and provide insights into structurally and dynamically correlated protein sites at atomic resolution. This is demonstrated in the present work for the well-studied PDZ2 domain of protein human tyrosine phosphatase 1E for which protein allostery had been predicted. Two-state protein structures were calculated for both the free form and in complex with the RA-GEF2 peptide using the exact nuclear Overhauser effect (eNOE) method. In the apo protein, an allosteric conformational selection step comprising almost 60% of the domain was detected with an "open" ligand welcoming state and a "closed" state that obstructs the binding site by changing the distance between the ß-sheet 2, α-helix 2, and sidechains of residues Lys38 and Lys72. The observed induced fit-type apo-holo structural rearrangements are in line with the previously published evolution-based analysis covering ~25% of the domain with only a partial overlap with the protein allostery of the open form. These presented structural studies highlight the presence of a dedicated highly optimized and complex dynamic interplay of the PDZ2 domain owed by the structure-dynamics landscape.

The next decade of protein structure.

With recent dramatic advances in various techniques used for protein structure research, we asked researchers to comment on the next exciting questions for the field and about how these techniques will advance our knowledge not only about proteins but also about human health and diseases.

Optimization and validation of multi-state NMR protein structures using structural correlations.

Recent advances in the field of protein structure determination using liquid-state NMR enable the elucidation of multi-state protein conformations that can provide insight into correlated and non-correlated protein dynamics at atomic resolution. So far, NMR-derived multi-state structures were typically evaluated by means of visual inspection of structure superpositions, target function values that quantify the violation of experimented restraints and root-mean-square deviations that quantify similarity between conformers. As an alternative or complementary approach, we present here the use of a recently introduced structural correlation measure, PDBcor, that quantifies the clustering of protein states as an additional measure for multi-state protein structure analysis. It can be used for various assays including the validation of experimental distance restraints, optimization of the number of protein states, estimation of protein state populations, identification of key distance restraints, NOE network analysis and semiquantitative analysis of the protein correlation network. We present applications for the final quality analysis stages of typical multi-state protein structure calculations.

Non-invasive imaging of tau-targeted probe uptake by whole brain multi-spectral optoacoustic tomography.

PURPOSE: Abnormal tau accumulation within the brain plays an important role in tauopathies such as Alzheimer's disease and frontotemporal dementia. High-resolution imaging of tau deposits at the whole-brain scale in animal disease models is highly desired. METHODS: We approached this challenge by non-invasively imaging the brains of P301L mice of 4-repeat tau with concurrent volumetric multi-spectral optoacoustic tomography (vMSOT) at ~ 115 µm spatial resolution using the tau-targeted pyridinyl-butadienyl-benzothiazole derivative PBB5 (i.v.). In vitro probe characterization, concurrent vMSOT and epi-fluorescence imaging of in vivo PBB5 targeting (i.v.) was performed in P301L and wild-type mice, followed by ex vivo validation using AT-8 antibody for phosphorylated tau. RESULTS: PBB5 showed specific binding to recombinant K18 tau fibrils by fluorescence assay, to post-mortem Alzheimer's disease brain tissue homogenate by competitive binding against [11C]PBB3 and to tau deposits (AT-8 positive) in post-mortem corticobasal degeneration and progressive supranuclear palsy brains. Dose-dependent optoacoustic and fluorescence signal intensities were observed in the mouse brains following i.v. administration of different concentrations of PBB5. In vivo vMSOT brain imaging of P301L mice showed higher retention of PBB5 in the tau-laden cortex and hippocampus compared to wild-type mice, as confirmed by ex vivo vMSOT, epi-fluorescence, multiphoton microscopy, and immunofluorescence staining. CONCLUSIONS: We demonstrated non-invasive whole-brain imaging of tau in P301L mice with vMSOT system using PBB5 at a previously unachieved ~ 115 µm spatial resolution. This platform provides a new tool to study tau spreading and clearance in a tauopathy mouse model, foreseeable in monitoring tau targeting putative therapeutics.

An improved, time-efficient approach to extract accurate distance restraints for NMR2 structure calculation.

Exact nuclear Overhauser enhancement (eNOE) yields highly accurate, ensemble averaged 1H-1H distance restraints with an accuracy of up to 0.1 Šfor the multi-state structure determination of proteins as well as for nuclear magnetic resonance molecular replacement (NMR2) to determine the structure of the protein-ligand interaction site in a time-efficient manner. However, in the latter application, the acquired eNOEs lack the obtainable precision of 0.1 Šbecause of the asymmetrical nature of the filtered nuclear Overhauser enhancement spectroscopy (NOESY) experiment used in NMR2. This error is further propagated to the eNOE equations used to fit and extract the distance restraints. In this work, a new analysis method is proposed to obtain inter-molecular distance restraints from the filtered NOESY spectrum more accurately and intuitively by dividing the NOE cross peak by the corresponding diagonal peak of the ligand. The method termed diagonal-normalised eNOEs was tested on the data acquired by on the complex of PIN1 and a small, weak-binding phenylimidazole fragment. NMR2 calculations performed using the distances derived from diagonal-normalised eNOEs yielded the right orientation of the fragment in the binding pocket and produced a structure that more closely resembles the benchmark X-ray structure (2XP6) with an average heavy-atom root-mean-square deviation (RMSD) of 1.681 Šwith respect to it, when compared to the one produced with traditional NMR2 with an average heavy atom RMSD of 3.628 Å. This is attributed to the higher precision of the evaluated distance restraints.