Rapid Protein-Ligand Affinity Determination by Photoinduced Hyperpolarized NMR.

The binding affinity determination of protein-ligand complexes is a cornerstone of drug design. State-of-the-art techniques are limited by lengthy and expensive processes. Building upon our recently introduced novel screening method utilizing photochemically induced dynamic nuclear polarization (photo-CIDNP) NMR, we provide the methodological framework to determine binding affinities within 5-15 min using 0.1 mg of protein. The accuracy of our method is demonstrated for the affinity constants of peptides binding to a PDZ domain and fragment ligands binding to the protein PIN1. The method can also be extended to measure the affinity of nonphoto-CIDNP-polarizable ligands in competition binding experiments. Finally, we demonstrate a strong correlation between the ligand-reduced signals in photo-CIDNP-based NMR fragment screening and the well-established saturation transfer difference (STD) NMR. Thus, our methodology measures protein-ligand affinities in the micro- to millimolar range in only a few minutes and informs on the binding epitope in a single-scan experiment, opening new avenues for early stage drug discovery approaches.

The potential role of liquid-liquid phase separation in the cellular fate of the compartments for unconventional protein secretion.

Eukaryotic cells have developed intricate mechanisms for biomolecule transport, particularly in stressful conditions. This interdisciplinary study delves into unconventional protein secretion (UPS) pathways activated during starvation, facilitating the export of proteins bypassing most of the components of the classical secretory machinery. Specifically, we focus on the underexplored mechanisms of the GRASP's role in UPS, particularly in biogenesis and cargo recruitment for the vesicular-like compartment for UPS. Our results show that liquid-liquid phase separation (LLPS) plays a key role in the coacervation of Grh1, the GRASP yeast homologue, under starvation-like conditions. This association seems a precursor to the Compartment for Unconventional Protein Secretion (CUPS) biogenesis. Grh1's self-association is regulated by electrostatic, hydrophobic, and hydrogen-bonding interactions. Importantly, our study demonstrates that phase-separated states of Grh1 can recruit UPS cargo under starvation-like situations. Additionally, we explore how the coacervate liquid-to-solid transition could impact cells' ability to return to normal post-stress states. Our findings offer insights into intracellular protein dynamics and cell adaptive responses to stress.

Systematic identification of structure-specific protein-protein interactions.

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

An evolutionarily conserved mechanism controls reversible amyloids of pyruvate kinase via pH-sensing regions.

Amyloids are known as irreversible aggregates associated with neurodegenerative diseases. However, recent evidence shows that a subset of amyloids can form reversibly and fulfill essential cellular functions. Yet, the molecular mechanisms regulating functional amyloids and distinguishing them from pathological aggregates remain unclear. Here, we investigate the conserved principles of amyloid reversibility by studying the essential metabolic enzyme pyruvate kinase (PK) in yeast and human cells. We demonstrate that yeast PK (Cdc19) and human PK (PKM2) form reversible amyloids through a pH-sensitive amyloid core. Stress-induced cytosolic acidification promotes aggregation via protonation of specific glutamate (yeast) or histidine (human) residues within the amyloid core. Mutations mimicking protonation cause constitutive PK aggregation, while non-protonatable PK mutants remain soluble even upon stress. Physiological PK aggregation is coupled to metabolic rewiring and glycolysis arrest, causing severe growth defects when misregulated. Our work thus identifies an evolutionarily conserved, potentially widespread mechanism regulating functional amyloids during stress.

Characterization of self-templating catalytic amyloids.

Amyloid aggregates with unique periodic structures have garnered significant attention due to their association with numerous diseases, including systemic amyloidoses and the neurodegenerative diseases Parkinson's, Alzheimer's, and Creutzfeld-Jakob. However, more recent investigations have expanded our understanding of amyloids, revealing their diverse functional biological roles. Amyloids have also been proposed to have played a significant role in prebiotic molecular evolution because of their exceptional stability, spontaneous formation in a prebiotic environment, catalytic and templating abilities, and cooperative interaction with fatty acids, polysaccharides, and nucleic acids. This chapter summarizes methods and techniques associated with studying short amyloidogenic peptides, including detailed procedures for investigating cross-templating and autocatalytic templating reactions. Since the work with amyloidogenic peptides and their aggregates present unique challenges, we have attempted to address these with essential details throughout the procedures. The lessons herein may be used in any amyloid-related research to ensure more reproducible results and reduce entrance barriers for researchers new to the field.

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.

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.

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.

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.

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.

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.

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.