Structure-Free Validation of Residual Dipolar Coupling and Paramagnetic Relaxation Enhancement Measurements of Disordered Proteins

The ribosome lowers the entropic penalty of protein folding

Most proteins fold during biosynthesis on the ribosome1, and co-translational folding energetics, pathways and outcomes of many proteins have been found to differ considerably from those in refolding studies2-10. The origin of this folding modulation by the ribosome has remained unknown. Here we have determined atomistic structures of the unfolded state of a model protein on and off the ribosome, which reveal that the ribosome structurally expands the unfolded nascent chain and increases its solvation, resulting in its entropic destabilization relative to the peptide chain in isolation. Quantitative 19F NMR experiments confirm that this destabilization reduces the entropic penalty of folding by up to 30 kcal mol-1 and promotes formation of partially folded intermediates on the ribosome, an observation that extends to other protein domains and is obligate for some proteins to acquire their active conformation. The thermodynamic effects also contribute to the ribosome protecting the nascent chain from mutation-induced unfolding, which suggests a crucial role of the ribosome in supporting protein evolution. By correlating nascent chain structure and dynamics to their folding energetics and post-translational outcomes, our findings establish the physical basis of the distinct thermodynamics of co-translational protein folding.

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

Conformational Variability of Amyloid-β and the Morphological Diversity of Its Aggregates

Protein folding is the most fundamental and universal example of biomolecular self-organization and is characterized as an intramolecular process. In contrast, amyloidogenic proteins can interact with one another, leading to protein aggregation. The energy landscape of amyloid fibril formation is characterized by many minima for different competing low-energy structures and, therefore, is much more enigmatic than that of multiple folding pathways. Thus, to understand the entire energy landscape of protein aggregation, it is important to elucidate the full picture of conformational changes and polymorphisms of amyloidogenic proteins. This review provides an overview of the conformational diversity of amyloid-β (Aβ) characterized from experimental and theoretical approaches. Aβ exhibits a high degree of conformational variability upon transiently interacting with various binding molecules in an unstructured conformation in a solution, forming an α-helical intermediate conformation on the membrane and undergoing a structural transition to the β-conformation of amyloid fibrils. This review also outlines the structural polymorphism of Aβ amyloid fibrils depending on environmental factors. A comprehensive understanding of the energy landscape of amyloid formation considering various environmental factors will promote drug discovery and therapeutic strategies by controlling the fibril formation pathway and targeting the consequent morphology of aggregated structures.

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.

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales─picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure-function relationships require atomistic knowledge of the populated states in the ensemble. In this Perspective, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R _h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

Structural Insight into IAPP-Derived Amyloid Inhibitors and Their Mechanism of Action

Designed peptides derived from the islet amyloid polypeptide (IAPP) cross-amyloid interaction surface with Aβ (termed interaction surface mimics or ISMs) have been shown to be highly potent inhibitors of Aβ amyloid self-assembly. However, the molecular mechanism of their function is not well understood. Using solution-state and solid-state NMR spectroscopy in combination with ensemble-averaged dynamics simulations and other biophysical methods including TEM, fluorescence spectroscopy and microscopy, and DLS, we characterize ISM structural preferences and interactions. We find that the ISM peptide R3-GI is highly dynamic, can adopt a β-like structure, and oligomerizes into colloid-like assemblies in a process that is reminiscent of liquid-liquid phase separation (LLPS). Our results suggest that such assemblies yield multivalent surfaces for interactions with Aβ40. Sequestration of substrates into these colloid-like structures provides a mechanistic basis for ISM function and the design of novel potent anti-amyloid molecules.

NMR Characterization of Conformational Dynamics and Molecular Assemblies of Proteins

Dynamic conformational transitions and molecular assemblies are essential properties of proteins, and relevant to their biological and pathological functions. Neurodegenerative diseases are known to be caused by abnormal, toxic assemblies of related proteins, e.g., amyloid β (Aβ) in Alzheimer's disease. Growing evidence indicates that the aggregation of various amyloidogenic proteins, including Aβ, can be highly enhanced at glycolipid membranes, suggesting that dynamic glycolipid-dependent conformational changes of proteins constitute crucial steps for their subsequent pathogenic amyloid fibril formation. It has also been proposed that several proteins, including molecular chaperones, can capture amyloidogenic proteins and thereby suppress their fibrillization. NMR spectroscopy provides a powerful tool for characterizing the conformational dynamics and intermolecular interactions of proteins, as well as for exploring transiently formed weak interactions among proteins in solution with various biomolecules, such as glycolipids. Our research group therefore attempted to elucidate the structural basis of protein-glycolipid and protein-protein interactions that either promote or suppress molecular assemblies of amyloidogenic proteins, using both solution and solid-state NMR methods in conjunction with other biophysical techniques. Our findings provide structural views of molecular processes involving amyloidogenic proteins of clinical and pathological interest and offer clues for the development of drugs to prevent and treat neurodegenerative diseases.

Conformational Ensemble of Disordered Proteins Probed by Solvent Paramagnetic Relaxation Enhancement (sPRE)

Characterization of the conformational ensemble of disordered proteins is highly important for understanding protein folding and aggregation mechanisms, but remains a computational and experimental challenge owing to the dynamic nature of these proteins. New observables that can provide unique insights into transient residual structures in disordered proteins are needed. Here using denatured ubiquitin as a model system, NMR solvent paramagnetic relaxation enhancement (sPRE) measurements provide an accurate and highly sensitive probe for detecting low populations of residual structure in a disordered protein. Furthermore, a new ensemble calculation approach based on sPRE restraints in conjunction with residual dipolar couplings (RDCs) and small-angle X-ray scattering (SAXS) is used to define the conformational ensemble of disordered proteins at atomic resolution. The approach presented should be applicable to a wide range of dynamic macromolecules.

Probing Protein-Protein Interactions Using Asymmetric Labeling and Carbonyl-Carbon Selective Heteronuclear NMR Spectroscopy

Protein-protein interactions (PPIs) regulate a plethora of cellular processes and NMR spectroscopy has been a leading technique for characterizing them at the atomic resolution. Technically, however, PPIs characterization has been challenging due to multiple samples required to characterize the hot spots at the protein interface. In this paper, we review our recently developed methods that greatly simplify PPI studies, which minimize the number of samples required to fully characterize residues involved in the protein-protein binding interface. This original strategy combines asymmetric labeling of two binding partners and the carbonyl-carbon label selective (CCLS) pulse sequence element implemented into the heteronuclear single quantum correlation (¹H-15N HSQC) spectra. The CCLS scheme removes signals of the J-coupled 15N⁻13C resonances and records simultaneously two individual amide fingerprints for each binding partner. We show the application to the measurements of chemical shift correlations, residual dipolar couplings (RDCs), and paramagnetic relaxation enhancements (PRE). These experiments open an avenue for further modifications of existing experiments facilitating the NMR analysis of PPIs.

Simultaneous detection of intra- and inter-molecular paramagnetic relaxation enhancements in protein complexes

Paramagnetic relaxation enhancement (PRE) measurements constitute a powerful approach for detecting both permanent and transient protein-protein interactions. Typical PRE experiments require an intrinsic or engineered paramagnetic site on one of the two interacting partners; while a second, diamagnetic binding partner is labeled with stable isotopes (¹⁵N or ¹³C). Multiple paramagnetic labeled centers or reversed labeling schemes are often necessary to obtain sufficient distance restraints to model protein-protein complexes, making this approach time consuming and expensive. Here, we show a new strategy that combines a modified pulse sequence (¹H_N-Γ₂-CCLS) with an asymmetric labeling scheme to enable the detection of both intra- and inter-molecular PREs simultaneously using only one sample preparation. We applied this strategy to the non-covalent dimer of ubiquitin. Our method confirmed the previously identified binding interface for the transient di-ubiquitin complex, and at the same time, unveiled the internal structural dynamics rearrangements of ubiquitin upon interaction. In addition to reducing the cost of sample preparation and speed up PRE measurements, by detecting the intra-molecular PRE this new strategy will make it possible to measure and calibrate inter-molecular distances more accurately for both symmetric and asymmetric protein-protein complexes.

Application of NMR to studies of intrinsically disordered proteins

The prevalence of intrinsically disordered protein regions, particularly in eukaryotic proteins, and their clear functional advantages for signaling and gene regulation have created an imperative for high-resolution structural and mechanistic studies. NMR spectroscopy has played a central role in enhancing not only our understanding of the intrinsically disordered native state, but also how that state contributes to biological function. While pathological functions associated with protein aggregation are well established, it has recently become clear that disordered regions also mediate functionally advantageous assembly into high-order structures that promote the formation of membrane-less sub-cellular compartments and even hydrogels. Across the range of functional assembly states accessed by disordered regions, post-translational modifications and regulatory macromolecular interactions, which can also be investigated by NMR spectroscopy, feature prominently. Here we will explore the many ways in which NMR has advanced our understanding of the physical-chemical phase space occupied by disordered protein regions and provide prospectus for the future role of NMR in this emerging and exciting field.