Describing intrinsically disordered proteins at atomic resolution by NMR

Experimental methods to study the structure and dynamics of intrinsically disordered regions in proteins

Eukaryotic proteins often feature long stretches of amino acids that lack a well-defined three-dimensional structure and are referred to as intrinsically disordered proteins (IDPs) or regions (IDRs). Although these proteins challenge conventional structure-function paradigms, they play vital roles in cellular processes. Recent progress in experimental techniques, such as NMR spectroscopy, single molecule FRET, high speed AFM and SAXS, have provided valuable insights into the biophysical basis of IDP function. This review discusses the advancements made in these techniques particularly for the study of disordered regions in proteins. In NMR spectroscopy new strategies such as 13C detection, non-uniform sampling, segmental isotope labeling, and rapid data acquisition methods address the challenges posed by spectral overcrowding and low stability of IDPs. The importance of various NMR parameters, including chemical shifts, hydrogen exchange rates, and relaxation measurements, to reveal transient secondary structures within IDRs and IDPs are presented. Given the high flexibility of IDPs, the review outlines NMR methods for assessing their dynamics at both fast (ps-ns) and slow (μs-ms) timescales. IDPs exert their functions through interactions with other molecules such as proteins, DNA, or RNA. NMR-based titration experiments yield insights into the thermodynamics and kinetics of these interactions. Detailed study of IDPs requires multiple experimental techniques, and thus, several methods are described for studying disordered proteins, highlighting their respective advantages and limitations. The potential for integrating these complementary techniques, each offering unique perspectives, is explored to achieve a comprehensive understanding of IDPs.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning-based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long timescale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We observe that folding-upon-binding predominantly proceeds through a multi-step induced fit mechanism with several intermediates and do not find evidence for the existence of canonical conformational selection pathways. We observe four kinetically separated native-like bound states that interconvert on timescales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

Liquid-liquid phase separation in DNA double-strand breaks repair

DNA double-strand breaks (DSBs) are the fatal type of DNA damage mostly induced by exposure genome to ionizing radiation or genotoxic chemicals. DSBs are mainly repaired by homologous recombination (HR) and nonhomologous end joining (NHEJ). To repair DSBs, a large amount of DNA repair factors was observed to be concentrated at the end of DSBs in a specific spatiotemporal manner to form a repair center. Recently, this repair center was characterized as a condensate derived from liquid-liquid phase separation (LLPS) of key DSBs repair factors. LLPS has been found to be the mechanism of membraneless organelles formation and plays key roles in a variety of biological processes. In this review, the recent advances and mechanisms of LLPS in the formation of DSBs repair-related condensates are summarized.

Kinetic Resolution of the Atomic 3D Structures Formed by Ground and Excited Conformational States in an RNA Dynamic Ensemble

Knowing the 3D structures formed by the various conformations populating the RNA free-energy landscape, their relative abundance, and kinetic interconversion rates is required to obtain a quantitative and predictive understanding of how RNAs fold and function at the atomic level. While methods integrating ensemble-averaged experimental data with computational modeling are helping define the most abundant conformations in RNA ensembles, elucidating their kinetic rates of interconversion and determining the 3D structures of sparsely populated short-lived RNA excited conformational states (ESs) remains challenging. Here, we developed an approach integrating Rosetta-FARFAR RNA structure prediction with NMR residual dipolar couplings and relaxation dispersion that simultaneously determines the 3D structures formed by the ground-state (GS) and ES subensembles, their relative abundance, and kinetic rates of interconversion. The approach is demonstrated on HIV-1 TAR, whose six-nucleotide apical loop was previously shown to form a sparsely populated (∼13%) short-lived (lifetime ∼ 45 μs) ES. In the GS, the apical loop forms a broad distribution of open conformations interconverting on the pico-to-nanosecond time scale. Most residues are unpaired and preorganized to bind the Tat-superelongation protein complex. The apical loop zips up in the ES, forming a narrow distribution of closed conformations, which sequester critical residues required for protein recognition. Our work introduces an approach for determining the 3D ensemble models formed by sparsely populated RNA conformational states, provides a rare atomic view of an RNA ES, and kinetically resolves the atomic 3D structures of RNA conformational substates, interchanging on time scales spanning 6 orders of magnitude, from picoseconds to microseconds.

Phase Transitions of Associative Biomacromolecules

Multivalent proteins and nucleic acids, collectively referred to as multivalent associative biomacromolecules, provide the driving forces for the formation and compositional regulation of biomolecular condensates. Here, we review the key concepts of phase transitions of aqueous solutions of associative biomacromolecules, specifically proteins that include folded domains and intrinsically disordered regions. The phase transitions of these systems come under the rubric of coupled associative and segregative transitions. The concepts underlying these processes are presented, and their relevance to biomolecular condensates is discussed.

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long-time scale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We do not, however, find evidence for the existence of canonical conformational selection or induced fit binding pathways. We observe four kinetically separated native-like bound states that interconvert on time scales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

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.

Molecular Origin of Internal Friction in Intrinsically Disordered Proteins

Protein folding and dynamics are controlled by an interplay of thermal and viscosity effects. The effect of viscous drag through the solvent molecules is described by the classic Kramers theory in the high friction limit, which considers the dampening of the reactant molecules in the solution and quantifies the dependence of the reaction rate on the frictional drag. In addition to the external energy dissipation originating from the surrounding solvent molecules, there is an additional mode of internal energy dissipative force operative within the polypeptide chain reflecting the internal resistance of the chain to its conformational alterations. This dry, solvent-independent intrinsic frictional drag is termed internal friction. In the case of natively folded proteins, the physical origin of internal friction is primarily attributed to the intrachain interactions or other nonnative interactions in their compact states. However, the molecular origin of internal friction in intrinsically disordered proteins (IDPs) remains elusive.In this Account, we address this fundamental issue: what are the principal drivers of viscosity-independent (dry) friction in highly solvated, expanded, conformationally flexible, rapidly fluctuating IDPs that do not possess persistent intrachain interactions? IDPs exhibit diffusive conformational dynamics that is predominantly dominated by the segmental motion of the backbone arising due to the dihedral rotations in the Ramachandran Φ-Ψ space. The physical origin of friction in a complex biopolymeric system such as IDPs can be described by classic polymer models, namely, Rouse/Zimm models with internal friction. These one-dimensional models do not invoke torsional fluctuation components. Kuhn's classic description includes the connection between internal friction and microscopic dihedral hopping. Based on our time-resolved fluorescence anisotropy results, we describe that the sequence-dependent, collective, short-range backbone dihedral rotations govern localized internal friction in an archetypal IDP, namely, α-synuclein. The highly sensitive, residue-specific fluorescence depolarization kinetics offers a potent methodology to characterize and quantify the directional decorrelation engendered due to the short-range dihedral relaxation of the polypeptide backbone in the dihedral space. We utilized this characteristic relaxation time scale as our dynamic readout to quantify the site-specific frictional component. Our linear viscosity-dependent model of torsional relaxation time scale furnished a finite nonzero time constant at the zero solvent viscosity representing the solvent-independent internal friction. These results unveil the effect of the degree of dihedral restraining parameter on the internal friction component by showing that a restrained proline residue imparts higher torsional stiffness in the chain segments and, therefore, exhibits higher internal friction. This Account sheds light on the molecular underpinning of the sequence-specific internal friction in IDPs and will be of interest to unmask the role of internal friction in a diverse range of biomolecular processes involving binding-induced folding, allosteric interaction, protein misfolding and aggregation, and biomolecular condensation via phase separation.

Conformational ensemble of amyloid-forming semenogelin 1 peptide SEM1(68-107) by NMR spectroscopy and MD simulations

SEM1(68-107) is a peptide corresponding to the region of semenogelin 1 protein from 68 to 107 amino acid position. SEM1(68-107) is an abundant component of semen, which participates in HIV infection enhanced by amyloid fibrils forming. To understand the causes influencing amyloid fibril formation, it is necessary to determine the spatial structure of SEM1(68-107). It was shown that the determination of SEM1(68-107) structure is complicated by the non-informative NMR spectra due to the high intramolecular mobility of peptides. The complementary approach based on the geometric restrictions of individual peptide fragments and molecular modeling was used for the determination of the spatial structure of SEM1(68-107). The N- (SEM1(68-85)) and C-terminuses (SEM1(86-107)) of SEM1(68-107) were chosen as two individual peptide fragments. SEM1(68-85) and SEM1(86-107) structures were established with NMR and circular dichroism CD spectroscopies. These regions were used as geometric restraints for the SEM1(68-107) structure modeling. Even though most of the SEM1(68-107) peptide is unstructured, our detailed analysis revealed the following structured elements: N-terminus (70His-84Gln) forms an α-helix, (86Asp-94Thr) and (101Gly-103Ser) regions fold into 3₁₀-helixes. The absence of a SEM1(68-107) rigid conformation leads to instability of these secondary structure regions. The calculated SEM1(68-107) structure is in good agreement with experimental values of hydrodynamic radius and dihedral angles obtained by NMR spectroscopy. This testifies the adequacy of a combined approach based on the use of peptide fragment structures for the molecular modeling formation of full-size peptide spatial structure.

How phosphorylation impacts intrinsically disordered proteins and their function

Phosphorylation is the most common post-translational modification (PTM) in eukaryotes, occurring particularly frequently in intrinsically disordered proteins (IDPs). These proteins are highly flexible and dynamic by nature. Thus, it is intriguing that the addition of a single phosphoryl group to a disordered chain can impact its function so dramatically. Furthermore, as many IDPs carry multiple phosphorylation sites, the number of possible states increases, enabling larger complexities and novel mechanisms. Although a chemically simple and well-understood process, the impact of phosphorylation on the conformational ensemble and molecular function of IDPs, not to mention biological output, is highly complex and diverse. Since the discovery of the first phosphorylation site in proteins 75 years ago, we have come to a much better understanding of how this PTM works, but with the diversity of IDPs and their capacity for carrying multiple phosphoryl groups, the complexity grows. In this Essay, we highlight some of the basic effects of IDP phosphorylation, allowing it to serve as starting point when embarking on studies into this topic. We further describe how recent complex cases of multisite phosphorylation of IDPs have been instrumental in widening our view on the effect of protein phosphorylation. Finally, we put forward perspectives on the phosphorylation of IDPs, both in relation to disease and in context of other PTMs; areas where deep insight remains to be uncovered.

Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states

Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design.

Measuring thermodynamic preferences to form non-native conformations in nucleic acids using ultraviolet melting

Thermodynamic preferences to form non-native conformations are crucial for understanding how nucleic acids fold and function. However, they are difficult to measure experimentally because this requires accurately determining the population of minor low-abundance (<10%) conformations in a sea of other conformations. Here, we show that melting experiments enable facile measurements of thermodynamic preferences to adopt nonnative conformations in DNA and RNA. The key to this "delta-melt" approach is to use chemical modifications to render specific minor non-native conformations the major state. The validity and robustness of delta-melt is established for four different non-native conformations under various physiological conditions and sequence contexts through independent measurements of thermodynamic preferences using NMR. Delta-melt is faster relative to NMR, simple, and cost-effective and enables thermodynamic preferences to be measured for exceptionally low-populated conformations. Using delta-melt, we obtained rare insights into conformational cooperativity, obtaining evidence for significant cooperativity (1.0 to 2.5 kcal/mol) when simultaneously forming two adjacent Hoogsteen base pairs. We also measured the thermodynamic preferences to form G-C⁺ and A-T Hoogsteen and A-T base open states for nearly all 16 trinucleotide sequence contexts and found distinct sequence-specific variations on the order of 2 to 3 kcal/mol. This rich landscape of sequence-specific non-native minor conformations in the DNA double helix may help shape the sequence specificity of DNA biochemistry. Thus, melting experiments can now be used to access thermodynamic information regarding regions of the free energy landscape of biomolecules beyond the native folded and unfolded conformations.

A modular approach to map out the conformational landscapes of unbound intrinsically disordered proteins

SignificanceIntrinsically disordered proteins have the unique ability to morph in response to multiple partners and thereby process sophisticated inputs and outputs. It is, however, a mystery whether their response is passive, that is, entirely determined by the partner, or controlled via an internal, yet unknown, folding mechanism. Here we introduce an approach to examine this key question and demonstrate its potential by dissecting the conformational properties of the partially disordered protein NCBD and obtaining important clues about how it performs its biological function.

Competing interactions give rise to two-state behavior and switch-like transitions in charge-rich intrinsically disordered proteins

The most commonly occurring intrinsically disordered proteins (IDPs) are polyampholytes, which are defined by the duality of low net charge per residue and high fractions of charged residues. Recent experiments have uncovered nuances regarding sequence–ensemble relationships of model polyampholytic IDPs. These include differences in conformational preferences for sequences with lysine vs. arginine and the suggestion that well-mixed sequences form a range of conformations, including globules, conformations with ensemble averages that are reminiscent of ideal chains, or self-avoiding walks. Here, we explain these observations by analyzing results from atomistic simulations. We find that polyampholytic IDPs generally sample two distinct stable states, namely, globules and self-avoiding walks. Globules are favored by electrostatic attractions between oppositely charged residues, whereas self-avoiding walks are favored by favorable free energies of hydration of charged residues. We find sequence-specific temperatures of bistability at which globules and self-avoiding walks can coexist. At these temperatures, ensemble averages over coexisting states give rise to statistics that resemble ideal chains without there being an actual counterbalancing of intrachain and chain-solvent interactions. At equivalent temperatures, arginine-rich sequences tilt the preference toward globular conformations whereas lysine-rich sequences tilt the preference toward self-avoiding walks. We also identify differences between aspartate- and glutamate-containing sequences, whereby the shorter aspartate side chain engenders preferences for metastable, necklace-like conformations. Finally, although segregation of oppositely charged residues within the linear sequence maintains the overall two-state behavior, compact states are highly favored by such systems.

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.

Atomic resolution dynamics of cohesive interactions in phase-separated Nup98 FG domains

Cohesive FG domains assemble into a condensed phase forming the selective permeability barrier of nuclear pore complexes. Nanoscopic insight into fundamental cohesive interactions has long been hampered by the sequence heterogeneity of native FG domains. We overcome this challenge by utilizing an engineered perfectly repetitive sequence and a combination of solution and magic angle spinning NMR spectroscopy. We map the dynamics of cohesive interactions in both phase-separated and soluble states at atomic resolution using TROSY for rotational correlation time (TRACT) measurements. We find that FG repeats exhibit nanosecond-range rotational correlation times and remain disordered in both states, although FRAP measurements show slow translation of phase-separated FG domains. NOESY measurements enable the direct detection of contacts involved in cohesive interactions. Finally, increasing salt concentration and temperature enhance phase separation and decrease local mobility of FG repeats. This lower critical solution temperature (LCST) behaviour indicates that cohesive interactions are driven by entropy.

The permeability barrier of nuclear pores is formed by disordered and yet self-interacting FG repeat domains, whose sequence heterogeneity is a challenge for mechanistic insights. Here the authors overcome this challenge and characterize the protein’s dynamics by applying NMR techniques to an FG phase system that has been simplified to its essentials.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Phase separation of the mammalian prion protein: physiological and pathological perspectives

Abnormal phase transitions have been implicated in the occurrence of proteinopathies. Disordered proteins with nucleic acid binding ability drive the formation of reversible micron-sized condensates capable of controlling nucleic acid processing/transport. This mechanism, achieved via liquid-liquid phase separation (LLPS), underlies the formation of long-studied membraneless organelles (e.g., nucleolus) and various transient condensates formed by driver proteins. The prion protein (PrP) is not a classical nucleic acid-binding protein. However, it binds nucleic acids with high affinity, undergoes nucleocytoplasmic shuttling, contains a long intrinsically disordered region rich in glycines and evenly spaced aromatic residues, among other biochemical/biophysical properties of bona fide drivers of phase transitions. Because of this, our group and others have characterized LLPS of recombinant PrP. In vitro phase separation of PrP is modulated by nucleic acid aptamers, and, depending on the aptamer conformation, the liquid droplets evolve to solid-like species. Herein we discuss recent studies and previous evidence supporting PrP phase transitions. We focus on the central role of LLPS related to PrP physiology and pathology, with a special emphasis on the interaction of PrP with different ligands, such as proteins and nucleic acids, which can play a role in prion disease pathogenesis. Finally, we comment on therapeutic strategies directed at the nonfunctional phase separation that could potentially tackle prion diseases or other protein misfolding disorders.

Molecular Basis of Small-Molecule Binding to α-Synuclein

Intrinsically disordered proteins (IDPs) are implicated in many human diseases. They have generally not been amenable to conventional structure-based drug design, however, because their intrinsic conformational variability has precluded an atomic-level understanding of their binding to small molecules. Here we present long-time-scale, atomic-level molecular dynamics (MD) simulations of monomeric α-synuclein (an IDP whose aggregation is associated with Parkinson’s disease) binding the small-molecule drug fasudil in which the observed protein–ligand interactions were found to be in good agreement with previously reported NMR chemical shift data. In our simulations, fasudil, when bound, favored certain charge–charge and π-stacking interactions near the C terminus of α-synuclein but tended not to form these interactions simultaneously, rather breaking one of these interactions and forming another nearby (a mechanism we term dynamic shuttling). Further simulations with small molecules chosen to modify these interactions yielded binding affinities and key structural features of binding consistent with subsequent NMR experiments, suggesting the potential for MD-based strategies to facilitate the rational design of small molecules that bind with disordered proteins.

Phase separation in plants: New insights into cellular compartmentalization

A fundamental challenge for cells is how to coordinate various biochemical reactions in space and time. To achieve spatiotemporal control, cells have developed organelles that are surrounded by lipid bilayer membranes. Further, membraneless compartmentalization, a process induced by dynamic physical association of biomolecules through phase transition offers another efficient mechanism for intracellular organization. While our understanding of phase separation was predominantly dependent on yeast and animal models, recent findings have provided compelling evidence for emerging roles of phase separation in plants. In this review, we first provide an overview of the current knowledge of phase separation, including its definition, biophysical principles, molecular features and regulatory mechanisms. Then we summarize plant-specific phase separation phenomena and describe their functions in plant biological processes in great detail. Moreover, we propose that phase separation is an evolutionarily conserved and efficient mechanism for cellular compartmentalization which allows for distinct metabolic processes and signaling pathways, and is especially beneficial for the sessile lifestyle of plants to quickly and efficiently respond to the changing environment.

Intrinsic disorder in protein kinase A anchoring proteins signaling complexes

Protein kinase A (PKA) is regulated by a diverse class of anchoring proteins known as AKAPs that target PKA to subsets of its activators and substrates. Recently, it was reported that PKA can remain bound to its regulatory subunit after activation in contrast to classical model of activation-by-dissociation. This implies that PKA remains bound to the AKAPs and its substrates, and thus suggest many phosphorylation reactions occur while PKA is physically connected to its substrate. Intra-complex reactions are sensitive to the architecture of the signaling complex, but generally concentration independent. We show that most AKAPs have long intrinsically disordered regions, and suggest that they represent an adaptation for intra-complex phosphorylation. Based on polymer models of the disordered proteins, we predict that the effective concentrations of tethered substrates range from the low millimolar range to tens of micromolar. Based on recent models for intra-complex enzyme reactions, we suggest that the structure of the AKAP signaling complex is likely to be source of allosteric regulation of PKA signaling.

The sequence-ensemble relationship in fuzzy protein complexes

Intrinsically disordered proteins (IDPs) interact with globular proteins through a variety of mechanisms, resulting in the structurally heterogeneous ensembles known as fuzzy complexes. While there exists a reasonable comprehension on how IDP sequence determines the unbound IDP ensemble, little is known about what shapes the structural characteristics of IDPs bound to their targets. Using a statistical thermodynamic model, we show that the target-bound ensembles are determined by a simple code that combines the IDP sequence and the distribution of IDP-target interaction hotspots. These two parameters define the conformational space of target-bound IDPs and rationalize the observed structural heterogeneity of fuzzy complexes. The presented model successfully reproduces the dynamical signatures of target-bound IDPs from the NMR relaxation experiments as well as the changes of interaction affinity and the IDP helicity induced by mutations. The model explains how the target-bound IDP ensemble adapts to mutations in order to achieve an optimal balance between conformational freedom and interaction energy. Taken together, the presented sequence-ensemble relationship of fuzzy complexes explains the different manifestations of IDP disorder in folding-upon-binding processes.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database

The Protein Ensemble Database (PED; https://proteinensemble.org/) is the major repository of conformational ensembles of intrinsically disordered proteins (IDPs). Conformational ensembles of IDPs are primarily provided by their authors or occasionally collected from literature, and are subsequently deposited in PED along with the corresponding structured, manually curated metadata. The modeling of conformational ensembles usually relies on experimental data from small-angle X-ray scattering (SAXS), fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics (MD) simulations, or a combination of these techniques. The growing number of scientific studies based on these data, along with the astounding and swift progress in the field of protein intrinsic disorder, has required a significant update and upgrade of PED, first published in 2014. To this end, the database was entirely renewed in 2020 and now has a dedicated team of biocurators providing manually curated descriptions of the methods and conditions applied to generate the conformational ensembles and for checking consistency of the data. Here, we present a detailed description on how to explore PED with its protein pages and experimental pages, and how to interpret entries of conformational ensembles. We describe how to efficiently search conformational ensembles deposited in PED by means of its web interface and API. We demonstrate how to make sense of the PED protein page and its associated experimental entry pages with reference to the yeast Sic1 use case. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Performing a search in PED Support Protocol 1: Programmatic access with the PED API Basic Protocol 2: Interpreting the protein page and the experimental entry page-the Sic1 use case Support Protocol 2: Downloading options Support Protocol 3: Understanding the validation report-the Sic1 use case Basic Protocol 3: Submitting new conformational ensembles to PED Basic Protocol 4: Providing feedback in PED.

Intrinsic disorder in integral membrane proteins

The well-defined roles and specific protein-protein interactions of many integral membrane proteins (IMPs), such as those functioning as receptors for extracellular matrix proteins and soluble growth factors, easily align with considering IMP structure as a classical "lock-and-key" concept. Nevertheless, continued advances in understanding protein conformation, such as those which established the widespread existence of intrinsically disordered proteins (IDPs) and especially intrinsically disordered regions (IDRs) in otherwise three-dimensionally organized proteins, call for ongoing reevaluation of transmembrane proteins. Here, we present basic traits of IDPs and IDRs, and, for some select single-span IMPs, consider the potential functional advantages intrinsic disorder might provide and the possible conformational impact of disease-associated mutations. For transmembrane proteins in general, we highlight several investigational approaches, such as biophysical and computational methods, stressing the importance of integrating them to produce a more-complete mechanistic model of disorder-containing IMPs. These procedures, when synergized with in-cell assessments, will likely be key in translating in silico and in vitro results to improved understanding of IMP conformational flexibility in normal cell physiology as well as disease, and will help to extend their potential as therapeutic targets.

A combined evolutionary and structural approach to disclose the primary structural determinants essential for proneurotrophins biological functions

The neurotrophins, i.e., Nerve Growth Factor (NGF), Brain-Derived Neurotrophic Factor (BDNF), Neurotrophin 3 (NT3) and Neurotrophin 4 (NT4), are known to play a range of crucial functions in the developing and adult peripheral and central nervous systems. Initially synthesized as precursors, i.e., proneurotrophins (proNTs), that are cleaved to release C-terminal mature forms, they act through two types of receptors, the specific Trk receptors (Tropomyosin-related kinases) and the pan-neurotrophin receptor p75NTR, to initiate survival and differentiative responses. Recently, all the proNTs but proNT4 have been demonstrated to be not just inactive precursors, but signaling ligands that mediate opposing actions in fundamental aspects of the nervous system with respect to the mature counterparts through dual-receptor complexes formation with a member of the VPS10 family and p75NTR. Despite the functional relevance, the molecular determinants underpinning the interactions between the pro-domains and their receptors are still elusive probably due to their intrinsically disordered nature. Here we present an evolutionary approach coupled to an experimental study aiming to uncover the structural and dynamical basis of the biological function displayed by proNGF, proBDNF and proNT3 but missing in proNT4. A bioinformatic analysis allowed to elucidate the functional adaptability of the proNTs family in vertebrates, identifying conserved key structural features. The combined biochemical and SAXS experiments shed lights on the structure and dynamic behavior of the human proNTs in solution, giving insights on the evolutionary conserved structural motifs, essential for the multifaceted roles of proNTs in physiological as well as in pathological contexts.

Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation

Many proteins have been shown to function via liquid-liquid phase separation. Computational modeling could offer much needed structural details of protein condensates and reveal the set of molecular interactions that dictate their stability. However, the presence of both ordered and disordered domains in these proteins places a high demand on the model accuracy. Here, we present an algorithm to derive a coarse-grained force field, MOFF, which can model both ordered and disordered proteins with consistent accuracy. It combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to ensure that MOFF recreates experimental radii of gyration while predicting the folded structures for globular proteins with lower energy. The theta temperature determined from MOFF separates ordered and disordered proteins at 300 K and exhibits a strikingly linear relationship with amino acid sequence composition. We further applied MOFF to study the phase behavior of HP1, an essential protein for post-translational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologues despite their high sequence similarity. We carried out large-scale simulations with hundreds of proteins to determine the critical temperature of phase separation and uncover multivalent interactions that stabilize higher-order assemblies. In all, our work makes significant methodological strides to connect theories of ordered and disordered proteins and provides a powerful tool for studying liquid-liquid phase separation with near-atomistic details.

Flexibility of Oxidized and Reduced States of the Chloroplast Regulatory Protein CP12 in Isolation and in Cell Extracts

In the chloroplast, Calvin–Benson–Bassham enzymes are active in the reducing environment created in the light by electrons from the photosystems. In the dark, these enzymes are inhibited, mainly caused by oxidation of key regulatory cysteine residues. CP12 is a small protein that plays a role in this regulation with four cysteine residues that undergo a redox transition. Using amide-proton exchange with solvent, measured by nuclear magnetic resonance (NMR) and mass-spectrometry, we confirmed that reduced CP12 is intrinsically disordered. Using real-time NMR, we showed that the oxidation of the two disulfide bridges is simultaneous. In oxidized CP12, the C₂₃–C₃₁ pair is in a region that undergoes a conformational exchange in the NMR-intermediate timescale. The C₆₆–C₇₅ pair is in the C-terminus that folds into a stable helical turn. We confirmed that these structural states exist in a physiologically relevant environment: a cell extract from Chlamydomonas reinhardtii. Consistent with these structural equilibria, the reduction is slower for the C₆₆–C₇₅ pair than for the C₂₃–C₃₁ pair. The redox mid-potentials for the two cysteine pairs differ and are similar to those found for glyceraldehyde 3-phosphate dehydrogenase and phosphoribulokinase, consistent with the regulatory role of CP12.

Structural Proteomics Methods to Interrogate the Conformations and Dynamics of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) and regions of intrinsic disorder (IDRs) are abundant in proteomes and are essential for many biological processes. Thus, they are often implicated in disease mechanisms, including neurodegeneration and cancer. The flexible nature of IDPs and IDRs provides many advantages, including (but not limited to) overcoming steric restrictions in binding, facilitating posttranslational modifications, and achieving high binding specificity with low affinity. IDPs adopt a heterogeneous structural ensemble, in contrast to typical folded proteins, making it challenging to interrogate their structure using conventional tools. Structural mass spectrometry (MS) methods are playing an increasingly important role in characterizing the structure and function of IDPs and IDRs, enabled by advances in the design of instrumentation and the development of new workflows, including in native MS, ion mobility MS, top-down MS, hydrogen-deuterium exchange MS, crosslinking MS, and covalent labeling. Here, we describe the advantages of these methods that make them ideal to study IDPs and highlight recent applications where these tools have underpinned new insights into IDP structure and function that would be difficult to elucidate using other methods.

Computational Screening of Phase-separating Proteins

Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies.

Aberrant phase separation and cancer

Eukaryotic cells are intracellularly divided into numerous compartments or organelles, which coordinate specific molecules and biological reactions. Membrane-bound organelles are physically separated by lipid bilayers from the surrounding environment. Biomolecular condensates, also referred to membraneless organelles, are micron-scale cellular compartments that lack membranous enclosures but function to concentrate proteins and RNA molecules, and these are involved in diverse processes. Liquid-liquid phase separation (LLPS) driven by multivalent weak macromolecular interactions is a critical principle for the formation of biomolecular condensates, and a multitude of combinations among multivalent interactions may drive liquid-liquid phase transition (LLPT). Dysregulation of LLPS and LLPT leads to aberrant condensate and amyloid formation, which causes many human diseases, including neurodegeneration and cancer. Here, we describe recent findings regarding abnormal forms of biomolecular condensates and aggregation via aberrant LLPS and LLPT of cancer-related proteins in cancer development driven by mutation and fusion of genes. Moreover, we discuss the regulatory mechanisms by which aberrant LLPS and LLPT occur in cancer and the drug candidates targeting these mechanisms. Further understanding of the molecular events regulating how biomolecular condensates and aggregation form in cancer tissue is critical for the development of therapeutic strategies against tumorigenesis.

Novel NMR Assignment Strategy Reveals Structural Heterogeneity in Solution of the nsP3 HVD Domain of Venezuelan Equine Encephalitis Virus

In recent years, intrinsically disordered proteins (IDPs) and disordered domains have attracted great attention. Many of them contain linear motifs that mediate interactions with other factors during formation of multicomponent protein complexes. NMR spectrometry is a valuable tool for characterizing this type of interactions on both amino acid (aa) and atomic levels. Alphaviruses encode a nonstructural protein nsP3, which drives viral replication complex assembly. nsP3 proteins contain over 200-aa-long hypervariable domains (HVDs), which exhibits no homology between different alphavirus species, are predicted to be intrinsically disordered and appear to be critical for alphavirus adaptation to different cells. Previously, we have shown that nsP3 HVD of chikungunya virus (CHIKV) is completely disordered with low tendency to form secondary structures in free form. In this new study, we used novel NMR approaches to assign the spectra for the nsP3 HVD of Venezuelan equine encephalitis virus (VEEV). The HVDs of CHIKV and VEEV have no homology but are both involved in replication complex assembly and function. We have found that VEEV nsP3 HVD is also mostly disordered but contains a short stable α-helix in its C-terminal fragment, which mediates interaction with the members of cellular Fragile X syndrome protein family. Our NMR data also suggest that VEEV HVD has several regions with tendency to form secondary structures.

C-Terminal Tail Polyglycylation and Polyglutamylation Alter Microtubule Mechanical Properties

Microtubules are biopolymers that perform diverse cellular functions. Microtubule behavior regulation occurs in part through post-translational modification of both the α- and β-subunits of tubulin. One class of modifications is the heterogeneous addition of glycine and/or glutamate residues to the disordered C-terminal tails (CTTs) of tubulin. Because of their prevalence in stable, high-stress cellular structures such as cilia, we sought to determine if these modifications alter microtubules' intrinsic stiffness. Here, we describe the purification and characterization of differentially modified pools of tubulin from Tetrahymena thermophila. We found that post-translational modifications do affect microtubule stiffness but do not affect the number of protofilaments incorporated into microtubules. We measured the spin dynamics of nuclei in the CTT backbone by NMR spectroscopy to explore the mechanism of this change. Our results show that the α-tubulin CTT does not protrude out from the microtubule surface, as is commonly depicted in models, but instead interacts with the dimer's surface. This suggests that the interactions of the α-tubulin CTT with the tubulin body contributes to the stiffness of the assembled microtubule, thus providing insight into the mechanism by which polyglycylation and polyglutamylation can alter microtubule mechanical properties.

The molecular structure of long non-coding RNAs: emerging patterns and functional implications

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

Information theoretic measures for quantifying sequence-ensemble relationships of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.

Impact of the Hereditary P301L Mutation on the Correlated Conformational Dynamics of Human Tau Protein Revealed by the Paramagnetic Relaxation Enhancement NMR Experiments

Tau forms intracellular insoluble aggregates as a neuropathological hallmark of Alzheimer’s disease. Tau is largely unstructured, which complicates the characterization of the tau aggregation process. Recent studies have demonstrated that tau samples two distinct conformational ensembles, each of which contains the soluble and aggregation-prone states of tau. A shift to populate the aggregation-prone ensemble may promote tau fibrillization. However, the mechanism of this ensemble transition remains elusive. In this study, we explored the conformational dynamics of a tau fragment by using paramagnetic relaxation enhancement (PRE) and interference (PRI) NMR experiments. The PRE correlation map showed that tau is composed of segments consisting of residues in correlated motions. Intriguingly, residues forming the β-structures in the heparin-induced tau filament coincide with residues in these segments, suggesting that each segment behaves as a structural unit in fibrillization. PRI data demonstrated that the P301L mutation exclusively alters the transiently formed tau structures by changing the short- and long-range correlated motions among residues. The transient conformations of P301L tau expose the amyloid motif PHF6 to promote tau self-aggregation. We propose the correlated motions among residues within tau determine the population sizes of the conformational ensembles, and perturbing the correlated motions populates the aggregation-prone form.

The (un)structural biology of biomolecular liquid-liquid phase separation using NMR spectroscopy

Liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a phenomenon that underlies membraneless compartmentalization of the cell. The underlying molecular interactions that underpin biomolecular LLPS have been of increased interest due to the importance of membraneless organelles in facilitating various biological processes and the disease association of several of the proteins that mediate LLPS. Proteins that are able to undergo LLPS often contain intrinsically disordered regions and remain dynamic in solution. Solution-state NMR spectroscopy has emerged as a leading structural technique to characterize protein LLPS due to the variety and specificity of information that can be obtained about intrinsically disordered sequences. This review discusses practical aspects of studying LLPS by NMR, summarizes recent work on the molecular aspects of LLPS of various protein systems, and discusses future opportunities for characterizing the molecular details of LLPS to modulate phase separation.

New technologies to analyse protein function: an intrinsic disorder perspective

Functions of intrinsically disordered proteins do not require structure. Such structure-independent functionality has melted away the classic rigid "lock and key" representation of structure-function relationships in proteins, opening a new page in protein science, where molten keys operate on melted locks and where conformational flexibility and intrinsic disorder, structural plasticity and extreme malleability, multifunctionality and binding promiscuity represent a new-fangled reality. Analysis and understanding of this new reality require novel tools, and some of the techniques elaborated for the examination of intrinsically disordered protein functions are outlined in this review.

Quantitative Protein Disorder Assessment Using NMR Chemical Shifts

Disorder is vital for the biological function of many proteins. The huge diversity found in disorder composition and amplitude reflects the complexity and pluripotency of intrinsically disordered proteins (IDPs). The first step toward a better understanding of IDPs is a quantitative and position-specific experimental characterization, and nuclear magnetic resonance (NMR) spectroscopy has emerged as the method of first choice. Here, we describe how to quantitatively assess the local balance between order and disorder in proteins by utilizing the Chemical shift Z-score for assessing Order/Disorder (CheZOD Z-score). This order/disorder metric is computed from the difference between experimentally determined NMR chemical shifts and computed random coil reference values. We explain in detail how CheZOD Z-scores are calculated fast and easily, either by using a python executable or by data submission to a server.

Conformational tuning of a DNA-bound transcription factor

Transcription factors are involved in many cellular processes that take place remote from their cognate DNA sequences. The efficiencies of these activities are thus in principle counteracted by high binding affinities of the factors to their cognate DNAs. Models such as facilitated diffusion or dissociation address this apparent contradiction. We show that the MYC associated transcription factor X (MAX) undergoes nanoscale conformational fluctuations in the DNA-bound state, which is consistent with facilitated dissociation from or diffusion along DNA strands by transiently reducing binding energies. An integrative approach involving EPR, NMR, crystallographic and molecular dynamics analyses demonstrates that the N-terminal domain of MAX constantly opens and closes around a bound DNA ligand thereby dynamically tuning the binding epitope and the mode of interaction.

Considerations and Challenges in Studying Liquid-Liquid Phase Separation and Biomolecular Condensates

Evidence is now mounting that liquid-liquid phase separation (LLPS) underlies the formation of membraneless compartments in cells. This realization has motivated major efforts to delineate the function of such biomolecular condensates in normal cells and their roles in contexts ranging from development to age-related disease. There is great interest in understanding the underlying biophysical principles and the specific properties of biological condensates with the goal of bringing insights into a wide range of biological processes and systems. The explosion of physiological and pathological contexts involving LLPS requires clear standards for their study. Here, we propose guidelines for rigorous experimental characterization of LLPS processes in vitro and in cells, discuss the caveats of common experimental approaches, and point out experimental and theoretical gaps in the field.

Characterizing Post-Translational Modifications and Their Effects on Protein Conformation Using NMR Spectroscopy

The diversity of the cellular proteome substantially exceeds the number of genes coded by the DNA of an organism because one or more residues in a majority of eukaryotic proteins are post-translationally modified (PTM) by the covalent conjugation of specific chemical groups. We now know that PTMs alter protein conformation and function in ways that are not entirely understood at the molecular level. NMR spectroscopy has been particularly successful as an analytical tool in elucidating the themes underlying the structural role of PTMs. In this Perspective, we focus on the NMR-based characterization of three abundant PTMs: phosphorylation, acetylation, and glycosylation. We detail NMR methods that have found success in detecting these modifications at a site-specific level. We also highlight NMR studies that have mapped the conformational changes ensuing from these PTMs as well as evaluated their relation to function. The NMR toolbox is expanding rapidly with experiments available to probe not only the average structure of biomolecules but also how this structure changes with time on time scales ranging from picoseconds to seconds. The atomic resolution insights into the biomolecular structure, dynamics, and mechanism accessible from NMR spectroscopy ensure that NMR will continue to be at the forefront of research in the structural biology of PTMs.

Involvement of Local, Rapid Conformational Dynamics in Binding of Flexible Recognition Motifs

Flexible protein sequences populate ensembles of rapidly interconverting states differentiated by small-scale fluctuations; however, elucidating whether and how the ensembles determine function experimentally is challenged by the combined high spatial and temporal resolution needed to capture the states. We used carbon-deuterium (C-D) bond vibrations incorporated as infrared probes to characterize with residue-specific detail the heterogeneity of states adopted by proline-rich (PR) sequences and assess their involvement in recognition of Src homology 3 domains. The C-D absorption envelopes provided evidence for two or three sub-populations at all proline residues. The changes in the subpopulations induced by binding generally reflected recognition by conformational selection but depended on the residue and the state of the ligand to illuminate distinct mechanisms among the PR ligands. Notably, the spectral data indicate that greater adaptability among the states is associated with reduced recognition specificity and that perturbation to the ensemble populations contributes to differences in binding entropy. Broadly, the study quantifies rapidly interconverting ensembles with residue-specific detail and implicates them in function.