Improved Structural Characterizations of the drkN SH3 Domain Unfolded State Suggest a Compact Ensemble with Native-like and Non-native Structure

Structural basis of nucleic acid recognition by the N-terminal cold shock domain of the plant glycine-rich protein AtGRP2

AtGRP2 is a glycine-rich, RNA-binding protein that plays pivotal roles in abiotic stress response and flowering time regulation in Arabidopsis thaliana. AtGRP2 consists of an N-terminal cold shock domain (CSD) and two C-terminal CCHC-type zinc knuckles interspersed with glycine-rich regions. Here, we investigated the structure, dynamics, and nucleic acid binding properties of AtGRP2-CSD. The 2D [1H,15N] HSQC spectrum of AtGRP2-CSD1-79 revealed the presence of a partially folded intermediate in equilibrium with the folded state. The addition of eleven residues at the C-terminus stabilized the folded conformation. The three-dimensional structure of AtGRP2-CSD1-90 unveiled a β-barrel composed of five antiparallel β-strands and a 310 helical turn, along with an ordered C-terminal extension, a conserved feature in eukaryotic CSDs. Direct contacts between the C-terminal extension and the β3-β4 loop further stabilized the CSD fold. AtGRP2-CSD1-90 exhibited nucleic acid binding via solvent-exposed residues on strands β2 and β3, as well as the β3-β4 loop, with higher affinity for DNA over RNA, particularly favoring pyrimidine-rich sequences. Furthermore, DNA binding induced rigidity in the β3-β4 loop, evidenced by 15N-{1H} NOE values. Mutation of residues W17, F26, and F37, in the central β-sheet, completely abolished DNA binding, highlighting the significance of π-stacking interactions in the binding mechanism. These results shed light on the mechanism of nucleic acid recognition employed by AtGRP2, creating a framework for the development of biotechnological strategies aimed at enhancing plant resistance to abiotic stresses.

Conformational and Interaction Landscape of Histone H4 Tails in Nucleosomes Probed by Paramagnetic NMR Spectroscopy

The fundamental repeat unit of chromatin, the nucleosome, consists of approximately 147 base pairs of double-stranded DNA and a histone protein octamer containing two copies each of histones H2A, H2B, H3, and H4. Each histone possesses a dynamically disordered N-terminal tail domain, and it is well-established that the tails of histones H3 and H4 play key roles in chromatin compaction and regulation. Here we investigate the conformational ensemble and interactions of the H4 tail in nucleosomes by means of solution NMR measurements of paramagnetic relaxation enhancements (PREs) in recombinant samples reconstituted with 15N-enriched H4 and nitroxide spin-label tagged H3. The experimental PREs, which report on the proximities of individual H4 tail residues to the different H3 spin-label sites, are interpreted by using microsecond time-scale molecular dynamics simulations of the nucleosome core particle. Collectively, these data enable improved localization of histone H4 tails in nucleosomes and support the notion that H4 tails engage in a fuzzy complex interaction with nucleosomal DNA.

The Analytical Flory Random Coil Is a Simple-to-Use Reference Model for Unfolded and Disordered Proteins

Denatured, unfolded, and intrinsically disordered proteins (collectively referred to here as unfolded proteins) can be described using analytical polymer models. These models capture various polymeric properties and can be fit to simulation results or experimental data. However, the model parameters commonly require users' decisions, making them useful for data interpretation but less clearly applicable as stand-alone reference models. Here we use all-atom simulations of polypeptides in conjunction with polymer scaling theory to parameterize an analytical model of unfolded polypeptides that behave as ideal chains (ν = 0.50). The model, which we call the analytical Flory random coil (AFRC), requires only the amino acid sequence as input and provides direct access to probability distributions of global and local conformational order parameters. The model defines a specific reference state to which experimental and computational results can be compared and normalized. As a proof-of-concept, we use the AFRC to identify sequence-specific intramolecular interactions in simulations of disordered proteins. We also use the AFRC to contextualize a curated set of 145 different radii of gyration obtained from previously published small-angle X-ray scattering experiments of disordered proteins. The AFRC is implemented as a stand-alone software package and is also available via a Google Colab notebook. In summary, the AFRC provides a simple-to-use reference polymer model that can guide intuition and aid in interpreting experimental or simulation results.

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.

The analytical Flory random coil is a simple-to-use reference model for unfolded and disordered proteins

Denatured, unfolded, and intrinsically disordered proteins (collectively referred to here as unfolded proteins) can be described using analytical polymer models. These models capture various polymeric properties and can be fit to simulation results or experimental data. However, the model parameters commonly require users' decisions, making them useful for data interpretation but less clearly applicable as stand-alone reference models. Here we use all-atom simulations of polypeptides in conjunction with polymer scaling theory to parameterize an analytical model of unfolded polypeptides that behave as ideal chains (ν = 0.50). The model, which we call the analytical Flory Random Coil (AFRC), requires only the amino acid sequence as input and provides direct access to probability distributions of global and local conformational order parameters. The model defines a specific reference state to which experimental and computational results can be compared and normalized. As a proof-of-concept, we use the AFRC to identify sequence-specific intramolecular interactions in simulations of disordered proteins. We also use the AFRC to contextualize a curated set of 145 different radii of gyration obtained from previously published small-angle X-ray scattering experiments of disordered proteins. The AFRC is implemented as a stand-alone software package and is also available via a Google colab notebook. In summary, the AFRC provides a simple-to-use reference polymer model that can guide intuition and aid in interpreting experimental or simulation results.

Protein Dynamics to Define and Refine Disordered Protein Ensembles

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R₂ relaxation rates and Lipari-Szabo order parameters (S² values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

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

Denatured State Conformational Biases in Three-Helix Bundles Containing Divergent Sequences Localize near Turns and Helix Capping Residues

Rhodopseudomonas palustris cytochrome c', a four-helix bundle, and the second ubiquitin-associated domain, UBA(2), a three-helix bundle from the human homologue of yeast Rad23, HHR23A, deviate from random coil behavior under denaturing conditions in a fold-specific manner. The random coil deviations in each of these folds occur near interhelical turns and loops in their tertiary structures. Here, we examine an additional three-helix bundle with an identical fold to UBA(2), but a highly divergent sequence, the first ubiquitin-associated domain, UBA(1), of HHR23A. We use histidine-heme loop formation methods, employing eight single histidine variants, to probe for denatured state conformational bias of a UBA(1) domain fused to the N-terminus of iso-1-cytochrome c (iso-1-Cytc). Guanidine hydrochloride (GuHCl) denaturation shows that the iso-1-Cytc domain unfolds first, followed by the UBA(1) domain. Denatured state (4 and 6 M GuHCl) histidine-heme loop formation studies show that as the size of the histidine-heme loop increases, loop stability decreases, as expected for the Jacobson-Stockmayer relationship. However, loops formed with His35, His31, and His15, of UBA(1), are 0.6-1.1 kcal/mol more stable than expected from the Jacobson-Stockmayer relationship, confirming the importance of deviations of the denatured state from random coil behavior near interhelical turns of helical domains for facilitating folding to the correct topology. For UBA(1) and UBA(2), hydrophobic clusters on either side of the turns partially explain deviations from random coil behavior; however, helix capping also appears to be important.

Membrane-bound KRAS approximates an entropic ensemble of configurations

KRAS4B is a membrane-anchored signaling protein and a primary target in cancer research. Predictions from molecular dynamics simulations that have previously shaped our mechanistic understanding of KRAS signaling disagree with recent experimental results from neutron reflectometry, NMR, and thermodynamic binding studies. To gain insight into these discrepancies, we compare this body of biophysical data to back-calculated experimental results from a series of molecular simulations that implement different subsets of molecular interactions. Our results show that KRAS4B approximates an entropic ensemble of configurations at model membranes containing 30% phosphatidylserine lipids, which is not significantly shaped by interactions between the globular G-domain of KRAS4B and the lipid membrane. These findings revise our understanding of KRAS signaling and promote a model in which the protein samples the accessible conformational space in a near-uniform manner while being available to bind to effector proteins.

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.

Phosphorylation orchestrates the structural ensemble of the intrinsically disordered protein HMGA1a and modulates its DNA binding to the NFκB promoter

High Mobility Group Protein A1a (HMGA1a) is a highly abundant nuclear protein, which plays a crucial role during embryogenesis, cell differentiation, and neoplasia. Here, we present the first ever NMR-based structural ensemble of full length HMGA1a. Our results show that the protein is not completely random coil but adopts a compact structure consisting of transient long-range contacts, which is regulated by post-translational phosphorylation. The CK2-, cdc2- and cdc2/CK2-phosphorylated forms of HMGA1a each exhibit a different binding affinity towards the PRD2 element of the NFκB promoter. Our study identifies connected regions between phosphorylation sites in the wildtype ensemble that change considerably upon phosphorylation, indicating that these posttranslational modifications sites are part of an electrostatic contact network that alters the structural ensemble by shifting the conformational equilibrium. Moreover, ITC data reveal that the CK2-phosphorylated HMGA1a exhibits a different DNA promoter binding affinity for the PRD2 element. Furthermore, we present the first structural model for AT-hook 1 of HMGA1a that can adopt a transient α-helical structure, which might serve as an additional regulatory mechanism in HMAG1a. Our findings will help to develop new therapeutic strategies against HMGA1a-associated cancers by taking posttranslational modifications into consideration.

Assessing Site-Specific Enhancements Imparted by Hyperpolarized Water in Folded and Unfolded Proteins by 2D HMQC NMR

Hyperpolarized water can be a valuable aid in protein NMR, leading to amide group ¹H polarizations that are orders of magnitude larger than their thermal counterparts. Suitable procedures can exploit this to deliver 2D ¹H–¹⁵N correlations with good resolution and enhanced sensitivity. These enhancements depend on the exchange rates between the amides and the water, thereby yielding diagnostic information about solvent accessibility. This study applied this “HyperW” method to four proteins exhibiting a gamut of exchange behaviors: PhoA^(350–471), an unfolded 122-residue fragment; barstar, a fully folded ribonuclease inhibitor; R17, a 13.3 kDa system possessing folded and unfolded forms under slow interconversion; and drkN SH3, a protein domain whose folded and unfolded forms interchange rapidly and with temperature-dependent population ratios. For PhoA4^(350–471) HyperW sensitivity enhancements were ≥300×, as expected for an unfolded protein sequence. Though fully folded, barstar also exhibited substantial enhancements; these, however, were not uniform and, according to CLEANEX experiments, reflected the solvent-exposed residues. R17 showed the expected superposition of ≥100-fold enhancements for its unfolded form, coexisting with more modest enhancements for their folded counterparts. Unexpected, however, was the behavior of drkN SH3, for which HyperW enhanced the unfolded but, surprisingly, enhanced even more certain folded protein sites. These preferential enhancements were repeatedly and reproducibly observed. A number of explanations—including three-site exchange magnetization transfers between water and the unfolded and folded states; cross-correlated relaxation processes from hyperpolarized “structural” waters and labile side-chain protons; and the possibility that faster solvent exchange rates characterize certain folded sites over their unfolded counterparts—are considered to account for them.

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule-IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions.

Multi-state recognition pathway of the intrinsically disordered protein kinase inhibitor by protein kinase A

In the nucleus, the spatiotemporal regulation of the catalytic subunit of cAMP-dependent protein kinase A (PKA-C) is orchestrated by an intrinsically disordered protein kinase inhibitor, PKI, which recruits the CRM1/RanGTP nuclear exporting complex. How the PKA-C/PKI complex assembles and recognizes CRM1/RanGTP is not well understood. Using NMR, SAXS, fluorescence, metadynamics, and Markov model analysis, we determined the multi-state recognition pathway for PKI. After a fast binding step in which PKA-C selects PKI's most competent conformations, PKI folds upon binding through a slow conformational rearrangement within the enzyme's binding pocket. The high-affinity and pseudo-substrate regions of PKI become more structured and the transient interactions with the kinase augment the helical content of the nuclear export sequence, which is then poised to recruit the CRM1/RanGTP complex for nuclear translocation. The multistate binding mechanism featured by PKA-C/PKI complex represents a paradigm on how disordered, ancillary proteins (or protein domains) are able to operate multiple functions such as inhibiting the kinase while recruiting other regulatory proteins for nuclear export.

The effect of an engineered ATCUN motif on the structure and biophysical properties of the SH3 domain of c-Src tyrosine kinase

Metal binding to sites engineered in proteins can provide an increase in their stability and facilitate new functions. Besides the sites introduced in purpose, sometimes they are present accidentally as a consequence of the expression system used to produce the protein. This happens with the copper- and nickel-binding (ATCUN) motif generated by the amino-terminal residues Gly-Ser-His. This ATCUN motif is fortuitously present in many proteins, but how it affects the structural and biophysical characterization of the proteins has not been studied. In this work, we have compared the structure and biophysical properties of a small modular domain, the SH3 domain of the c-Src tyrosine kinase, cloned with and without an ATCUN motif at the N terminus. At pH 7.0, the SH3 domain with the ATCUN motif binds nickel with a binding constant K_a = 28.0 ± 3.0 mM^-1. The formation of the nickel complex increases the thermal and chemical stability of the SH3 domain. A comparison of the crystal structures of the SH3 domain with and without the ATCUN motif shows that the binding of nickel does not affect the overall structure of the SH3 domain. In all crystal structures analyzed, residues Gly-Ser-His in complex with Ni²⁺ show a square planar geometry. The CD visible spectrum of the nickel complex shows that this geometry is also present in the solution. Therefore, our results not only show that the ATCUN motif might influence the biophysical properties of the protein, but also points to an advantageous stabilization of the protein with potential biotechnological applications.

Using Small-Angle Scattering Data and Parametric Machine Learning to Optimize Force Field Parameters for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10–15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.

NMR characterization of solvent accessibility and transient structure in intrinsically disordered proteins

In order to understand the conformational behavior of intrinsically disordered proteins (IDPs) and their biological interaction networks, the detection of residual structure and long-range interactions is required. However, the large number of degrees of conformational freedom of disordered proteins require the integration of extensive sets of experimental data, which are difficult to obtain. Here, we provide a straightforward approach for the detection of residual structure and long-range interactions in IDPs under near-native conditions using solvent paramagnetic relaxation enhancement (sPRE). Our data indicate that for the general case of an unfolded chain, with a local flexibility described by the overwhelming majority of available combinations, sPREs of non-exchangeable protons can be accurately predicted through an ensemble-based fragment approach. We show for the disordered protein α-synuclein and disordered regions of the proteins FOXO4 and p53 that deviation from random coil behavior can be interpreted in terms of intrinsic propensity to populate local structure in interaction sites of these proteins and to adopt transient long-range structure. The presented modification-free approach promises to be applicable to study conformational dynamics of IDPs and other dynamic biomolecules in an integrative approach.

Collapse Transitions of Proteins and the Interplay Among Backbone, Sidechain, and Solvent Interactions

Proteins can collapse into compact globules or form expanded, solvent-accessible, coil-like conformations. Additionally, they can fold into well-defined three-dimensional structures or remain partially or entirely disordered. Recent discoveries have shown that the tendency for proteins to collapse or remain expanded is not intrinsically coupled to their ability to fold. These observations suggest that proteins do not have to form compact globules in aqueous solutions. They can be intrinsically disordered, collapsed, or expanded, and even form well-folded, elongated structures. This ability to decouple collapse from folding is determined by the sequence details of proteins. In this review, we highlight insights gleaned from studies over the past decade. Using a polymer physics framework, we explain how the interplay among sidechains, backbone units, and solvent determines the driving forces for collapsed versus expanded states in aqueous solvents.

Effect of an Imposed Contact on Secondary Structure in the Denatured State of Yeast Iso-1-cytochrome c

There is considerable evidence that long-range interactions stabilize residual protein structure under denaturing conditions. However, evaluation of the effect of a specific contact on structure in the denatured state has been difficult. Iso-1-cytochrome c variants with a Lys54 → His mutation form a particularly stable His-heme loop in the denatured state, suggestive of loop-induced residual structure. We have used multidimensional nuclear magnetic resonance methods to assign ¹H and ¹⁵N backbone amide and ¹³C backbone and side chain chemical shifts in the denatured state of iso-1-cytochrome c carrying the Lys54 → His mutation in 3 and 6 M guanidine hydrochloride and at both pH 6.4, where the His54-heme loop is formed, and pH 3.6, where the His54-heme loop is broken. Using the secondary structure propensity score, with the 6 M guanidine hydrochloride chemical shift data as a random coil reference state for data collected in 3 M guanidine hydrochloride, we found residual helical structure in the denatured state for the 60s helix and the C-terminal helix, but not in the N-terminal helix in the presence or absence of the His54-heme loop. Non-native helical structure is observed in two regions that form Ω-loops in the native state. There is more residual helical structure in the C-terminal helix at pH 6.4 when the loop is formed. Loop formation also appears to stabilize helical structure near His54, consistent with induction of helical structure observed when His-heme bonds form in heme-peptide model systems. The results are discussed in the context of the folding mechanism of cytochrome c.

Unraveling the meaning of chemical shifts in protein NMR

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Cosolutes, Crowding, and Protein Folding Kinetics

Long accepted as the most important interaction, recent work shows that steric repulsions alone cannot explain the effects of macromolecular cosolutes on the equilibrium thermodynamics of protein stability. Instead, chemical interactions have been shown to modulate, and even dominate, crowding-induced steric repulsions. Here, we use 19F NMR to examine the effects of small and large cosolutes on the kinetics of protein folding and unfolding using the metastable 7 kDa N-terminal SH3 domain of the Drosophila signaling protein drk (SH3), which folds by a two-state mechanism. The small cosolutes consist of trimethylamine N-oxide and sucrose, which increase equilibrium protein stability, and urea, which destabilizes proteins. The macromolecules comprise the stabilizing sucrose polymer, Ficoll, and the destabilizing globular protein, lysozyme. We assessed the effects of these cosolutes on the differences in free energy between the folded state and the transition state and between the unfolded ensemble and the transition state. We then examined the temperature dependence to assess changes in activation enthalpy and entropy. The enthalpically mediated effects are more complicated than suggested by equilibrium measurements. We also observed enthalpic effects with the supposedly inert sucrose polymer, Ficoll, that arise from its macromolecular nature. Assessment of activation entropies shows important contributions from solvent and cosolute, in addition to the configurational entropy of the protein that, again, cannot be gleaned from equilibrium data. Comparing the effects of Ficoll to those of the more physiologically relevant cosolute lysozyme reveals that synthetic polymers are not appropriate models for understanding the kinetics of protein folding in cells.

Protein folding by NMR

Protein folding is a highly complex process proceeding through a number of disordered and partially folded nonnative states with various degrees of structural organization. These transiently and sparsely populated species on the protein folding energy landscape play crucial roles in driving folding toward the native conformation, yet some of these nonnative states may also serve as precursors for protein misfolding and aggregation associated with a range of devastating diseases, including neuro-degeneration, diabetes and cancer. Therefore, in vivo protein folding is often reshaped co- and post-translationally through interactions with the ribosome, molecular chaperones and/or other cellular components. Owing to developments in instrumentation and methodology, solution NMR spectroscopy has emerged as the central experimental approach for the detailed characterization of the complex protein folding processes in vitro and in vivo. NMR relaxation dispersion and saturation transfer methods provide the means for a detailed characterization of protein folding kinetics and thermodynamics under native-like conditions, as well as modeling high-resolution structures of weakly populated short-lived conformational states on the protein folding energy landscape. Continuing development of isotope labeling strategies and NMR methods to probe high molecular weight protein assemblies, along with advances of in-cell NMR, have recently allowed protein folding to be studied in the context of ribosome-nascent chain complexes and molecular chaperones, and even inside living cells. Here we review solution NMR approaches to investigate the protein folding energy landscape, and discuss selected applications of NMR methodology to studying protein folding in vitro and in vivo. Together, these examples highlight a vast potential of solution NMR in providing atomistic insights into molecular mechanisms of protein folding and homeostasis in health and disease.

Bayesian inference of protein ensembles from SAXS data

The inherent flexibility of intrinsically disordered proteins (IDPs) and multi-domain proteins with intrinsically disordered regions (IDRs) presents challenges to structural analysis. These macromolecules need to be represented by an ensemble of conformations, rather than a single structure. Small-angle X-ray scattering (SAXS) experiments capture ensemble-averaged data for the set of conformations. We present a Bayesian approach to ensemble inference from SAXS data, called Bayesian ensemble SAXS (BE-SAXS). We address two issues with existing methods: the use of a finite ensemble of structures to represent the underlying distribution, and the selection of that ensemble as a subset of an initial pool of structures. This is achieved through the formulation of a Bayesian posterior of the conformational space. BE-SAXS modifies a structural prior distribution in accordance with the experimental data. It uses multi-step expectation maximization, with alternating rounds of Markov-chain Monte Carlo simulation and empirical Bayes optimization. We demonstrate the method by employing it to obtain a conformational ensemble of the antitoxin PaaA2 and comparing the results to a published ensemble.

Small-angle scattering studies of intrinsically disordered proteins and their complexes

Intrinsically Disordered Proteins (IDPs) perform a broad range of biological functions. Their relevance has motivated intense research activity seeking to characterize their sequence/structure/function relationships. However, the conformational plasticity of these molecules hampers the application of traditional structural approaches, and new tools and concepts are being developed to address the challenges they pose. Small-Angle Scattering (SAS) is a structural biology technique that probes the size and shape of disordered proteins and their complexes with other biomolecules. The low-resolution nature of SAS can be compensated with specially designed computational tools and its combined interpretation with complementary structural information. In this review, we describe recent advances in the application of SAS to disordered proteins and highly flexible complexes and discuss current challenges.

Finding Our Way in the Dark Proteome

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However, approximately one-third of the human proteome is comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain "unseen" by traditional structural biology methods. This Perspective considers the challenges raised by the "Dark Proteome", in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a "computational beamline" that will allow key functional features to be identified in IDP structural ensembles.

Dynamic Protein Interaction Networks and New Structural Paradigms in Signaling

Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins (IDPs) and regions (IDRs), which represent ∼30% of the proteome and enable unique regulatory mechanisms. In this review, we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of conformational ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as "ultrasensitivity" and "regulated folding and unfolding". We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information.

Quantitative evaluation of positive ϕ angle propensity in flexible regions of proteins from three-bond J couplings

(3)JHNHα and (3)JC'C' couplings can be readily measured in isotopically enriched proteins and were shown to contain precise information on the backbone torsion angles, ϕ, sampled in disordered regions of proteins. However, quantitative interpretation of these couplings required the population of conformers with positive ϕ angles to be very small. Here, we demonstrate that this restriction can be removed by measurement of (3)JC'Hα values. Even though the functional forms of the (3)JC'Hα and (3)JHNHα Karplus equations are the same, large differences in their coefficients enable accurate determination of the fraction of time that positive ϕ angles are sampled. A four-dimensional triple resonance HACANH[C'] E.COSY experiment is introduced to simultaneously measure (3)JC'Hα and (3)JHNC' in the typically very congested spectra of disordered proteins. High resolution in these spectra is obtained by non-uniform sampling (in the 0.1-0.5% range). Application to the intrinsically disordered protein α-synuclein shows that while most residues have close-to-zero positive ϕ angle populations, up to 16% positive ϕ population is observed for Asn residues. Positive ϕ angle populations determined with the new approach agree closely with consensus values from protein coil libraries and prior analysis of a large set of other NMR parameters. The combination of (3)JHNC' and (3)JC'C' provides information about the amplitude of ϕ angle dynamics.

A practical guide to small angle X-ray scattering (SAXS) of flexible and intrinsically disordered proteins

Small-angle X-ray scattering (SAXS) is a biophysical method to study the overall shape and structural transitions of biological macromolecules in solution. SAXS provides low resolution information on the shape, conformation and assembly state of proteins, nucleic acids and various macromolecular complexes. The technique also offers powerful means for the quantitative analysis of flexible systems, including intrinsically disordered proteins (IDPs). Here, the basic principles of SAXS are presented, and profits and pitfalls of the characterization of multidomain flexible proteins and IDPs using SAXS are discussed from the practical point of view. Examples of the synergistic use of SAXS with high resolution methods like X-ray crystallography and nuclear magnetic resonance (NMR), as well as other experimental and in silico techniques to characterize completely, or partially unstructured proteins, are presented.

Multi-scale ensemble modeling of modular proteins with intrinsically disordered linker regions: application to p53

In eukaryotic proteins, intrinsically disordered regions (IDRs) are ubiquitous and often exist in linker regions that flank the functional domains of modular proteins, regulating their functions. For detailed structural ensemble modeling of IDRs, we propose a multiscale method for IDRs that possess significant long-range order in modular proteins and apply it to the eukaryotic transcription factor p53 as an example. First, we performed all-atom (AA) molecular dynamics (MD) simulations of the explicitly solvated p53 linker region, without experimental restraint terms, finding fractional long-range contacts within the linker. Second, we fed this AA MD ensemble into a coarse-grained (CG) model, finding an optimal set of contact potentials. The optimized CG MD simulations reproduced the contact probability map from the AA MD simulations. Finally, we performed the CG MD simulation of the tetrameric p53 fragments including the core domains, the linker, and the tetramerization domain. Using the obtained ensemble, we theoretically calculated the small angle x-ray scattering (SAXS) profile of this fragment. The obtained SAXS profile agrees well with the experiment. We also found that the long-range contacts in the p53 linker region are required to reproduce the experimental SAXS profile. The developed framework in which we calculate the long-range contact probability map from the AA MD simulation and incorporate it to the CG model can be applied to broad range of IDRs.

Systematically constructing kinetic transition network in polypeptide from top to down: trajectory mapping

Molecular dynamics (MD) simulation is an important tool for understanding bio-molecules in microscopic temporal/spatial scales. Besides the demand in improving simulation techniques to approach experimental scales, it becomes more and more crucial to develop robust methodology for precisely and objectively interpreting massive MD simulation data. In our previous work [J Phys Chem B 114, 10266 (2010)], the trajectory mapping (TM) method was presented to analyze simulation trajectories then to construct a kinetic transition network of metastable states. In this work, we further present a top-down implementation of TM to systematically detect complicate features of conformational space. We first look at longer MD trajectory pieces to get a coarse picture of transition network at larger time scale, and then we gradually cut the trajectory pieces in shorter for more details. A robust clustering algorithm is designed to more effectively identify the metastable states and transition events. We applied this TM method to detect the hierarchical structure in the conformational space of alanine-dodeca-peptide from microsecond to nanosecond time scales. The results show a downhill folding process of the peptide through multiple pathways. Even in this simple system, we found that single common-used order parameter is not sufficient either in distinguishing the metastable states or predicting the transition kinetics among these states.

Using chemical shifts to generate structural ensembles for intrinsically disordered proteins with converged distributions of secondary structure

A short segment of the disordered p53 transactivation domain (p53TAD) forms an amphipathic helix when bound to the E3 ubiquitin ligase, MDM2. In the unbound p53TAD, this short segment has transient helical secondary structure. Using a method that combines broad sampling of conformational space with re-weighting, it is shown that it is possible to generate multiple, independent structural ensembles that have highly similar secondary structure distributions for both p53TAD and a P27A mutant. Fractional amounts of transient helical secondary structure were found at the MDM2 binding site that are very similar to estimates based directly on experimental observations. Structures were identified in these ensembles containing segments that are highly similar to short p53 peptides bound to MDM2, even though the ensembles were re-weighted using unbound experimental data. Ensembles were generated using chemical shift data (alpha carbon only, or in combination with other chemical shifts) and cross-validated by predicting residual dipolar couplings. We think this ensemble generator could be used to predict the bound state structure of protein interaction sites in IDPs if there are detectable amounts of matching transient secondary structure in the unbound state.

Visualizing transient dark states by NMR spectroscopy

Myriad biological processes proceed through states that defy characterization by conventional atomic-resolution structural biological methods. The invisibility of these 'dark' states can arise from their transient nature, low equilibrium population, large molecular weight, and/or heterogeneity. Although they are invisible, these dark states underlie a range of processes, acting as encounter complexes between proteins and as intermediates in protein folding and aggregation. New methods have made these states accessible to high-resolution analysis by nuclear magnetic resonance (NMR) spectroscopy, as long as the dark state is in dynamic equilibrium with an NMR-visible species. These methods - paramagnetic NMR, relaxation dispersion, saturation transfer, lifetime line broadening, and hydrogen exchange - allow the exploration of otherwise invisible states in exchange with a visible species over a range of timescales, each taking advantage of some unique property of the dark state to amplify its effect on a particular NMR observable. In this review, we introduce these methods and explore two specific techniques - paramagnetic relaxation enhancement and dark state exchange saturation transfer - in greater detail.

A maximum entropy approach to the study of residue-specific backbone angle distributions in α-synuclein, an intrinsically disordered protein

α-Synuclein is an intrinsically disordered protein of 140 residues that switches to an α-helical conformation upon binding phospholipid membranes. We characterize its residue-specific backbone structure in free solution with a novel maximum entropy procedure that integrates an extensive set of NMR data. These data include intraresidue and sequential H(N) − H(α) and H(N) − H(N) NOEs, values for (3) JHNHα, (1) JHαCα, (2) JCαN, and (1) JCαN, as well as chemical shifts of (15)N, (13)C(α), and (13)C' nuclei, which are sensitive to backbone torsion angles. Distributions of these torsion angles were identified that yield best agreement to the experimental data, while using an entropy term to minimize the deviation from statistical distributions seen in a large protein coil library. Results indicate that although at the individual residue level considerable deviations from the coil library distribution are seen, on average the fitted distributions agree fairly well with this library, yielding a moderate population (20-30%) of the PPII region and a somewhat higher population of the potentially aggregation-prone β region (20-40%) than seen in the database. A generally lower population of the αR region (10-20%) is found. Analysis of (1)H − (1)H NOE data required consideration of the considerable backbone diffusion anisotropy of a disordered protein.

Biophysical properties of intrinsically disordered p130Cas substrate domain--implication in mechanosensing

Mechanical stretch-induced tyrosine phosphorylation in the proline-rich 306-residue substrate domain (CasSD) of p130Cas (or BCAR1) has eluded an experimentally validated structural understanding. Cellular p130Cas tyrosine phosphorylation is shown to function in areas without internal actomyosin contractility, sensing force at the leading edge of cell migration. Circular dichroism shows CasSD is intrinsically disordered with dominant polyproline type II conformations. Strongly conserved in placental mammals, the proline-rich sequence exhibits a pseudo-repeat unit with variation hotspots 2–9 residues before substrate tyrosine residues. Atomic-force microscopy pulling experiments show CasSD requires minimal extension force and exhibits infrequent, random regions of weak stability. Proteolysis, light scattering and ultracentrifugation results show that a monomeric intrinsically disordered form persists for CasSD in solution with an expanded hydrodynamic radius. All-atom 3D conformer sampling with the TraDES package yields ensembles in agreement with experiment when coil-biased sampling is used, matching the experimental radius of gyration. Increasing β-sampling propensities increases the number of prolate conformers. Combining the results, we conclude that CasSD has no stable compact structure and is unlikely to efficiently autoinhibit phosphorylation. Taking into consideration the structural propensity of CasSD and the fact that it is known to bind to LIM domains, we propose a model of how CasSD and LIM domain family of transcription factor proteins may function together to regulate phosphorylation of CasSD and effect machanosensing.

Mechanical stretching of cells causes the substrate domain of p130Cas (CasSD) to be phosphorylated on 15 tyrosine residues embedded along its length. CasSD is rich in proline and surprisingly well conserved in placental mammals. Stretching of CasSD by atomic force microscopy has identified that it requires far less force than normal folded proteins. Classical biophysical analyses have determined that CasSD is a typical intrinsically disordered protein, a difficult-to-study group of molecules covering about 30% of human proteins. The average size of CasSD is larger and elongated than folded globular proteins but smaller than chemically denatured proteins. We have simulated a large number of all-atom protein structures using a fast all-atom sampling method. The result is in good agreement with the experimental observation. As it is already known that stretching somehow exposes the tyrosine residues to phosphorylation, a mechanism is proposed where straightening of the p130Cas substrate domain backbone conformation through mechanical stretching can lead to dissociation of p130Cas-binding LIM domain proteins and exposure of CasSD tyrosine residues for phosphorylation. This study has led to a new model of a protein-based mechanism of force sensing at the leading edge of cells that allows the cells to feel their way as they move.

Comparison of structure determination methods for intrinsically disordered amyloid-β peptides

Intrinsically disordered proteins (IDPs) represent a new frontier in structural biology since the primary characteristic of IDPs is that structures need to be characterized as diverse ensembles of conformational substates. We compare two general but very different ways of combining NMR spectroscopy with theoretical methods to derive structural ensembles for the disease IDPs amyloid-β 1-40 and amyloid-β 1-42, which are associated with Alzheimer's Disease. We analyze the performance of de novo molecular dynamics and knowledge-based approaches for generating structural ensembles by assessing their ability to reproduce a range of NMR experimental observables. In addition to the comparison of computational methods, we also evaluate the relative value of different types of NMR data for refining or validating the IDP structural ensembles for these important disease peptides.

Differences in β-strand populations of monomeric Aβ40 and Aβ42

Using homonuclear (1)H NOESY spectra, with chemical shifts, (3)JH(N)H(α) scalar couplings, residual dipolar couplings, and (1)H-(15)N NOEs, we have optimized and validated the conformational ensembles of the amyloid-β 1-40 (Aβ40) and amyloid-β 1-42 (Aβ42) peptides generated by molecular dynamics simulations. We find that both peptides have a diverse set of secondary structure elements including turns, helices, and antiparallel and parallel β-strands. The most significant difference in the structural ensembles of the two peptides is the type of β-hairpins and β-strands they populate. We find that Aβ42 forms a major antiparallel β-hairpin involving the central hydrophobic cluster residues (16-21) with residues 29-36, compatible with known amyloid fibril forming regions, whereas Aβ40 forms an alternative but less populated antiparallel β-hairpin between the central hydrophobic cluster and residues 9-13, that sometimes forms a β-sheet by association with residues 35-37. Furthermore, we show that the two additional C-terminal residues of Aβ42, in particular Ile-41, directly control the differences in the β-strand content found between the Aβ40 and Aβ42 structural ensembles. Integrating the experimental and theoretical evidence accumulated over the last decade, it is now possible to present monomeric structural ensembles of Aβ40 and Aβ42 consistent with available information that produce a plausible molecular basis for why Aβ42 exhibits greater fibrillization rates than Aβ40.

Application of the maximum entropy principle to determine ensembles of intrinsically disordered proteins from residual dipolar couplings

We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs).

Probing the residual structure in avian prion hexarepeats by CD, NMR and MD techniques

Many proteins perform essential biological functions by means of regions that lacking specific organized structure exist as an ensemble of interconverting transient conformers. The characterization of such regions, including the description of their structural propensities, number of conformations and relative populations can provide useful insights. Prion diseases result from the conversion of a normal glycoprotein into a misfolded pathogenic isoform. The structures of mammal and chicken prion proteins show a similar fold with a globular domain and a flexible N-terminal portion that contains different repeated regions: octarepeats (PHGGGWGQ) in mammals and hexarepeats (PHNPGY) in chickens. The higher number of prolines in the hexarepeat region suggests that this region may retain a significant amount of residual secondary structure. Here, we report the CD, NMR and MD characterization of a peptide (2-HexaPY) composed of two hexarepeats. We combine experimental NMR data and MD to investigate at atomic level its ensemble-averaged structural properties, demonstrating how each residue of both repeats has a different quantified PPII propensity that shows a periodicity along the sequence. This feature explains the absence of cooperativity to stabilize a PPII conformation. Nonetheless, such residual structure can play a role in nucleating local structural transitions as well as modulating intra-molecular or inter-molecular interactions.

SS-map: Visualizing cooperative secondary structure elements in protein ensembles

We present SS-map, a tool to visualize the secondary structure content of ensembles of proteins. When generating ensembles of intrinsically disordered proteins, we lose the understanding a single native structure gives for folded proteins. It then becomes difficult to visualize the composition of the ensembles or to detect transient helices such as MoRFs. Conformational propensities for single residues also hide the nature of cooperative structures. Here we show how SS-map describes folded and unfolded ensembles of some peptides and gives a new view of the ensembles used to describe intrinsically disordered proteins with residual structure in computational and NMR experiments. This tool is implemented in an open-source python code located at code.google.com/p/ss-map

Describing intrinsically disordered proteins at atomic resolution by NMR

There is growing interest in the development of physical methods to study the conformational behaviour and biological activity of intrinsically disordered proteins (IDPs). In this review recent advances in the elucidation of quantitative descriptions of disordered proteins from nuclear magnetic resonance spectroscopy are presented. Ensemble approaches are particularly well adapted to map the conformational energy landscape sampled by the protein at atomic resolution. Significant advances in development of calibrated approaches to the statistical representation of the conformational behaviour of IDPs are presented, as well as applications to some biologically important systems where disorder plays a crucial role.

Structural characterization of intrinsically disordered proteins by the combined use of NMR and SAXS

In recent years, IDPs (intrinsically disordered proteins) have emerged as pivotal actors in biology. Despite IDPs being present in all kingdoms of life, they are more abundant in eukaryotes where they are involved in the vast majority of regulation and signalling processes. The realization that, in some cases, functional states of proteins were partly or fully disordered was in contradiction to the traditional view where a well defined three-dimensional structure was required for activity. Several experimental evidences indicate, however, that structural features in IDPs such as transient secondary-structural elements and overall dimensions are crucial to their function. NMR has been the main tool to study IDP structure by probing conformational preferences at residue level. Additionally, SAXS (small-angle X-ray scattering) has the capacity to report on the three-dimensional space sampled by disordered states and therefore complements the local information provided by NMR. The present review describes how the synergy between NMR and SAXS can be exploited to obtain more detailed structural and dynamic models of IDPs in solution. These combined strategies, embedded into computational approaches, promise the elucidation of the structure-function properties of this important, but elusive, family of biomolecules.

Experiments and simulations show how long-range contacts can form in expanded unfolded proteins with negligible secondary structure

The sizes of unfolded proteins under highly denaturing conditions scale as N(0.59) with chain length. This suggests that denaturing conditions mimic good solvents, whereby the preference for favorable chain-solvent interactions causes intrachain interactions to be repulsive, on average. Beyond this generic inference, the broader implications of N(0.59) scaling for quantitative descriptions of denatured state ensembles (DSEs) remain unresolved. Of particular interest is the degree to which N(0.59) scaling can simultaneously accommodate intrachain attractions and detectable long-range contacts. Here we present data showing that the DSE of the N-terminal domain of the L9 (NTL9) ribosomal protein in 8.3 M urea lacks detectable secondary structure and forms expanded conformations in accord with the expected N(0.59) scaling behavior. Paramagnetic relaxation enhancements, however, indicate the presence of detectable long-range contacts in the denatured-state ensemble of NTL9. To explain these observations we used atomistic thermal unfolding simulations to identify ensembles whose properties are consistent with all of the experimental observations, thus serving as useful proxies for the DSE of NTL9 in 8.3 M urea. Analysis of these ensembles shows that residual attractions are present under mimics of good solvent conditions, and for NTL9 they result from low-likelihood, medium/long-range contacts between hydrophobic residues. Our analysis provides a quantitative framework for the simultaneous observation of N(0.59) scaling and low-likelihood long-range contacts for the DSE of NTL9. We propose that such low-likelihood intramolecular hydrophobic clusters might be a generic feature of DSEs that play a gatekeeping role to protect against aggregation during protein folding.

Characterization of disordered proteins with ENSEMBLE

ENSEMBLE is a computational approach for determining a set of conformations that represents the structural ensemble of a disordered protein based on input experimental data. The disordered protein can be an unfolded or intrinsically disordered state. Here, we introduce the latest version of the program, which has been enhanced to facilitate its general release and includes an intuitive user interface, as well as new approaches to treat data and analyse results.

AVAILABILITY AND IMPLEMENTATION

ENSEMBLE is a program implemented in C and embedded in a Perl wrapper. It is supported on main Linux distributions. Source codes and installation files, including a detailed example, can be freely downloaded at http://abragam.med.utoronto.ca/∼JFKlab.