Ensemble modeling of protein disordered states: experimental restraint contributions and validation

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering

The intrinsically disordered 4E-BP2 protein regulates mRNA cap-dependent translation through interaction with the predominantly folded eukaryotic initiation factor 4E (eIF4E). Phosphorylation of 4E-BP2 dramatically reduces the level of eIF4E binding, in part by stabilizing a binding-incompatible folded domain. Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). We then applied an integrative Bayesian approach to obtain NP and 5P conformational ensembles that agree with experimental data from nuclear magnetic resonance, small-angle X-ray scattering, and single-molecule Förster resonance energy transfer (smFRET). For the NP state, inter-residue distance scaling and 2D maps revealed the role of charge segregation and pi interactions in driving contacts between distal regions of the chain (∼70 residues apart). The 5P ensemble shows prominent contacts of the N-IDR region with the two phosphosites in the folded domain, pT37 and pT46, and, to a lesser extent, delocalized interactions with the C-IDR region. Agglomerative hierarchical clustering led to partitioning of each of the two ensembles into four clusters with different global dimensions and contact maps. This helped delineate an NP cluster that, based on our smFRET data, is compatible with the eIF4E-bound state. 5P clusters were differentiated by interactions of C-IDR with the folded domain and of the N-IDR with the two phosphosites in the folded domain. Our study provides both a better visualization of fundamental structural poses of 4E-BP2 and a set of falsifiable insights on intrachain interactions that bias folding and binding of this protein.

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

Quantitative prediction of ensemble dynamics, shapes and contact propensities of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup, which are two IDPs that fold upon binding to their targets, how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including 15N spin relaxation, without the need for reweighting. Further inspection revealed that 10-20% of the individual MD trajectories display the formation of secondary structures not observed in the experimental NMR data. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Integrative Conformational Ensembles of Sic1 Using Different Initial Pools and Optimization Methods

Intrinsically disordered proteins play key roles in regulatory protein interactions, but their detailed structural characterization remains challenging. Here we calculate and compare conformational ensembles for the disordered protein Sic1 from yeast, starting from initial ensembles that were generated either by statistical sampling of the conformational landscape, or by molecular dynamics simulations. Two popular, yet contrasting optimization methods were used, ENSEMBLE and Bayesian Maximum Entropy, to achieve agreement with experimental data from nuclear magnetic resonance, small-angle X-ray scattering and single-molecule Förster resonance energy transfer. The comparative analysis of the optimized ensembles, including secondary structure propensity, inter-residue contact maps, and the distributions of hydrogen bond and pi interactions, revealed the importance of the physics-based generation of initial ensembles. The analysis also provides insights into designing new experiments that report on the least restrained features among the optimized ensembles. Overall, differences between ensembles optimized from different priors were greater than when using the same prior with different optimization methods. Generating increasingly accurate, reliable and experimentally validated ensembles for disordered proteins is an important step towards a mechanistic understanding of their biological function and involvement in various diseases.

Dimerization Activity of a Disordered N-Terminal Domain from Drosophila CLAMP Protein

In Drosophila melanogaster, CLAMP is an essential zinc-finger transcription factor that is involved in chromosome architecture and functions as an adaptor for the dosage compensation complex. Most of the known Drosophila architectural proteins have structural N-terminal homodimerization domains that facilitate distance interactions. Because CLAMP performs architectural functions, we tested its N-terminal region for the presence of a homodimerization domain. We used a yeast two-hybrid assay and biochemical studies to demonstrate that the adjacent N-terminal region between 46 and 86 amino acids is capable of forming homodimers. This region is conserved in CLAMP orthologs from most insects, except Hymenopterans. Biophysical techniques, including nuclear magnetic resonance (NMR) and small-angle X-ray scattering (SAXS), suggested that this domain lacks secondary structure and has features of intrinsically disordered regions despite the fact that the protein structure prediction algorithms suggested the presence of beta-sheets. The dimerization domain is essential for CLAMP functions in vivo because its deletion results in lethality. Thus, CLAMP is the second architectural protein after CTCF that contains an unstructured N-terminal dimerization domain.

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.

Recent Developments in Data-Assisted Modeling of Flexible Proteins

Many proteins can fold into well-defined conformations. However, intrinsically-disordered proteins (IDPs) do not possess a defined structure. Moreover, folded multi-domain proteins often digress into alternative conformations. Collectively, the conformational dynamics enables these proteins to fulfill specific functions. Thus, most experimental observables are averaged over the conformations that constitute an ensemble. In this article, we review the recent developments in the concept and methods for the determination of the dynamic structures of flexible peptides and proteins. In particular, we describe ways to extract information from nuclear magnetic resonance small-angle X-ray scattering (SAXS), and chemical cross-linking coupled with mass spectroscopy (XL-MS) measurements. All these techniques can be used to obtain ensemble-averaged restraints or to re-weight the simulated conformational ensembles.

Drugging the Undruggable: Targeting the N-Terminal Domain of Nuclear Hormone Receptors

This chapter focuses on the development of drugs targeting the N-terminal domain of nuclear hormone receptors, using progress with the androgen receptor as an example. Historically, development of therapies targeting nuclear hormone receptors has focused on the folded C-terminal ligand-binding domain. Therapies were traditionally not developed to target the intrinsically disordered N-terminal domain as it was considered "undruggable". Recent developments have now shown it is possible to direct therapies to the N-terminal domain. This chapter will provide an introduction of the structure and function of the domains of nuclear hormone receptors, followed by a discussion of the rationale supporting the development of N-terminal domain inhibitors. Chemistry and mechanisms of action of small molecule inhibitors will be described with emphasis on N-terminal domain inhibitors developed to the androgen receptor including those in clinical trials.

FuzDB: a new phase in understanding fuzzy interactions

Fuzzy interactions are specific, variable contacts between proteins and other biomolecules (proteins, DNA, RNA, small molecules) formed in accord to the cellular context. Fuzzy interactions have recently been demonstrated to regulate biomolecular condensates generated by liquid-liquid phase separation. The FuzDB v4.0 database (https://fuzdb.org) assembles experimentally identified examples of fuzzy interactions, where disordered regions mediate functionally important, context-dependent contacts between the partners in stoichiometric and higher-order assemblies. The new version of FuzDB establishes cross-links with databases on structure (PDB, BMRB, PED), function (ELM, UniProt) and biomolecular condensates (PhaSepDB, PhaSePro, LLPSDB). FuzDB v4.0 is a source to decipher molecular basis of complex cellular interaction behaviors, including those in protein droplets.

Intrinsically disordered proteins: modes of binding with emphasis on disordered domains

Our notions of protein function have long been determined by the protein structure-function paradigm. However, the idea that protein function is dictated by a prerequisite complementarity of shapes at the binding interface is becoming increasingly challenged. Interactions involving intrinsically disordered proteins (IDPs) have indicated a significant degree of disorder present in the bound state, ranging from static disorder to complete disorder, termed 'random fuzziness'. This review assesses the anatomy of an IDP and relates how its intrinsic properties permit promiscuity and allow for the various modes of interaction. Furthermore, a mechanistic overview of the types of disordered domains is detailed, while also relating to a recent example and the kinetic and thermodynamic principles governing its formation.

Probabilistic Modeling of RNA Ensembles Using NMR Chemical Shifts

NMR-derived chemical shifts are structural fingerprints that are sensitive to the underlying conformational distributions of molecules. Thus, chemical shift data are now routinely used to infer the dynamical or conformational ensembles of peptides and proteins. However, for RNAs, techniques for inferring their conformational ensembles from chemical shift data have received less attention. Here, we used chemical shift data and the Bayesian/maximum entropy (BME) approach to model the secondary structure ensembles of several single-stranded RNAs. Inspection of the resulting ensembles indicates that the secondary structure of the highest weighted (most probable) conformer in the ensemble typically resembled the known NMR structure. Furthermore, using apo chemical shifts measured for the HIV-1 TAR RNA, we found that our framework reproduces the expected structure yet predicts the existence of a previously unobserved base pair, which we speculate may be sampled transiently. We expect that the chemical shift-based BME (CS-BME) framework we describe here should find utility as a general strategy for modeling RNA ensembles using chemical shift data.

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.

CheSPI: chemical shift secondary structure population inference

NMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI provides predictions for up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

Exploring Energy Landscapes of Intrinsically Disordered Proteins: Insights into Functional Mechanisms

Intrinsically disordered proteins (IDPs) lack a rigid three-dimensional structure and populate a polymorphic ensemble of conformations. Because of the lack of a reference conformation, their energy landscape representation in terms of reaction coordinates presents a daunting challenge. Here, our newly developed energy landscape visualization method (ELViM), a reaction coordinate-free approach, shows its prime application to explore frustrated energy landscapes of an intrinsically disordered protein, prostate-associated gene 4 (PAGE4). PAGE4 is a transcriptional coactivator that potentiates the oncogene c-Jun. Two kinases, namely, HIPK1 and CLK2, phosphorylate PAGE4, generating variants phosphorylated at different serine/threonine residues (HIPK1-PAGE4 and CLK2-PAGE4, respectively) with opposing functions. While HIPK1-PAGE4 predominantly phosphorylates Thr51 and potentiates c-Jun, CLK2-PAGE4 hyperphosphorylates PAGE4 and attenuates transactivation. To understand the underlying mechanisms of conformational diversity among different phosphoforms, we have analyzed their atomistic trajectories simulated using AWSEM forcefield, and the energy landscapes were elucidated using ELViM. This method allows us to identify and compare the population distributions of different conformational ensembles of PAGE4 phosphoforms using the same effective phase space. The results reveal a predominant conformational ensemble with an extended C-terminal segment of WT PAGE4, which exposes a functional residue Thr51, implying its potential of undertaking a fly-casting mechanism while binding to its cognate partner. In contrast, for HIPK1-PAGE4, a compact conformational ensemble enhances its population sequestering phosphorylated-Thr51. This clearly explains the experimentally observed weaker affinity of HIPK1-PAGE4 for c-Jun. ELViM appears as a powerful tool, especially to analyze the highly frustrated energy landscape representation of IDPs where appropriate reaction coordinates are hard to apprehend.

Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions

Function of intrinsically disordered proteins may depend on deviation of their conformational ensemble from that of a random coil. Such deviation may be hard to characterize and quantify, if it is weak. We explored the potential of distance distributions between spin labels, as they can be measured by electron paramagnetic resonance techniques, for aiding such characterization. On the example of the intrinsically disordered N-terminal domain 1-267 of fused in sarcoma (FUS) we examined what such distance distributions can and cannot reveal on the random-coil reference state. On the example of the glycine-rich domain 188-320 of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) we studied whether deviation from a random-coil ensemble can be robustly detected with 19 distance distribution restraints. We discuss limitations imposed by ill-posedness of the conversion of primary data to distance distributions and propose overlap of distance distributions as a fit criterion that can tackle this problem. For testing consistency and size sufficiency of the restraint set, we propose jack-knife resampling. At current desktop computers, our approach is expected to be viable for domains up to 150 residues and for between 10 and 50 distance distribution restraints.

Reinforcement learning to boost molecular docking upon protein conformational ensemble

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

Structural Insights into the Interaction of the Intrinsically Disordered Co-activator TIF2 with Retinoic Acid Receptor Heterodimer (RXR/RAR)

Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) form heterodimers that activate target gene transcription by recruiting co-activator complexes in response to ligand binding. The nuclear receptor (NR) co-activator TIF2 mediates this recruitment by interacting with the ligand-binding domain (LBD) of NRs trough the nuclear receptor interaction domain (TIF2_NRID) containing three highly conserved α-helical LxxLL motifs (NR-boxes). The precise binding mode of this domain to RXR/RAR is not clear due to the disordered nature of TIF2. Here we present the structural characterization of TIF2_NRID by integrating several experimental (NMR, SAXS, Far-UV CD, SEC-MALS) and computational data. Collectively, the data are in agreement with a largely disordered protein with partially structured regions, including the NR-boxes and their flanking regions, which are evolutionary conserved. NMR and X-ray crystallographic data on TIF2_NRID in complex with RXR/RAR reveal a multisite binding of the three NR-boxes as well as an active role of their flanking regions in the interaction.

Ensemble-Based Thermodynamics of the Fuzzy Binding between Intrinsically Disordered Proteins and Small-Molecule Ligands

In contrast to the "lock-and-key" model underlying the long-term success of structural biology and rational drug design, intrinsically disordered proteins (IDPs) exist in an ensemble of highly heterogeneous conformations even after binding with small-molecule ligands. It remains controversial how to characterize the thermodynamics of such fuzzy interactions. Here, we derive an ensemble-based thermodynamic framework to analyze the apparent affinity between IDPs and ligands. It is shown that the apparent affinity is related to the interaction free energy between the individual conformation and ligand in a way similar to Jarzynski's equality in nonequilibrium statistics. The oncoprotein c-Myc is adopted as an example to demonstrate the related properties, for example, the distribution of conformation-ligand interaction free energy, the entropic contribution from the ensemble, the conformation shift under ligand binding, and how to control the error under a limited number of sampled conformations.

Conformational Ensembles of an Intrinsically Disordered Protein Consistent with NMR, SAXS, and Single-Molecule FRET

Intrinsically disordered proteins (IDPs) have fluctuating heterogeneous conformations, which makes their structural characterization challenging. Although challenging, characterization of the conformational ensembles of IDPs is of great interest, since their conformational ensembles are the link between their sequences and functions. An accurate description of IDP conformational ensembles depends crucially on the amount and quality of the experimental data, how it is integrated, and if it supports a consistent structural picture. We used integrative modeling and validation to apply conformational restraints and assess agreement with the most common structural techniques for IDPs: Nuclear Magnetic Resonance (NMR) spectroscopy, Small-angle X-ray Scattering (SAXS), and single-molecule Förster Resonance Energy Transfer (smFRET). Agreement with such a diverse set of experimental data suggests that details of the generated ensembles can now be examined with a high degree of confidence. Using the disordered N-terminal region of the Sic1 protein as a test case, we examined relationships between average global polymeric descriptions and higher-moments of their distributions. To resolve apparent discrepancies between smFRET and SAXS inferences, we integrated SAXS data with NMR data and reserved the smFRET data for independent validation. Consistency with smFRET, which was not guaranteed a priori, indicates that, globally, the perturbative effects of NMR or smFRET labels on the Sic1 ensemble are minimal. Analysis of the ensembles revealed distinguishing features of Sic1, such as overall compactness and large end-to-end distance fluctuations, which are consistent with biophysical models of Sic1's ultrasensitive binding to its partner Cdc4. Our results underscore the importance of integrative modeling and validation in generating and drawing conclusions from IDP conformational ensembles.

Small-angle X-ray scattering experiments of monodisperse intrinsically disordered protein samples close to the solubility limit

The condensation of biomolecules into biomolecular condensates via liquid-liquid phase separation (LLPS) is a ubiquitous mechanism that drives cellular organization. To enable these functions, biomolecules have evolved to drive LLPS and facilitate partitioning into biomolecular condensates. Determining the molecular features of proteins that encode LLPS will provide critical insights into a plethora of biological processes. Problematically, probing biomolecular dense phases directly is often technologically difficult or impossible. By capitalizing on the symmetry between the conformational behavior of biomolecules in dilute solution and dense phases, it is possible to infer details critical to phase separation by precise measurements of the dilute phase thus circumventing complicated characterization of dense phases. The symmetry between dilute and dense phases is found in the size and shape of the conformational ensemble of a biomolecule-parameters that small-angle X-ray scattering (SAXS) is ideally suited to probe. Recent technological advances have made it possible to accurately characterize samples of intrinsically disordered protein regions at low enough concentration to avoid interference from intermolecular attraction, oligomerization or aggregation, all of which were previously roadblocks to characterizing self-assembling proteins. Herein, we describe the pitfalls inherent to measuring such samples, the experimental details required for circumventing these issues and analysis methods that place the results of SAXS measurements into the theoretical framework of LLPS.

A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins

As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.

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.

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.

Obtaining Hydrodynamic Radii of Intrinsically Disordered Protein Ensembles by Pulsed Field Gradient NMR Measurements

In the disordered state, a protein exhibits a high degree of structural freedom, in both space and time. For an ensemble of disordered or unfolded proteins, this means that the ensemble comprises a high diversity of structures, ranging from compact collapsed states to fully extended polypeptide chains. In addition, each chain is highly dynamic and undergoes conformational changes and local dynamics on both fast and slow timescales. The size properties of disordered proteins are thus best described as ensemble averages. A straightforward measure of the size is the hydrodynamic radius, R_H, of the ensemble. Since the disordered state is conformationally fluid, the observed R_H does not refer to a particular shape or fold. Instead, it should be interpreted as a measure for the average compaction of the structural ensemble. In addition to characterizing the disordered ensemble itself, R_H can be used to, with good precision, monitor changes in the ensemble size properties upon functional interactions of the disordered protein, e.g., dimerization, ligand binding, and folding pathways. Here, we present a step-by-step protocol for diffusion measurements using pulsed field gradient nuclear magnetic resonance (PFG NMR) spectroscopy. We describe how to calibrate the magnetic field gradient and offer different schemes for sample preparation. Finally, we describe how to obtain R_H directly from the diffusion coefficient as well as from using an internal standard as a reference.

Perspective: the essential role of NMR in the discovery and characterization of intrinsically disordered proteins

The 2019 ISMAR Prize recognized NMR studies of disordered proteins. Here we provide a highly personal perspective on the discovery of intrinsically disordered proteins and the development and application of NMR methods to characterize their conformational ensembles, dynamics, and interactions.

Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology

Small-angle X-ray scattering (SAXS) has emerged as an enabling integrative technique for comprehensive analyses of macromolecular structures and interactions in solution. Over the past two decades, SAXS has become a mainstay of the structural biologist's toolbox, supplying multiplexed measurements of molecular shape and dynamics that unveil biological function. Here, we discuss evolving SAXS theory, methods, and applications that extend the field of small-angle scattering beyond simple shape characterization. SAXS, coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved (TR-SAXS) methods, is now providing high-resolution insight into macromolecular flexibility and ensembles, delineating biophysical landscapes, and facilitating high-throughput library screening to assess macromolecular properties and to create opportunities for drug discovery. Looking forward, we consider SAXS in the integrative era of hybrid structural biology methods, its potential for illuminating cellular supramolecular and mesoscale structures, and its capacity to complement high-throughput bioinformatics sequencing data. As advances in the field continue, we look forward to proliferating uses of SAXS based upon its abilities to robustly produce mechanistic insights for biology and medicine.

Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts

Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.

Protein NMR: Boundless opportunities

Over the past approximately three decades, isotope-directed NMR spectroscopy has become a powerful method for determining 3D structures of biological macromolecules and their complexes in solution. From a structural perspective NMR provides an invaluable tool for studying systems that are not amenable to crystallization, including intrinsically disordered proteins and weak complexes. In contrast to both X-ray crystallography and cryo-electron microscopy which afford a largely static view of the systems under consideration, the great power of NMR lies in its ability to quantitatively probe exchange dynamics between interconverting states, and to reveal and characterize at atomic resolution the existence of transient states that may be populated at levels as low as 1%. Such "excited" states play a key role in macromolecular recognition, allostery, signal transduction and macromolecular assembly, including the initial events involved in aggregation and amyloid formation. Optimal application of NMR to such systems of fundamental biological interest requires a sound footing of the physical underpinnings of today's and tomorrow's sophisticated NMR experiments.

q-Canonical Monte Carlo Sampling for Modeling the Linkage between Charge Regulation and Conformational Equilibria of Peptides

The overall charge content and the patterning of charged residues have a profound impact on the conformational ensembles adopted by intrinsically disordered proteins. These parameters can be altered by charge regulation, which refers to the effects of post-translational modifications, pH-dependent changes to charge, and conformational fluctuations that modify the pKa values of ionizable residues. Although atomistic simulations have played a prominent role in uncovering the major sequence-ensemble relationships of IDPs, most simulations assume fixed charge states for ionizable residues. This may lead to erroneous estimates for conformational equilibria if they are linked to charge regulation. Here, we report the development of a new method we term q-canonical Monte Carlo sampling for modeling the linkage between charge regulation and conformational equilibria. The method, which is designed to be interoperable with the ABSINTH implicit solvation model, operates as follows: For a protein sequence with n ionizable residues, we start with all 2n charge microstates and use a criterion based on model compound pKa values to prune down to a subset of thermodynamically relevant charge microstates. This subset is then grouped into mesostates, where all microstates that belong to a mesostate have the same net charge. Conformational distributions, drawn from a canonical ensemble, are generated for each of the charge microstates that make up a mesostate using a method we designate as proton walk sampling. This method combines Metropolis Monte Carlo sampling in conformational space with an auxiliary Markov process that enables interconversions between charge microstates along a mesostate. Proton walk sampling helps identify the most likely charge microstate per mesostate. We then use thermodynamic integration aided by the multistate Bennett acceptance ratio method to estimate the free energies for converting between mesostates. These free energies are then combined with the per-microstate weights along each mesostate to estimate standard state free energies and pH-dependent free energies for all thermodynamically relevant charge microstates. The results provide quantitative estimates of the probabilities and preferred conformations associated with every thermodynamically accessible charge microstate. We showcase the application of q-canonical sampling using two model systems. The results establish the soundness of the method and the importance of charge regulation in systems characterized by conformational heterogeneity.

Using a FRET Library with Multiple Probe Pairs To Drive Monte Carlo Simulations of α-Synuclein

We describe a strategy for experimentally-constraining computational simulations of intrinsically disordered proteins (IDPs), using α-synuclein, an IDP with a central role in Parkinson's disease pathology, as an example. Previously, data from single-molecule Förster Resonance Energy Transfer (FRET) experiments have been effectively utilized to generate experimentally constrained computational models of IDPs. However, the fluorophores required for single-molecule FRET experiments are not amenable to the study of short-range (<30 Å) interactions. Using ensemble FRET measurements allows one to acquire data from probes with multiple distance ranges, which can be used to constrain Monte Carlo simulations in PyRosetta. To appropriately employ ensemble FRET data as constraints, we optimized the shape and weight of constraining potentials to afford ensembles of structures that are consistent with experimental data. We also used this approach to examine the structure of α-synuclein in the presence of the compacting osmolyte trimethylamine-N-oxide. Despite significant compaction imparted by 2 M trimethylamine-N-oxide, the underlying ensemble of α-synuclein remains largely disordered and capable of aggregation, also in agreement with experimental data. These proof-of-concept experiments demonstrate that our modeling protocol enables one to efficiently generate experimentally constrained models of IDPs that incorporate atomic-scale detail, allowing one to study an IDP under a variety of conditions.

The combined force field-sampling problem in simulations of disordered amyloid-β peptides

Molecular dynamics simulations of intrinsically disordered proteins (IDPs) can provide high resolution structural ensembles if the force field is accurate enough and if the simulation sufficiently samples the conformational space of the IDP with the correct weighting of sub-populations. Here, we investigate the combined force field-sampling problem by testing a standard force field as well as newer fixed charge force fields, the latter specifically motivated for better description of unfolded states and IDPs, and comparing them with a standard temperature replica exchange (TREx) protocol and a non-equilibrium Temperature Cool Walking (TCW) sampling algorithm. The force field and sampling combinations are used to characterize the structural ensembles of the amyloid-beta peptides Aβ42 and Aβ43, which both should be random coils as shown recently by experimental nuclear magnetic resonance (NMR) and 2D Förster resonance energy transfer (FRET) experiments. The results illustrate the key importance of the sampling algorithm: while the standard force field using TREx is in poor agreement with the NMR J-coupling and nuclear Overhauser effect and 2D FRET data, when using the TCW method, the standard and optimized protein-water force field combinations are in very good agreement with the same experimental data since the TCW sampling method produces qualitatively different ensembles than TREx. We also discuss the relative merit of the 2D FRET data when validating structural ensembles using the different force fields and sampling protocols investigated in this work for small IDPs such as the Aβ42 and Aβ43 peptides.

Troubleshooting Guide to Expressing Intrinsically Disordered Proteins for Use in NMR Experiments

Intrinsically disordered proteins (IDPs) represent a structural class of proteins that do not have a well-defined, 3D fold in solution, and often have little secondary structure. To characterize their function and molecular mechanism, it is helpful to examine their structure using nuclear magnetic resonance (NMR), which can report on properties, such as residual structure (at both the secondary and tertiary levels), ligand binding affinity, and the effect of ligand binding on IDP structure, all on a per residue basis. This brief review reports on the common problems and decisions that are involved when preparing a disordered protein for NMR studies. The paper covers gene design, expression host choice, protein purification, and the initial NMR experiments that are performed. While many of these steps are essentially identical to those for ordered proteins, a few key differences are highlighted, including the extreme sensitivity of IDPs to proteolytic cleavage, the ability to use denaturing conditions without having to refold the protein, the optimal chromatographic system choice, and the challenges of quantifying an IDP. After successful purification, characterization by NMR can be done using the standard ¹⁵N-heteronuclear single quantum coherence (¹⁵N-HSQC) experiment, or the newer CON series of experiments that are superior for disordered proteins.

AWSEM-IDP: A Coarse-Grained Force Field for Intrinsically Disordered Proteins

The associative memory, water-mediated, structure and energy model (AWSEM) has been successfully used to study protein folding, binding, and aggregation problems. In this work, we introduce AWSEM-IDP, a new AWSEM branch for simulating intrinsically disordered proteins (IDPs), where the weights of the potentials determining secondary structure formation have been finely tuned, and a novel potential is introduced that helps to precisely control both the average extent of protein chain collapse and the chain's fluctuations in size. AWSEM-IDP can efficiently sample large conformational spaces, while retaining sufficient molecular accuracy to realistically model proteins. We applied this new model to two IDPs, demonstrating that AWSEM-IDP can reasonably well reproduce higher-resolution reference data, thus providing the foundation for a transferable IDP force field. Finally, we used thermodynamic perturbation theory to show that, in general, the conformational ensembles of IDPs are highly sensitive to fine-tuning of force field parameters.

Characterization of intrinsically disordered proteins and their dynamic complexes: From in vitro to cell-like environments

Over the last two decades, it has become increasingly clear that a large fraction of the human proteome is intrinsically disordered or contains disordered segments of significant length. These intrinsically disordered proteins (IDPs) play important regulatory roles throughout biology, underlining the importance of understanding their conformational behavior and interaction mechanisms at the molecular level. Here we review recent progress in the NMR characterization of the structure and dynamics of IDPs in various functional states and environments. We describe the complementarity of different NMR parameters for quantifying the conformational propensities of IDPs in their isolated and phosphorylated states, and we discuss the challenges associated with obtaining structural models of dynamic protein-protein complexes involving IDPs. In addition, we review recent progress in understanding the conformational behavior of IDPs in cell-like environments such as in the presence of crowding agents, in membrane-less organelles and in the complex environment of the human cell.

Conformational Heterogeneity and FRET Data Interpretation for Dimensions of Unfolded Proteins

A mathematico-physically valid formulation is required to infer properties of disordered protein conformations from single-molecule Förster resonance energy transfer (smFRET). Conformational dimensions inferred by conventional approaches that presume a homogeneous conformational ensemble can be unphysical. When all possible-heterogeneous as well as homogeneous-conformational distributions are taken into account without prejudgment, a single value of average transfer efficiency 〈E〉 between dyes at two chain ends is generally consistent with highly diverse, multiple values of the average radius of gyration 〈R_g〉. Here we utilize unbiased conformational statistics from a coarse-grained explicit-chain model to establish a general logical framework to quantify this fundamental ambiguity in smFRET inference. As an application, we address the long-standing controversy regarding the denaturant dependence of 〈R_g〉 of unfolded proteins, focusing on Protein L as an example. Conventional smFRET inference concluded that 〈R_g〉 of unfolded Protein L is highly sensitive to [GuHCl], but data from SAXS suggested a near-constant 〈R_g〉 irrespective of [GuHCl]. Strikingly, our analysis indicates that although the reported 〈E〉 values for Protein L at [GuHCl] = 1 and 7 M are very different at 0.75 and 0.45, respectively, the Bayesian R_g² distributions consistent with these two 〈E〉 values overlap by as much as 75%. Our findings suggest, in general, that the smFRET-SAXS discrepancy regarding unfolded protein dimensions likely arise from highly heterogeneous conformational ensembles at low or zero denaturant, and that additional experimental probes are needed to ascertain the nature of this heterogeneity.

Effect of a Paramagnetic Spin Label on the Intrinsically Disordered Peptide Ensemble of Amyloid-β

Paramagnetic relaxation enhancement is an NMR technique that has yielded important insight into the structure of folded proteins, although the perturbation introduced by the large spin probe might be thought to diminish its usefulness when applied to characterizing the structural ensembles of intrinsically disordered proteins (IDPs). We compare the computationally generated structural ensembles of the IDP amyloid-β42 (Aβ42) to an alternative sequence in which a nitroxide spin label attached to cysteine has been introduced at its N-terminus. Based on this internally consistent computational comparison, we find that the spin label does not perturb the signature population of the β-hairpin formed by residues 16-21 and 29-36 that is dominant in the Aβ42 reference ensemble. However, the presence of the tag induces a strong population shift in a subset of the original Aβ42 structural sub-populations, including a sevenfold enhancement of the β-hairpin formed by residues 27-31 and 33-38. Through back-calculation of NMR observables from the computational structural ensembles, we show that the structural differences between the labeled and unlabeled peptide would be evident in local residual dipolar couplings, and possibly differences in homonuclear ¹H-¹H nuclear Overhauser effects (NOEs) and heteronuclear ¹H-¹⁵N NOEs if the paramagnetic contribution to the longitudinal relaxation does not suppress the NOE intensities in the real experiment. This work shows that molecular simulation provides a complementary approach to resolving the potential structural perturbations introduced by reporter tags that can aid in the interpretation of paramagnetic relaxation enhancement, double electron-electron resonance, and fluorescence resonance energy transfer experiments applied to IDPs.

Structure of an Intrinsically Disordered Stress Protein Alone and Bound to a Membrane Surface

Dehydrins are a group of intrinsically disordered proteins that protect plants from damage caused by drought, cold, and high salinity. Like other intrinsically disordered proteins, dehydrins can gain structure when bound to a ligand. Previous studies have shown that dehydrins are able to protect liposomes from cold damage, but the interactions that drive membrane binding and the detailed structure of the bound and unbound forms are not known. We use an ensemble-structure approach to generate models of a dehydrin known as K2 in the presence and absence of sodium dodecyl sulfate micelles, and we docked the bound structure to the micelle. The collection of residual dipolar coupling data, amide protection factors, and paramagnetic relaxation enhancement distances, in combination with chemical shifts and relaxation measurements, allows for determining plausible structures that are not otherwise visible in time-averaged structural data. The results show that in the bound structure, the conserved lysines are important for membrane binding, whereas the flanking hydrophobic residues play a lesser role. The unbound structure shows a high level of disorder and an extended structure. We propose that the structural differences between bound and unbound forms allow dehydrins to act as molecular shields in their unbound state and as membrane protectants in their bound state. Unlike α-synuclein, the significant gain of α-helicity in K2 at low concentrations of sodium dodecyl sulfate is not due to a decrease in the critical micelle concentration. The study provides structural insight into how a disordered protein can interact with a membrane surface.

Application of NMR to studies of intrinsically disordered proteins

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

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.

Structural Characterization of the Intrinsically Disordered Protein p53 Using Raman Spectroscopy

The intrinsically disordered protein p53 has attracted a strong interest for its important role in genome safeguarding and potential therapeutic applications. However, its disordered character makes difficult a full characterization of p53 structural architecture. A deep knowledge of p53 structural motifs could significantly help the understanding of its functional properties, in connection with its complex binding network. We have applied Raman spectroscopy to investigate the structural composition and the conformational heterogeneity of both full-length p53 and its DNA binding domain (DBD), in different solvent environments. In particular, a careful analysis of the Amide I Raman band, which is highly sensitive to protein secondary structure elements such as α-helices, β-sheets and random coils, has revealed the presence of extended random coils in p53 and predominant β-sheet regions in its DBD. In addition, this analysis has allowed us to explore the ensemble of interchanging conformations in both p53 and its DBD, highlighting a higher conformational heterogeneity in p53 than in its DBD. Furthermore, by applying a principal components analysis, we have identified the principal spectral markers in both p53 and DBD samples. The combination of the two approaches could be insightful for the study of intrinsically disordered proteins, by offering increased versatility and wide application as a label-free, real-time and non-invasive detection method.

The metastasis suppressor KISS1 is an intrinsically disordered protein slightly more extended than a random coil

The metastasis suppressor KISS1 is reported to be involved in the progression of several solid neoplasias, making it a promising molecular target for controlling their metastasis. The KISS1 sequence contains an N-terminal secretion signal and several dibasic sequences that are proposed to be the proteolytic cleavage sites. We present the first structural characterization of KISS1 by circular dichroism, multi-angle light scattering, small angle X-Ray scattering and NMR spectroscopy. An analysis of the KISS1 backbone NMR chemical shifts does not reveal any preferential conformation and deviation from a random coil ensemble. The backbone ¹⁵N transverse relaxation times indicate a mildly reduced mobility for two regions that are rich in bulky residues. The small angle X-ray scattering curve of KISS1 is likewise consistent with a predominantly random coil ensemble, although an ensemble optimization analysis indicates some preference for more extended conformations possibly due to positive charge repulsion between the abundant basic residues. Our results support the hypothesis that KISS1 mostly samples a random coil conformational space, which is consistent with its high susceptibility to proteolysis and the generation of Kisspeptin fragments.

Shedding light on the extra thermal stability of thermophilic proteins

An entropic stabilization mechanism has recently gained attention and credibility as the physical ground for the extra thermal stability of globular proteins from thermophilic microorganisms. An empirical result, obtained from the analysis of thermodynamic data for a large set of proteins, strengthens the general reliability of the theoretical approach originally devised to rationalize the occurrence of cold denaturation [Graziano, PCCP 2014, 16, 21755-21767]. It is shown that this theoretical approach can readily account for the entropic stabilization mechanism. On decreasing the conformational entropy gain associated with denaturation, the thermal stability of a model globular protein increases markedly.

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.

Structured States of Disordered Proteins from Genomic Sequences

Protein flexibility ranges from simple hinge movements to functional disorder. Around half of all human proteins contain apparently disordered regions with little 3D or functional information, and many of these proteins are associated with disease. Building on the evolutionary couplings approach previously successful in predicting 3D states of ordered proteins and RNA, we developed a method to predict the potential for ordered states for all apparently disordered proteins with sufficiently rich evolutionary information. The approach is highly accurate (79%) for residue interactions as tested in more than 60 known disordered regions captured in a bound or specific condition. Assessing the potential for structure of more than 1,000 apparently disordered regions of human proteins reveals a continuum of structural order with at least 50% with clear propensity for three- or two-dimensional states. Co-evolutionary constraints reveal hitherto unseen structures of functional importance in apparently disordered proteins.

FuzDB: database of fuzzy complexes, a tool to develop stochastic structure-function relationships for protein complexes and higher-order assemblies

FuzDB (http://protdyn-database.org) compiles experimentally observed fuzzy protein complexes, where intrinsic disorder (ID) is maintained upon interacting with a partner (protein, nucleic acid or small molecule) and directly impacts biological function. Entries in the database have both (i) structural evidence demonstrating the structural multiplicity or dynamic disorder of the ID region(s) in the partner bound form of the protein and (ii) in vitro or in vivo biological evidence that indicates the significance of the fuzzy region(s) in the formation, function or regulation of the assembly. Unlike the other intrinsically disordered or unfolded protein databases, FuzDB focuses on ID regions within a biological context, including higher-order assemblies and presents a detailed analysis of the structural and functional data. FuzDB also provides interpretation of experimental results to elucidate the molecular mechanisms by which fuzzy regions-classified on the basis of topology and mechanism-interfere with the structural ensembles and activity of protein assemblies. Regulatory sites generated by alternative splicing (AS) or post-translational modifications (PTMs) are also collected. By assembling all this information, FuzDB could be utilized to develop stochastic structure-function relationships for proteins and could contribute to the emergence of a new paradigm.