There is Diversity in Disorder-"In all Chaos there is a Cosmos, in all Disorder a Secret Order"

Transient Structural Properties of the Rho GDP-Dissociation Inhibitor

Rho GTPases, master spatial regulators of a wide range of cellular processes, are orchestrated by complex formation with guanine nucleotide dissociation inhibitors (RhoGDIs). These have been thought to possess an unstructured N-terminus that inhibits nucleotide exchange of their client upon binding/folding. Via NMR analyses, molecular dynamics simulations, and biochemical assays, we reveal instead pertinent structural properties transiently maintained both, in the presence and absence of the client, imposed onto the terminus context-specifically by modulating interactions with the surface of the folded C-terminal domain. These observations revise the long-standing textbook picture of the GTPases' mechanism of membrane extraction. Rather than by a disorder-to-order transition upon binding of an inhibitory peptide, the intricate and highly selective extraction process of RhoGTPases is orchestrated via a dynamic ensemble bearing preformed transient structural properties, suitably modulated by the specific surrounding along the multi-step process.

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs

In studying secondary structural propensities of proteins by nuclear magnetic resonance (NMR) spectroscopy, secondary chemical shifts (SCSs) serve as the primary atomic scale observables. For SCS calculation, the selection of an appropriate random coil chemical shift (RCCS) dataset is a crucial step, especially when investigating intrinsically disordered proteins (IDPs). The scientific literature is abundant in such datasets, however, the effect of choosing one over all the others in a concrete application has not yet been studied thoroughly and systematically. Hereby, we review the available RCCS prediction methods and to compare them, we conduct statistical inference by means of the nonparametric sum of ranking differences and comparison of ranks to random numbers (SRD-CRRN) method. We try to find the RCCS predictors best representing the general consensus regarding secondary structural propensities. The existence and the magnitude of resulting differences on secondary structure determination under varying sample conditions (temperature, pH) are demonstrated and discussed for globular proteins and especially IDPs.

SETH predicts nuances of residue disorder from protein embeddings

Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer’s Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, “experimentally resolved” prediction and “relative solvent accessibility”) correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH’s nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.

Backbone NMR assignments of the extensive human and chicken TRPV4 N-terminal intrinsically disordered regions as important players in ion channel regulation

Transient receptor potential (TRP) channels are important pharmacological targets due to their ability to act as sensory transducers on the organismic and cellular level, as polymodal signal integrators and because of their role in numerous diseases. However, a detailed molecular understanding of the structural dynamics of TRP channels and their integration into larger cellular signalling networks remains challenging, in part due to the systematic absence of highly dynamic regions pivotal for channel regulation from available structures. In human TRP vanilloid 4 (TRPV4), a ubiquitously expressed homotetrameric cation channel involved in temperature, osmo- and mechano-sensation and in a multitude of (patho)physiological processes, the intrinsically disordered N-terminus encompasses 150 amino acids and thus represents > 17% of the entire channel sequence. Its deletion renders the channel significantly less excitable to agonists supporting a crucial role in TRPV4 activation and regulation. For a structural understanding and a comparison of its properties across species, we determined the NMR backbone assignments of the human and chicken TRPV4 N-terminal IDRs.

The Ni(II)-Binding Activity of the Intrinsically Disordered Region of Human NDRG1, a Protein Involved in Cancer Development

Nickel exposure is associated with tumors of the respiratory tract such as lung and nasal cancers, acting through still-uncharacterized mechanisms. Understanding the molecular basis of nickel-induced carcinogenesis requires unraveling the mode and the effects of Ni(II) binding to its intracellular targets. A possible Ni(II)-binding protein and a potential focus for cancer treatment is hNDRG1, a protein induced by Ni(II) through the hypoxia response pathway, whose expression correlates with higher cancer aggressiveness and resistance to chemotherapy in lung tissue. The protein sequence contains a unique C-terminal sequence of 83 residues (hNDRG1*C), featuring a three-times-repeated decapeptide, involved in metal binding, lipid interaction and post-translational phosphorylation. In the present work, the biochemical and biophysical characterization of unmodified hNDRG1*C was performed. Bioinformatic analysis assigned it to the family of the intrinsically disordered regions and the absence of secondary and tertiary structure was experimentally proven by circular dichroism and NMR. Isothermal titration calorimetry revealed the occurrence of a Ni(II)-binding event with micromolar affinity. Detailed information on the Ni(II)-binding site and on the residues involved was obtained in an extensive NMR study, revealing an octahedral paramagnetic metal coordination that does not cause any major change of the protein backbone, which is coherent with CD analysis. hNDRG1*C was found in a monomeric form by light-scattering experiments, while the full-length hNDRG1 monomer was found in equilibrium between the dimer and tetramer, both in solution and in human cell lines. The results are the first essential step for understanding the cellular function of hNDRG1*C at the molecular level, with potential future applications to clarify its role and the role of Ni(II) in cancer development.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Non-specific porins of Gram-negative bacteria as proteins containing intrinsically disordered regions with amyloidogenic potential

Features of the structure and functional activity of bacterial outer membrane porins, coupled with their dynamic "behavior," suggests that intrinsically disordered regions (IDPRs) are contained in their structure. Using bioinformatic analysis, the quantitative content of amyloidogenic regions in the amino acid sequence of non-specific porins inhabiting various natural niches was determined: from terrestrial bacteria of the genus Yersinia (OmpF and OmpC proteins of Y. pseudotuberculosis and Y. ruckeri) and from the marine bacterium Marinomonas primoryensis (MpOmp). It was found that OmpF and OmpC porins can be classified as moderately disordered proteins, while MpOmp can be classified as highly disordered protein. Mapping of IDPRs, performed using 3D structures of monomers of the proteins, showed that the regions of increased conformational plasticity fall on the regions, the functional importance of which has been reliably confirmed as a result of numerous experimental studies. The revealed correlation made it possible to explain the differences in the physicochemical characteristics and properties of not only porins from terrestrial and marine bacteria, but also non-specific porins of different types, OmpF and OmpC proteins. First of all, this concerns the flexible outer loops that form the pore vestibule, as well as regions of the barrel with an increased "ability" for aggregation, the so-called "hot spots" of aggregation. The abnormally high content of IDPRs in the MpOmp structure made it possible to suggest that the high adaptive potential of bacteria may correlate with an increase in the number of IDPRs and/or regions with increased conformational variability.

Tandem domain structure determination based on a systematic enumeration of conformations

Protein structure determination is undergoing a change of perspective due to the larger importance taken in biology by the disordered regions of biomolecules. In such cases, the convergence criterion is more difficult to set up and the size of the conformational space is a obstacle to exhaustive exploration. A pipeline is proposed here to exhaustively sample protein conformations using backbone angle limits obtained by nuclear magnetic resonance (NMR), and then to determine the populations of conformations. The pipeline is applied to a tandem domain of the protein whirlin. An original approach, derived from a reformulation of the Distance Geometry Problem is used to enumerate the conformations of the linker connecting the two domains. Specifically designed procedure then permit to assemble the domains to the linker conformations and to optimize the tandem domain conformations with respect to two sets of NMR measurements: residual dipolar couplings and paramagnetic resonance enhancements. The relative populations of optimized conformations are finally determined by fitting small angle X-ray scattering (SAXS) data. The most populated conformation of the tandem domain is a semi-closed one, fully closed and more extended conformations being in minority, in agreement with previous observations. The SAXS and NMR data show different influences on the determination of populations.

The contribution of electrostatics to hydrogen exchange in the unfolded protein state

Although electrostatics have long been recognized to play an important role in hydrogen exchange (HX) with solvent, the quantitative assessment of its magnitude in the unfolded state has hitherto been lacking. This limits the utility of HX as a quantitative method to study protein stability, folding, and dynamics. Using the intrinsically disordered human protein α-synuclein as a proxy for the unfolded state, we show that a hybrid mean-field approach can effectively compute the electrostatic potential at all backbone amide positions along the chain. From the electrochemical potential, a fourfold reduction in hydroxide concentration near the protein backbone is predicted for the C-terminal domain, a prognosis that is in direct agreement with experimentally derived protection factors from NMR spectroscopy. Thus, impeded HX for the C-terminal region of α-synuclein is not the result of intramolecular hydrogen bonding and/or structure formation.

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.

Calmodulin Directly Interacts with the Cx43 Carboxyl-Terminus and Cytoplasmic Loop Containing Three ODDD-Linked Mutants (M147T, R148Q, and T154A) that Retain α-Helical Structure, but Exhibit Loss-of-Function and Cellular Trafficking Defects

The autosomal-dominant pleiotropic disorder called oculodentodigital dysplasia (ODDD) is caused by mutations in the gap junction protein Cx43. Of the 73 mutations identified to date, over one-third are localized in the cytoplasmic loop (Cx43CL) domain. Here, we determined the mechanism by which three ODDD mutations (M147T, R148Q, and T154A), all of which localize within the predicted 1-5-10 calmodulin-binding motif of the Cx43CL, manifest the disease. Nuclear magnetic resonance (NMR) and circular dichroism revealed that the three ODDD mutations had little-to-no effect on the ability of the Cx43CL to form α-helical structure as well as bind calmodulin. Combination of microscopy and a dye-transfer assay uncovered these mutations increased the intracellular level of Cx43 and those that trafficked to the plasma membrane did not form functional channels. NMR also identify that CaM can directly interact with the Cx43CT domain. The Cx43CT residues involved in the CaM interaction overlap with tyrosines phosphorylated by Pyk2 and Src. In vitro and in cyto data provide evidence that the importance of the CaM interaction with the Cx43CT may lie in restricting Pyk2 and Src phosphorylation, and their subsequent downstream effects.

ODiNPred: comprehensive prediction of protein order and disorder

Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

Quantitative Protein Disorder Assessment Using NMR Chemical Shifts

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

An intrinsically disordered proteins community for ELIXIR

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled “An intrinsically disordered protein user community proposal for ELIXIR” held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders.

E46K-like α-synuclein mutants increase lipid interactions and disrupt membrane selectivity

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, and both genetic and histopathological evidence have implicated the ubiquitous presynaptic protein α-synuclein (αSyn) in its pathogenesis. Recent work has investigated how disrupting αSyn's interaction with membranes triggers trafficking defects, cellular stress, and apoptosis. Special interest has been devoted to a series of mutants exacerbating the effects of the E46K mutation (associated with autosomal dominant PD) through homologous Glu-to-Lys substitutions in αSyn's N-terminal region (i.e. E35K and E61K). Such E46K-like mutants have been shown to cause dopaminergic neuron loss and severe but L-DOPA-responsive motor defects in mouse overexpression models, presenting enormous translational potential for PD and other "synucleinopathies." In this work, using a variety of biophysical techniques, we characterize the molecular pathology of E46K-like αSyn mutants by studying their structure and membrane-binding and remodeling abilities. We find that, although a slight increase in the mutants' avidity for synaptic vesicle-like membranes can be detected, most of their deleterious effects are connected to their complete disruption of αSyn's curvature selectivity. Indiscriminate binding can shift αSyn's subcellular localization away from its physiological interactants at the synaptic bouton toward trafficking vesicles and organelles, as observed in E46K-like cellular and murine models, as well as in human pathology. In conclusion, our findings suggest that a loss of curvature selectivity, rather than increased membrane affinity, could be the critical dyshomeostasis in synucleinopathies.

Quality and bias of protein disorder predictors

Disorder in proteins is vital for biological function, yet it is challenging to characterize. Therefore, methods for predicting protein disorder from sequence are fundamental. Currently, predictors are trained and evaluated using data from X-ray structures or from various biochemical or spectroscopic data. However, the prediction accuracy of disordered predictors is not calibrated, nor is it established whether predictors are intrinsically biased towards one of the extremes of the order-disorder axis. We therefore generated and validated a comprehensive experimental benchmarking set of site-specific and continuous disorder, using deposited NMR chemical shift data. This novel experimental data collection is fully appropriate and represents the full spectrum of disorder. We subsequently analyzed the performance of 26 widely-used disorder prediction methods and found that these vary noticeably. At the same time, a distinct bias for over-predicting order was identified for some algorithms. Our analysis has important implications for the validity and the interpretation of protein disorder, as utilized, for example, in assessing the content of disorder in proteomes.

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25-78%, with the largest improvements observed for ¹Hα and ¹³C'. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org .

MOAG-4 promotes the aggregation of α-synuclein by competing with self-protective electrostatic interactions

Aberrant protein aggregation underlies a variety of age-related neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Little is known, however, about the molecular mechanisms that modulate the aggregation process in the cellular environment. Recently, MOAG-4/SERF has been identified as a class of evolutionarily conserved proteins that positively regulates aggregate formation. Here, by using nuclear magnetic resonance (NMR) spectroscopy, we examine the mechanism of action of MOAG-4 by characterizing its interaction with α-synuclein (α-Syn). NMR chemical shift perturbations demonstrate that a positively charged segment of MOAG-4 forms a transiently populated α-helix that interacts with the negatively charged C terminus of α-Syn. This process interferes with the intramolecular interactions between the N- and C-terminal regions of α-Syn, resulting in the protein populating less compact forms and aggregating more readily. These results provide a compelling example of the complex competition between molecular and cellular factors that protect against protein aggregation and those that promote it.

Structural Basis of the Substrate Specificity and Enzyme Catalysis of a Papaver somniferum Tyrosine Decarboxylase

Tyrosine decarboxylase (TyDC), a type II pyridoxal 5′-phosphate decarboxylase, catalyzes the decarboxylation of tyrosine. Due to a generally high sequence identity to other aromatic amino acid decarboxylases (AAADs), primary sequence information is not enough to understand substrate specificities with structural information. In this study, we selected a typical TyDC from Papaver somniferum as a model to study the structural basis of AAAD substrate specificities. Analysis of the native P. somniferum TyDC crystal structure and subsequent molecular docking and dynamics simulation provide some structural bases that explain substrate specificity for tyrosine. The result confirmed the previous proposed mechanism for the enzyme selectivity of indolic and phenolic substrates. Additionally, this study yields the first crystal structure for a plant type II pyridoxal-5'-phosphate decarboxylase.

An Integrated Mass Spectrometry Based Approach to Probe the Structure of the Full-Length Wild-Type Tetrameric p53 Tumor Suppressor

We present an integrated approach for investigating the topology of proteins through native mass spectrometry (MS) and cross-linking/MS, which we applied to the full-length wild-type p53 tetramer. For the first time, the two techniques were combined in one workflow to obtain not only structural insight in the p53 tetramer, but also information on the cross-linking efficiency and the impact of cross-linker modification on the conformation of an intrinsically disordered protein (IDP). P53 cross-linking was monitored by native MS and as such, our strategy serves as a quality control for different cross-linking reagents. Our approach can be applied to the structural investigation of various protein systems, including IDPs and large protein assemblies, which are challenging to study by the conventional methods used for protein structure characterization.