Intrinsically Disordered Proteins Research Articles

Scale-free Spatio-temporal Correlations in Conformational Fluctuations of Intrinsically Disordered Proteins.

The self-assembly of intrinsically disordered proteins (IDPs) into condensed phases and the formation of membrane-less organelles (MLOs) can be considered as the phenomenon of collective behavior. The conformational dynamics of IDPs are essential for their interactions and the formation of a condensed phase. From a physical perspective, collective behavior and the emergence of phase are associated with long-range correlations. Here the conformational dynamics of IDPs and the correlations therein are analyzed, using µs-scale atomistic molecular dynamics (MD) simulations and single-molecule Förster resonance energy transfer (smFRET) experiments. The existence of typical scale-free spatio-temporal correlations in IDP conformational fluctuations is demonstrated. Their conformational evolutions exhibit "1/f noise" power spectra and are accompanied by the appearance of residue domains following a power-law size distribution. Additionally, the motions of residues present scale-free behavioral correlation. These scale-free correlations resemble those in physical systems near critical points, suggesting that IDPs are poised at a critical state. Therefore, IDPs can effectively respond to finite differences in sequence compositions and engender considerable structural heterogeneity which is beneficial for IDP interactions and phase formation.

Context-Dependent Heterotypic Assemblies of Intrinsically Disordered Peptides.

Despite their critical role in context-dependent interactions for protein functions, intrinsically disordered regions (IDRs) are often overlooked for designing peptide assemblies. Here, we exploit IDRs to enable context-dependent heterotypic assemblies of intrinsically disordered peptides, where "context-dependent" refers to assembly behavior driven by interactions with other molecules. By attaching an aromatic segment to oppositely charged intrinsically disordered peptides, we achieve a nanofiber formation. Although the same-charged peptides cannot self-assemble, oppositely charged peptides form heterotypic nanofibers. Cryo-EM analysis reveals a β-sheet arrangement within the ordered core of these nanofibers, conformational heterogeneity, and a disorder-to-order continuum and shows a high number of hydrogen bonds between tyrosine and lysine ε-amine. Additionally, this work demonstrates a post-assembly morphological change resulting from local conformational flexibility. While equal molar mixtures of the charged intrinsically disordered peptides yield nanofibers, doubling the positively charged peptides after assembly produces bundles of nanofibers. Furthermore, reducing the number of aromatic amino acid residues reduces bundle formation. Demonstrating context-dependent self-assembly of intrinsically disordered peptides and revealing atomistic insights into heterotypic assemblies of intrinsically disordered peptides for the first time, this work illustrates a straightforward approach to enable heterotypic intrinsically disordered peptides to self-assemble for the design of adaptive, multifunctional peptide nanomaterials.

A multivalent engagement of ENL with MOZ.

The epigenetic cofactor ENL (eleven-nineteen-leukemia) and the acetyltransferase MOZ (monocytic leukemia zinc finger) have vital roles in transcriptional regulation and are implicated in aggressive forms of leukemia. Here, we describe the mechanistic basis for the intertwined association of ENL and MOZ. Genomic analysis shows that ENL and MOZ co-occupy active promoters and that MOZ recruits ENL to its gene targets. Structural studies reveal a multivalent assembly of ENL at the intrinsically disordered region (IDR) of MOZ. While the extraterminal (ET) domain of ENL recognizes the canonical ET-binding motif in IDR, the YEATS domains of ENL and homologous AF9 bind to a set of acetylation sites in the MOZ IDR that are generated by the acetyltransferase CBP (CREB-binding protein). Our findings suggest a multifaceted acetylation-dependent and independent coupling of ENL, MOZ and CBP/p300, which may contribute to leukemogenic activities of the ENL-MOZ assembly and chromosomal translocations of ENL, MOZ and CBP/p300.

Inherent asymmetry of Rpd3S coordinates its nucleosome engagement and association with elongating RNA polymerase II.

The Rpd3S histone deacetylase complex has a crucial role in genomic integrity by deacetylating transcribed nucleosomes following RNA polymerase (Pol) II passage. Cryo-EM studies highlight the importance of asymmetrical Rco1-Eaf3 dimers in nucleosome binding, yet the interaction dynamics with nucleosomal substrates alongside elongating Pol II are poorly understood. Here we demonstrate the essential function of the Rco1 N-terminal intrinsically disordered region (IDR) in modulating Pol II association, in which K/R mutations within the Rco1 IDR impair interaction of Rpd3S with the C-terminal domain (CTD) of Rpb1, without affecting nucleosome recognition or complex integrity. We also identify the Rco1-PHD1 and Eaf3-CHD domains as crucial for specific binding to Ser5-phosphorylated CTD. The Rco1 IDR alleviates autoinhibition from its C terminus, facilitating PHD1-CHD engagement with phosphorylated CTD. Furthermore, we reveal a conserved mechanism by which asymmetrical Rco1-Eaf3 dimers coordinate nucleosome engagement and Pol II interaction, enhancing understanding of epigenetic complexes associated with transcriptional machinery.

RBBP6 anchors pre-mRNA 3' end processing to nuclear speckles for efficient gene expression.

Pre-mRNA 3' processing is an integral step in mRNA biogenesis. However, where this process occurs in the nucleus remains unknown. Here, we demonstrate that nuclear speckles (NSs), membraneless organelles enriched with splicing factors, are major sites for pre-mRNA 3' processing in human cells. We show that the essential pre-mRNA 3' processing factor retinoblastoma-binding protein 6 (RBBP6) associates strongly with NSs via its C-terminal intrinsically disordered region (IDR). Importantly, although the conserved N-terminal domain (NTD) of RBBP6 is sufficient for pre-mRNA 3' processing invitro, its IDR-mediated association with NSs is required for efficient pre-mRNA 3' processing in cells. Through proximity labeling analyses, we provide evidence that pre-mRNA 3' processing for over 50% of genes occurs near NSs. We propose that NSs serve as hubs for RNA polymerase II transcription, pre-mRNA splicing, and 3' processing, thereby enhancing the efficiency and coordination of different gene expression steps.

Localized Reconstruction of Multimodal Distance Distribution from DEER Data of Biopolymers.

Pulsed Dipolar ESR Spectroscopy (PDS) is a uniquely powerful technique to characterize the structural property of intrinsically disordered proteins (IDPs) and polymers and the conformational evolution of IDPs and polymers, e.g. during assembly, by offering the probability distribution of segment end-to-end distances. However, it is challenging to determine distance distribution P ( r ) of IDPs by PDS because of the uncertain and broad shape information that is intrinsic to the distance distribution of IDPs. We demonstrate here that the Srivastava-Freed Singular Value Decomposition (SF-SVD) point-wise mathematical inversion method along with wavelet denoising (WavPDS) can aid in obtaining reliable shapes for the distance distribution, P ( r ), for IDPs. We show that broad regions of P ( r ) as well as mixed narrow and broad features within the captured distance distribution range can be effectively resolved and differentiated without a priori knowledge. The advantage of SF-SVD and WavPDS is that the methods are transparent, requiring no adjustable parameters, the processing of the magnitude for the probability distribution is performed separately for each distance increment, and the outcome of the analysis is independent of the user's judgement. We demonstrate the performance and present the application of WavPDS and SF-SVD on model ruler molecules, model polyethylene glycol polymers with end-to-end spin labeling, and IDPs with pairwise labeling spanning different segments of the protein tau to generate the transparent solutions to the P ( r )'s including their uncertainties and error analysis.

Divergence in a Eukaryotic Transcription Factor's co-TF Dependence Involves Multiple Intrinsically Disordered Regions Affecting Activation and Autoinhibition.

Combinatorial control by multiple transcription factors (TFs) is a hallmark of eukaryotic gene regulation. Despite its prevalence and crucial roles in enhancing specificity and integrating information, the mechanisms behind why eukaryotic TFs depend on one another, and whether such interdependence evolves, are not well understood. We exploit natural variation in co-TF dependence in the yeast phosphate starvation (PHO) response to address this question. In the model yeast Saccharomyces cerevisiae , the main TF, Pho4, relies on the co-TF Pho2 to regulate ∼28 genes. In a related yeast pathogen, Candida glabrata , its Pho4 exhibits significantly reduced Pho2 dependence and has an expanded target set of ∼70 genes. Biochemical analyses showed C. glabrata Pho4 (CgPho4) binds to the same consensus motif with 3-4-fold higher affinity than ScPho4 does. A machine-learning-based prediction and yeast one-hybrid assay identified two Intrinsically Disordered Regions (IDRs) in CgPho4 that boost the activity of the main activation domain but showed little to no activity on their own. We also found evidence for autoinhibition behind the co-TF dependence in ScPho4. An IDR in ScPho4 next to its DNA binding domain was found to act as a double-edged sword: it both allows for enhanced activity with Pho2, and inhibits Pho4's activity without Pho2. This study provides a detailed molecular picture of how co-TF dependence is mediated and how its evolution, mainly driven by IDR divergence, can lead to significant rewiring of the regulatory network.

Evaluation of predictions of disordered binding regions in the CAID2 experiment.

A large portion of the Intrinsically Disordered Regions (IDRs) in protein sequences interact with proteins, nucleic acids, and other types of ligands. Correspondingly, dozens of sequence-based predictors of binding IDRs were developed. A recently completed second community-based Critical Assessments of protein Intrinsic Disorder prediction (CAID2) evaluated 32 predictors of binding IDRs. However, CAID2 considered a rather narrow scenario by testing on 78 proteins with binding IDRs and not differentiating between different ligands, in spite that virtually all predictors target IDRs that interact with specific types of ligands. In that scenario, several intrinsic disorder predictors predict binding IDRs with accuracy equivalent to the best predictors of binding IDRs since large majority of IDRs in the 78 test proteins are binding. We substantially extended the CAID2's evaluation by using the entire CAID2 dataset of 348 proteins and considering several arguably more practical scenarios. We assessed whether predictors accurately differentiate binding IDRs from other types of IDRs and how they perform when predicting IDRs that interact with different ligand types. We found that intrinsic disorder predictors cannot accurately identify binding IDRs among other disordered regions, majority of the predictors of binding IDRs are ligand type agnostic (i.e., they cross predict binding in IDRs that interact with ligands that they do not cover), and only a handful of predictors of binding IDRs perform relatively well and generate reasonably low amounts of cross predictions. We also suggest a number of future research directions that would move this active field of research forward.

Ion mobility mass spectrometry unveils conformational effects of drug lead EPI-001 on the intrinsically disordered N-terminal domain of the androgen receptor.

Intrinsically disordered proteins (IDPs) are important drug targets as they are key actors within cell signaling networks. However, the conformational plasticity of IDPs renders them challenging to characterize, which is a bottleneck in developing small molecule drugs that bind to IDPs and modulate their behavior. In relation to this, ion mobility mass spectrometry (IM-MS) is a useful tool to investigate IDPs, as it can reveal their conformational preferences. It can also offer important insights in drug discovery, as it can measure binding stoichiometry and unveil conformational shifts of IDPs exerted by the binding of small drug-like molecules. Herein, we have used IM-MS to investigate the effect of drug lead EPI-001 on the disordered N-terminal domain of the androgen receptor (AR-NTD). Despite structural heterogeneity rendering the NTD a challenging region of the protein to drug, this domain harbors most, if not all, of the transcriptional activity. We quantify the stoichiometry of EPI-001 binding to various constructs corresponding to functional domains of AR-NTD and show that it binds to separate constructs containing transactivation unit (TAU)-1 and TAU-5, respectively, and that 1-2 molecules bind to a larger construct containing both sequences. We also identify a conformational shift upon EPI-001 binding to the TAU-5, and to a much lesser extent with TAU-1 containing constructs. This work provides novel insight on the interactions of EPI-001 with the AR-NTD, and the structural alterations that it exerts, and positions IM-MS as an informative tool that will enhance the tractability of IDPs, potentially leading to better therapies.

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

Computational Prediction of Linear Interacting Peptides.

Intrinsically disordered protein regions, IDRs, are observed in many eukaryotic proteins. They play critical roles in essentially all cellular processes because segments of these regions, known as linear interacting peptides (LIPs), are heavily involved in regulatory protein interactions across proteomes. This chapter presents an integrated summary of the results from the last two Critical Assessments of protein Intrinsic Disorder predictions, known as CAID events, on the computational prediction of LIP segments. Because the CAID community questioned the quality of the test dataset used by the first CAID event, we reannotated this dataset using more accurate annotations from the latest DisProt database release. Then, we compared the results of the first CAID with the updated data and the results of the second CAID event. Our comparison highlights the importance of data annotation on the evaluation outcome and provides recommendations for users of LIP predictors.

PairK: Pairwise k‐mer alignment for quantifying protein motif conservation in disordered regions

AbstractProtein–protein interactions are often mediated by a modular peptide recognition domain binding to a short linear motif (SLiM) in the disordered region of another protein. To understand the features of SLiMs that are important for binding and to identify motif instances that are important for biological function, it is useful to examine the evolutionary conservation of motifs across homologous proteins. However, the intrinsically disordered regions (IDRs) in which SLiMs reside evolve rapidly. Consequently, multiple sequence alignment (MSA) of IDRs often misaligns SLiMs and underestimates their conservation. We present PairK (pairwise k‐mer alignment), an MSA‐free method to align and quantify the relative local conservation of subsequences within an IDR. Lacking a ground truth for conservation, we tested PairK on the task of distinguishing biologically important motif instances from background motifs, under the assumption that biologically important motifs are more conserved. The method outperforms both standard MSA‐based conservation scores and a modern LLM‐based conservation score predictor. PairK can quantify conservation over wider phylogenetic distances than MSAs, indicating that some SLiMs are more conserved than MSA‐based metrics imply. PairK is available as an open‐source python package at https://github.com/jacksonh1/pairk. It is designed to be easily adapted for use with other SLiM tools and for diverse applications.

A Vanillin‐Derived Inhibitor of Aggregates via Targeting Intrinsically Disordered Regions of Phytoviral Nucleocapsid Protein

ABSTRACTPhase separation (PS) plays a fundamental role in organizing aggregates during the viral lifecycle, providing significant opportunities for in viral disease treatment by inhibiting PS. Intrinsically disordered regions (IDRs) have been extensively studied and found to be critical for PS. However, the discovery of small molecules that target residues within IDRs remains underexplored, particularly in the field of pesticides. Herein, we report a novel phytovirucide compound 29, which was screened from a series of vanillin derivatives designed with sulfonylpiperazine motifs. The inactivation efficacy of compound 29 against tomato spotted wilt virus (TSWV) was significantly superior to that of the control agents vanisulfane and ribavirin. Mechanistically, compound 29 binds to the TSWV nucleocapsid protein (NP) at residues Lys68 (K68), Thr92 (T92), and Arg94 (R94), with T92 and R94 located in the IDRs of NP. Mutations at these sites impair the ability to form aggregates. Furthermore, a host factor, GTP (Guanosine Triphosphate)‐binding nuclear protein Ran‐like (Niben101scf08341g01001, NbRANL), which interacts with NP and promotes its aggregation, was identified. Compound 29 also suppresses the expression of NbRANL, resulting in the dual inhibition of ribonucleoprotein complexes (RNPs) formation. This unique mechanism of action provides insights into IDRs‐based virucide discovery.

DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction.

The prediction of intrinsic disorder regions has significant implications for understanding protein functions and dynamics. It can help to discover novel protein-protein interactions essential for designing new drugs and enzymes. Recently, a new generation of predictors based on protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without calculating time-consuming multiple sequence alignments (MSAs). This article introduces the new DisorderUnetLM disorder predictor, which builds upon the idea of ProteinUnet. It uses the Attention U-Net convolutional network and incorporates features from the ProtTrans pLM. DisorderUnetLM achieves top results in the direct comparison with recent predictors exploiting MSAs and pLMs. Moreover, among 43 predictors on the latest CAID-2 benchmark, it ranks 1st for the NOX subset in terms of the ROC-AUC metric (0.844) and 2nd for the AP metric (0.596). For the CAID-2 PDB subset, it ranks in the top 10 (ROC-AUC of 0.924 and AP of 0.862). The code and model are publicly available and fully reproducible at doi.org/10.24433/CO.7350682.v1.

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein

The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defined ligand-binding pockets in its disordered structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able to statistically distinguish the fuzzy ensemble of conformational substates of αS in neat water from those in aqueous fasudil (small molecule of interest) solution. In particular, the presence of fasudil in the solvent either modulates pre-existing states of αS or gives rise to new conformational states of αS, akin to an ensemble-expansion mechanism. The ensembles display strong conformation-dependence in residue-wise interaction with the small molecule. A thermodynamic analysis indicates that small-molecule modulates the structural repertoire of αS by tuning protein backbone entropy, however entropy of the water remains unperturbed. Together, this study sheds light on the intricate interplay between small molecules and IDPs, offering insights into entropic modulation and ensemble expansion as key biophysical mechanisms driving potential therapeutics.

Identifying sequential differences between protein structural classes using network and statistical approaches

Protein sequence information is believed to embed the hint of their structures. To uncover the nature between protein sequence and their structures, this study motivates to inspect the dynamic interactions between various protein sequence features, and identify the sequential differences between the different protein structures. Protein sequence data from all structural classes in CATH and SCOP, and the structural disordered proteins from DisProt, as well as the structural motifs in PROSITE, are analyzed in this study. Betweenness and closeness centrality measures are employed to capture the topology of the networks constructed from amino acid feature interactions, while statistical tests are further implemented to compare the feature series distributions. Key findings suggest that in all structural classes, the features for Ala and α-helix and bend preference property, Ala and side-chain size, Ala and Gly, as well as Met and Leu attain significant interactions between each other, and the feature for Leu, Val, and Asn are acted as the critical sources of feature interactions, whereas Cys, His, Trp, and Met exhibit weak intra-type interactions with other features. These implicate that these feature interactions may have little impact in coding the structural differences. For the α structures, Glu, Pro and side-chain size, hydrophobicity properties exhibit high importance in feature interactions, whereas Gly, Thr and physical properties such as α-helix and bend preference, extended structural preference, pK-C value and surrounding hydrophobicity for β structures, show special high importance in β structures. Both α and β types of structures show Ser as the common sources of feature interactions, while the mixed α and β structures not only show common characters with the α and β types of structures, but also preferred interactions between Met, Lys and double-bend preference property, and between the sequence arrangements of Cys, His, Met, Tyr and amino acid composition features. The intrinsically disordered proteins (IDPs) present high frequency for the repetition patterns of certain amino acids, while the different structural motifs also show special characters. More sequential differences between the structures can also be identified from K-mers statistics and feature series distributions. The new discoveries reveal the nature of amino acid feature interaction mechanics, and show great importance of these interactions in coding the different types of protein structures. The results can not only contribute to future molecular design for protein-based vaccine or drug, but also enlighten the development for new protein structural classifiers.

Bioinformatic Analysis of Actin-Binding Proteins in the Nucleolus During Heat Shock.

Actin plays a crucial role not only in the cytoplasm, but also in the nucleus, influencing various cellular behaviors, including cell migration and gene expression. Recent studies reveal that nuclear actin dynamics is altered by cellular stresses, such as DNA damage; however, the effect of heat shock on nuclear actin dynamics, particularly in the nucleolus, remains unclear. This study aims to elucidate the contribution of nucleolar actin to cellular responses under heat shock conditions. Nuclear actin dynamics in response to heat shock were investigated using nAC-GFP, a GFP-tagged actin chromobody, to visualize nuclear actin in HeLa cells. Bioinformatic analyses were also performed. Heat shock induced the reversible assembly of nAC-GFP in the nucleolus, with disassembly occurring upon recovery in a heat shock protein (Hsp) 70-dependent manner. Because the nucleolus, formed via liquid-liquid phase separation (LLPS), sequesters misfolded proteins under heat shock to prevent irreversible aggregation, we hypothesized that nucleolar actin-binding proteins might also be sequestered in a similar manner. Using several databases, we identified 47 actin-binding proteins localized in the nucleolus and determined the proportion of intrinsically disordered regions (IDRs) known to promote LLPS. Our analysis revealed that many of these 47 proteins exhibited high levels of IDRs. The findings from our bioinformatics analysis and further cellular studies may help elucidate new roles for actin in the heat shock response.

Intrinsic disordered nature and prediction of secondary structures of pinoresinol lariciresinol reductases 2 in Flax (Linum usitatissimum)

Flax (Linum usitatissimum) contains a wide range of essential nutrients, such as proteins, polyunsaturated fatty acids (PUFAs), phenolic compounds, fibers, flavonoids, and lignans. The production of lignans in flax is mainly caused by pinoresinol-lariciresinol reductases (PLRs). Amongst these, PLR2s play a pivotal function by contributing to the further reduction of lariciresinol to secoisolariciresinol in the biosynthesis of lignans. However, the in-silico analysis of PLR2 gene family interlinking the intrinsic disordered nature and secondary structures that could support this hypothesis is still missing in Flax. Herein, we present the first study of Intrinsic disorder proteins (IDPs) in flax to gain an understanding of their biological functions. This study was conducted from June to August 2024 at the Department of Biochemistry, Lovely Professional University, Phagwara, Punjab, India. The flax genome assembly from Phytozome was used to retrieve 30 PLR2 genes which encode for 30 PLR2 proteins. Further, we used PONDR database to identify the intrinsic disordered nature of the proteins while GOR database was used to predict the secondary structures. For identifying the amino acid composition, Protparam database was used. The results of PONDR database revealed that all the proteins are somewhat disordered whereas amino acid composition marked the presence of disorder-promoting residues in almost all the proteins. Secondary structure predictions by GOR revealed the presence of coils, helices and strands. To relate the structural functional relationship, it is of the utmost importance to have an in-depth knowledge of the balance between intrinsic disorder and secondary structure as this will provide key insights into the dynamic functionality and regulatory mechanisms that are the foundation of complex biological processes.

Using temperature coefficients to support resonance assignment of intrinsically disordered proteins.

The resonance assignment of large intrinsically disordered proteins (IDPs) is difficult due to the low dispersion of chemical shifts (CSs). Luckily, CSs are often specific for certain residue types, which makes the task easier. Our recent work showed that the CS-based spin-system classification can be improved by applying a linear discriminant analysis (LDA). In this paper, we extend a set of classification parameters by adding temperature coefficients (TCs), i.e., rates of change of chemical shifts with temperature. As demonstrated previously by other groups, the TCs in IDPs depend on a residue type, although the relation is often too complex to be predicted theoretically. Thus, we propose an approach based on experimental data; CSs and TCs values of residues assigned using conventional methods serve as a training set for LDA, which then classifies the remaining resonances. The method is demonstrated on a large fragment (1-239) of highly disordered protein Tau. We noticed that adding TCs to sets of chemical shifts significantly improves the recognition efficiency. For example, it allows distinguishing between lysine and glutamic acid, as well as valine and isoleucine residues based on , N, and C data. Moreover, adding TCs to CSs of , N, , and C is more beneficial than adding CSs. Our program for LDA analysis is available at https://github.com/gugumatz/LDA-Temp-Coeff .

A genetically encoded anomalous SAXS ruler to probe the dimensions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) adopt ensembles of rapidly fluctuating heterogeneous conformations, influencing their binding capabilities and supramolecular transitions. The primary conformational descriptors for understanding IDP ensembles-the radius of gyration (RG), measured by small-angle X-ray scattering (SAXS), and the root mean square (rms) end-to-end distance (RE), probed by fluorescent resonance energy transfer (FRET)-are often reported to produce inconsistent results regarding IDP expansion as a function of denaturant concentration in the buffer. This ongoing debate surrounding the FRET-SAXS discrepancy raises questions about the overall reliability of either method for quantitatively studying IDP properties. To address this discrepancy, we introduce a genetically encoded anomalous SAXS (ASAXS) ruler, enabling simultaneous and direct measurements of RG and RE without assuming a specific structural model. This ruler utilizes a genetically encoded noncanonical amino acid with two bromine atoms, providing an anomalous X-ray scattering signal for precise distance measurements. Through this approach, we experimentally demonstrate that the ratio between RE and RG varies under different denaturing conditions, highlighting the intrinsic properties of IDPs as the primary source of the observed SAXS-FRET discrepancy rather than shortcomings in either of the two established methods. The developed genetically encoded ASAXS ruler emerges as a versatile tool for both IDPs and folded proteins, providing a unified approach for obtaining complementary and site-specific conformational information in scattering experiments, thereby contributing to a deeper understanding of protein functions.