Introducing protein intrinsic disorder

Unveiling the genetic tapestry: Rare disease genomics of spinal muscular atrophy and phenylketonuria proteins

Rare diseases, defined by their low prevalence, present significant challenges, including delayed detection, expensive treatments, and limited research. This study delves into the genetic basis of two noteworthy rare diseases in Saudi Arabia: Phenylketonuria (PKU) and Spinal Muscular Atrophy (SMA). PKU, resulting from mutations in the phenylalanine hydroxylase (PAH) gene, exhibits geographical variability and impacts intellectual abilities. SMA, characterized by motor neuron loss, is linked to mutations in the survival of motor neuron 1 (SMN1) gene. Recognizing the importance of unveiling signature genomics in rare diseases, we conducted a quantitative study on PAH and SMN1 proteins of multiple organisms by employing various quantitative techniques to assess genetic variations. The derived signature-genomics contributes to a deeper understanding of these critical genes, paving the way for enhanced diagnostics for disorders associated with PAH and SMN1.

The strand exchange domain of tumor suppressor PALB2 is intrinsically disordered and promotes oligomerization-dependent DNA compaction

The Partner and Localizer of BRCA2 (PALB2) is a scaffold protein that links BRCA1 with BRCA2 to initiate homologous recombination (HR). PALB2 interaction with DNA strongly enhances HR efficiency in cells. The PALB2 DNA-binding domain (PALB2-DBD) supports strand exchange, a complex multistep reaction conducted by only a few proteins such as RecA-like recombinases and Rad52. Using bioinformatics analysis, small-angle X-ray scattering, circular dichroism, and electron paramagnetic spectroscopy, we determined that PALB2-DBD is an intrinsically disordered region (IDR) forming compact molten globule-like dimer. IDRs contribute to oligomerization synergistically with the coiled-coil interaction. Using confocal single-molecule FRET we demonstrated that PALB2-DBD compacts single-stranded DNA even in the absence of DNA secondary structures. The compaction is bimodal, oligomerization-dependent, and is driven by IDRs, suggesting a novel strand exchange mechanism. Intrinsically disordered proteins (IDPs) are prevalent in the human proteome. Novel DNA binding properties of PALB2-DBD and the complexity of strand exchange mechanism significantly expands the functional repertoire of IDPs. Multivalent interactions and bioinformatics analysis suggest that PALB2 function is likely to depend on formation of protein-nucleic acids condensates. Similar intrinsically disordered DBDs may use chaperone-like mechanism to aid formation and resolution of DNA and RNA multichain intermediates during DNA replication, repair and recombination.

When an underdog becomes a major player: the role of protein structural disorder in the Atg8 conjugation system

The noncanonical ubiquitin-like conjugation cascade involving the E1 (Atg7), E2 (Atg3, Atg10), and E3 (Atg12-Atg5-Atg16 complex) enzymes is essential for incorporation of Atg8 into the growing phagophore via covalent linkage to PE. This process is an indispensable step in autophagy. Atg8 and E1-E3 enzymes are the first subset from the core autophagy protein machinery structures that were investigated in earlier studies by crystallographic analyses of globular domains. However, research over the past decade shows that many important functions in the conjugation machinery are mediated by intrinsically disordered protein regions (IDPRs) - parts of the protein that do not adopt a stable secondary or tertiary structure, which are inherently dynamic and well suited for protein-membrane interactions but are invisible in protein crystals. Here, we summarize earlier and recent findings on the autophagy conjugation machinery by focusing on the IDPRs. This summary reveals that IDPRs, originally considered dispensable, are in fact major players and a driving force in the function of the autophagy conjugation system. Abbreviation: AD, activation domain of Atg7; AH, amphipathic helix; AIM, Atg8-family interacting motif; CL, catalytic loop (of Atg7); CTD, C-terminal domain; FR, flexible region (of Atg3 or Atg10); GUV, giant unilammelar vesicles; HR, handle region (of Atg3); IDPR, intrinsically disordered protein region; IDPs: intrinsically disordered proteins; LIR, LC3-interacting region; NHD: N-terminal helical domain; NMR, nuclear magnetic resonance; PE, phosphatidylethanolamine; UBL, ubiquitin like.

Dynamics of Single-Chain Nanoparticles under Crowding: A Neutron Spin Echo Study

We present a neutron spin echo (NSE) investigation to examine the impact of macromolecular crowding on the dynamics of single-chain nanoparticles (SCNPs), serving as synthetic models for biomacromolecules with flexibility and internal degrees of freedom, such as intrinsically disordered proteins (IDPs). In particular, we studied the dynamics of a medium-size poly(methyl methacrylate) (PMMA)-based SCNP (33 kDa) in solutions with low- (10 kDa) and high- (100 kDa) molecular weight analogous deuterated PMMA linear crowders. The dynamic structure factors of the SCNPs in dilute solution show certain degrees of freedom, yet the analysis in terms of the Zimm model reveals high internal friction that effectively stiffens the chain-a phenomenon also observed for IDPs. Under crowding conditions, the internal dynamics remains essentially unchanged, but the center-of-mass diffusion slows down. The effective viscosity felt by the SCNPs at the timescales probed by NSE is lower than the macroscopic viscosity of the crowder solution, and it does not depend significantly on the molecular weight.

4,4-Difluoroproline as a Unique 19F NMR Probe of Proline Conformation

Despite the importance of proline conformational equilibria (trans versus cis amide and exo versus endo ring pucker) on protein structure and function, there is a lack of convenient ways to probe proline conformation. 4,4-Difluoroproline (Dfp) was identified to be a sensitive 19F NMR-based probe of proline conformational biases and cis-trans isomerism. Within model compounds and disordered peptides, the diastereotopic fluorines of Dfp exhibit similar chemical shifts (ΔδFF = 0-3 ppm) when a trans X-Dfp amide bond is present. In contrast, the diastereotopic fluorines exhibit a large (ΔδFF = 5-12 ppm) difference in chemical shift in a cis X-Dfp prolyl amide bond. DFT calculations, X-ray crystallography, and solid-state NMR spectroscopy indicated that ΔδFF directly reports on the relative preference of one proline ring pucker over the other: a fluorine which is pseudo-axial (i.e., the pro-4R-F in an exo ring pucker, or the pro-4S-F in an endo ring pucker) is downfield, while a fluorine which is pseudo-equatorial (i.e., pro-4S-F when exo, or pro-4R-F when endo) is upfield. Thus, when a proline is disordered (a mixture of exo and endo ring puckers, as at trans-Pro in peptides in water), it exhibits a small Δδ. In contrast, when the Pro is ordered (i.e., when one ring pucker is strongly preferred, as in cis-Pro amide bonds, where the endo ring pucker is strongly favored), a large Δδ is observed. Dfp can be used to identify inherent induced order in peptides and to quantify proline cis-trans isomerism. Using Dfp, we discovered that the stable polyproline II helix (PPII) formed in the denatured state (8 M urea) exhibits essentially equal populations of the exo and endo proline ring puckers. In addition, the data with Dfp suggested the specific stabilization of PPII by water over other polar solvents. These data strongly support the importance of carbonyl solvation and n → π* interactions for the stabilization of PPII. Dfp was also employed to quantify proline cis-trans isomerism as a function of phosphorylation and the R406W mutation in peptides derived from the intrinsically disordered protein tau. Dfp is minimally sterically disruptive and can be incorporated in expressed proteins, suggesting its broad application in understanding proline cis-trans isomerization, protein folding, and local order in intrinsically disordered proteins.

The green cupredoxin CopI is a multicopper protein able to oxidize Cu(I)

Anthropogenic activities in agriculture and health use the antimicrobial properties of copper. This has led to copper accumulation in the environment and contributed to the emergence of copper resistant microorganisms. Understanding bacterial copper homeostasis diversity is therefore highly relevant since it could provide valuable targets for novel antimicrobial treatments. The periplasmic CopI protein is a monodomain cupredoxin comprising several copper binding sites and is directly involved in copper resistance in bacteria. However, its structure and mechanism of action are yet to be determined. To study the different binding sites for cupric and cuprous ions and to understand their possible interactions, we have used mutants of the putative copper binding modules of CopI and spectroscopic methods to characterize their properties. We show that CopI is able to bind a cuprous ion in its central histidine/methionine-rich region and oxidize it thanks to its cupredoxin center. The resulting cupric ion can bind to a third site at the N-terminus of the protein. Nuclear magnetic resonance spectroscopy revealed that the central histidine/methionine-rich region exhibits a dynamic behavior and interacts with the cupredoxin binding region. CopI is therefore likely to participate in copper resistance by detoxifying the cuprous ions from the periplasm.

Hydrophobic Clusters Regulate Surface Hydration Dynamics of Bacillus subtilis Lipase A

The surface hydration diffusivity of Bacillus subtilis Lipase A (BSLA) has been characterized by low-field Overhauser dynamic nuclear polarization (ODNP) relaxometry using a series of spin-labeled constructs. Sites for spin-label incorporation were previously designed via an atomistic computational approach that screened for surface exposure, reflective of the surface hydration comparable to other proteins studied by this method, as well as minimal impact on protein function, dynamics, and structure of BSLA by excluding any surface site that participated in greater than 30% occupancy of a hydrogen bonding network within BSLA. Experimental ODNP relaxometry coupling factor results verify the overall surface hydration behavior for these BSLA spin-labeled sites similar to other globular proteins. Here, by plotting the ODNP parameters of relative diffusive water versus the relative bound water, we introduce an effective "phase-space" analysis, which provides a facile visual comparison of the ODNP parameters of various biomolecular systems studied to date. We find notable differences when comparing BSLA to other systems, as well as when comparing different clusters on the surface of BSLA. Specifically, we find a grouping of sites that correspond to the spin-label surface location within the two main hydrophobic core clusters of the branched aliphatic amino acids isoleucine, leucine, and valine cores observed in the BSLA crystal structure. The results imply that hydrophobic clustering may dictate local surface hydration properties, perhaps through modulation of protein conformations and samplings of the unfolded states, providing insights into how the dynamics of the hydration shell is coupled to protein motion and fluctuations.

Reexamining the diverse functions of arginine in biochemistry

Arginine in a free-state and as part of peptides and proteins shows distinct tendency to form clusters. In free-form, it has been found useful in cryoprotection, as a drug excipient for both solid and liquid formulations, as an aggregation suppressor, and an eluent in protein chromatography. In many cases, the mechanisms by which arginine acts in all these applications is either debatable or at least continues to attract interest. It is quite possible that arginine clusters may be involved in many such applications. Furthermore, it is possible that such clusters are likely to behave as intrinsically disordered polypeptides. These considerations may help in understanding the roles of arginine in diverse applications and may even lead to better strategies for using arginine in different situations.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Location of S-nitrosylated cysteines in protein three-dimensional structures

Although S-nitrosylation of cysteines is a common protein posttranslational modification, little is known about its three-dimensional structural features. This paper describes a systematic survey of the data available in the Protein Data Bank. Several interesting observations could be made. (1) As a result of radiation damage, S-nitrosylated cysteines (Snc) are frequently reduced, at least partially. (2) S-nitrosylation may be a protection against irreversible thiol oxidation; because the NO group of Snc is relatively accessible to the solvent, it may act as a cork to protect the sulfur atoms of cysteines from oxidation by molecular oxygen to sulfenic, sulfinic, and sulfonic acid; moreover, Snc are frequently found at the start or end of helices and strands and this might shield secondary structural elements from unfolding.

The disordered C-terminal tail of fungal LPMOs from phytopathogens mediates protein dimerization and impacts plant penetration

Lytic polysaccharide monooxygenases (LPMOs) are monocopper enzymes that oxidatively degrade various polysaccharides, such as cellulose. Despite extensive research on this class of enzymes, the role played by their C-terminal regions predicted to be intrinsically disordered (dCTR) has been overlooked. Here, we investigated the function of the dCTR of an LPMO, called CoAA9A, up-regulated during plant infection by Colletotrichum orbiculare, the causative agent of anthracnose. After recombinant production of the full-length protein, we found that the dCTR mediates CoAA9A dimerization in vitro, via a disulfide bridge, a hitherto-never-reported property that positively affects both binding and activity on cellulose. Using SAXS experiments, we show that the homodimer is in an extended conformation. In vivo, we demonstrate that gene deletion impairs formation of the infection-specialized cell called appressorium and delays penetration of the plant. Using immunochemistry, we show that the protein is a dimer not only in vitro but also in vivo when secreted by the appressorium. As these peculiar LPMOs are also found in other plant pathogens, our findings open up broad avenues for crop protection.

Exploring the shared pathogenic strategies of independently evolved effectors across distinct plant viruses

Plants have developed very diverse strategies to defend themselves against viral pathogens, among which plant hormones play pivotal roles. In response, some viruses have also deployed multifunctional viral effectors that effectively hijack key component hubs to counter or evade plant immune surveillance. Although significant progress has been made toward understanding counter-defense strategies that manipulate plant hormone regulatory molecules, these efforts have often been limited to an individual virus or specific host target/pathway. This review provides new insights into broad-spectrum antiviral responses in rice triggered by key components of phytohormone signaling, and highlights the common features of counter-defense strategies employed by distinct rice-infecting RNA viruses. These strategies involve the secretion of multifunctional virulence effectors that target the sophisticated phytohormone system, dampening immune responses by engaging with the same host targets. Additionally, the review provides an in-depth exploration of various viral effectors, emphasizing tertiary structure-based research and shared host targets. Understanding these conserved characteristics in detail may pave the way for molecular drug design, opening new opportunities to enhance broad-spectrum antiviral trials through precise engineering.

Best practices for the manual curation of intrinsically disordered proteins in DisProt

The DisProt database is a resource containing manually curated data on experimentally validated intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) from the literature. Developed in 2005, its primary goal was to collect structural and functional information into proteins that lack a fixed three-dimensional structure. Today, DisProt has evolved into a major repository that not only collects experimental data but also contributes to our understanding of the IDPs/IDRs roles in various biological processes, such as autophagy or the life cycle mechanisms in viruses or their involvement in diseases (such as cancer and neurodevelopmental disorders). DisProt offers detailed information on the structural states of IDPs/IDRs, including state transitions, interactions and their functions, all provided as curated annotations. One of the central activities of DisProt is the meticulous curation of experimental data from the literature. For this reason, to ensure that every expert and volunteer curator possesses the requisite knowledge for data evaluation, collection and integration, training courses and curation materials are available. However, biocuration guidelines concur on the importance of developing robust guidelines that not only provide critical information about data consistency but also ensure data acquisition.This guideline aims to provide both biocurators and external users with best practices for manually curating IDPs and IDRs in DisProt. It describes every step of the literature curation process and provides use cases of IDP curation within DisProt. Database URL: https://disprot.org/.

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

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

Invariant BECN1 CXXC motifs bind Zn2+ and regulate structure and function of the BECN1 intrinsically disordered region

ABBREVIATIONS

AFM: aromatic finger mutant; BH3D: BCL2 homology 3 domain; CCD: coiled-coil domain; CD: circular dichroism spectroscopy; [CysDM1]: C18S and C21S double mutant; [CysDM2]: C137S, and C140S double mutant; [CysTM], C18S, C21S, C137S, and C140S tetrad mutant; Dmax: maximum particle diameter; dRI, differential refractive index; EFA: evolving factor analysis; FHD: flexible helical domain; FL: full length; GFP: green fluorescent protein; HDX-MS: hydrogen/deuterium exchange mass spectrometry; ICP-MS: inductively coupled plasma mass spectrometry; IDR: intrinsically disordered region; ITC, isothermal titration calorimetry; MALS, multi angle light scattering; MBP: maltose-binding protein; MoRFs: molecular recognition features; P(r): pairwise-distance distribution; PtdIns3K: class III phosphatidylinositol 3-kinase; Rg: radius of gyration; SASBDB: small angle scattering biological data bank; SEC: size-exclusion chromatography; SEC-SAXS: size-exclusion chromatography in tandem with small angle X-ray scattering; TEV: tobacco-etch virus; TFE: 2,2,2-trifluoroethanol; TPEN: N,N,N,N-tetrakis(2-pyridinylmethyl)-1,2-ethanediamine; Vc: volume of correlation; WT: wild-type.

Biological importance of arginine: A comprehensive review of the roles in structure, disorder, and functionality of peptides and proteins

Arginine shows Jekyll and Hyde behavior in several respects. It participates in protein folding via ionic and H-bonds and cation-pi interactions; the charge and hydrophobicity of its side chain make it a disorder-promoting amino acid. Its methylation in histones; RNA binding proteins; chaperones regulates several cellular processes. The arginine-centric modifications are important in oncogenesis and as biomarkers in several cardiovascular diseases. The cross-links involving arginine in collagen and cornea are involved in pathogenesis of tissues but have also been useful in tissue engineering and wound-dressing materials. Arginine is a part of active site of several enzymes such as GTPases, peroxidases, and sulfotransferases. Its metabolic importance is obvious as it is involved in production of urea, NO, ornithine and citrulline. It can form unusual functional structures such as molecular tweezers in vitro and sprockets which engage DNA chains as part of histones in vivo. It has been used in design of cell-penetrating peptides as drugs. Arginine has been used as an excipient in both solid and injectable drug formulations; its role in suppressing opalescence due to liquid-liquid phase separation is particularly very promising. It has been known as a suppressor of protein aggregation during protein refolding. It has proved its usefulness in protein bioseparation processes like ion-exchange, hydrophobic and affinity chromatographies. Arginine is an amino acid, whose importance in biological sciences and biotechnology continues to grow in diverse ways.

Helicity of a tardigrade disordered protein contributes to its protective function during desiccation

To survive extreme drying (anhydrobiosis), many organisms, spanning every kingdom of life, accumulate intrinsically disordered proteins (IDPs). For decades, the ability of anhydrobiosis-related IDPs to form transient amphipathic helices has been suggested to be important for promoting desiccation tolerance. However, evidence empirically supporting the necessity and/or sufficiency of helicity in mediating anhydrobiosis is lacking. Here, we demonstrate that the linker region of CAHS D, a desiccation-related IDP from the tardigrade Hypsibius exemplaris, that contains significant helical structure, is the protective portion of this protein. Perturbing the sequence composition and grammar of the linker region of CAHS D, through the insertion of helix-breaking prolines, modulating the identity of charged residues, or replacement of hydrophobic amino acids with serine or glycine residues results in variants with different degrees of helical structure. Importantly, correlation of protective capacity and helical content in variants generated through different helix perturbing modalities does not show as strong a trend, suggesting that while helicity is important, it is not the only property that makes a protein protective during desiccation. These results provide direct evidence for the decades-old theory that helicity of desiccation-related IDPs is linked to their anhydrobiotic capacity.

HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins

Current predictors of DNA-binding residues (DBRs) from protein sequences belong to two distinct groups, those trained on binding annotations extracted from structured protein-DNA complexes (structure-trained) vs. intrinsically disordered proteins (disorder-trained). We complete the first empirical analysis of predictive performance across the structure- and disorder-annotated proteins for a representative collection of ten predictors. Majority of the structure-trained tools perform well on the structure-annotated proteins while doing relatively poorly on the disorder-annotated proteins, and vice versa. Several methods make accurate predictions for the structure-annotated proteins or the disorder-annotated proteins, but none performs highly accurately for both annotation types. Moreover, most predictors make excessive cross-predictions for the disorder-annotated proteins, where residues that interact with non-DNA ligand types are predicted as DBRs. Motivated by these results, we design, validate and deploy an innovative meta-model, hybridDBRpred, that uses deep transformer network to combine predictions generated by three best current predictors. HybridDBRpred provides accurate predictions and low levels of cross-predictions across the two annotation types, and is statistically more accurate than each of the ten tools and baseline meta-predictors that rely on averaging and logistic regression. We deploy hybridDBRpred as a convenient web server at http://biomine.cs.vcu.edu/servers/hybridDBRpred/ and provide the corresponding source code at https://github.com/jianzhang-xynu/hybridDBRpred.

Fine structures of intrinsically disordered proteins

We report simulation studies of 33 single intrinsically disordered proteins (IDPs) using coarse-grained bead-spring models where interactions among different amino acids are introduced through a hydropathy matrix and additional screened Coulomb interaction for the charged amino acid beads. Our simulation studies of two different hydropathy scales (HPS1, HPS2) [Dignon et al., PLoS Comput. Biol. 14, e1005941 (2018); Tesei et al. Proc. Natl. Acad. Sci. U. S. A. 118, e2111696118 (2021)] and the comparison with the existing experimental data indicate an optimal interaction parameter ϵ = 0.1 and 0.2 kcal/mol for the HPS1 and HPS2 hydropathy scales. We use these best-fit parameters to investigate both the universal aspects as well as the fine structures of the individual IDPs by introducing additional characteristics. (i) First, we investigate the polymer-specific scaling relations of the IDPs in comparison to the universal scaling relations [Bair et al., J. Chem. Phys. 158, 204902 (2023)] for the homopolymers. By studying the scaled end-to-end distances ⟨RN2⟩/(2Lℓp) and the scaled transverse fluctuations l̃⊥2=⟨l⊥2⟩/L, we demonstrate that IDPs are broadly characterized with a Flory exponent of ν ≃ 0.56 with the conclusion that conformations of the IDPs interpolate between Gaussian and self-avoiding random walk chains. Then, we introduce (ii) Wilson charge index (W) that captures the essential features of charge interactions and distribution in the sequence space and (iii) a skewness index (S) that captures the finer shape variation of the gyration radii distributions as a function of the net charge per residue and charge asymmetry parameter. Finally, our study of the (iv) variation of ⟨Rg⟩ as a function of salt concentration provides another important metric to bring out finer characteristics of the IDPs, which may carry relevant information for the origin of life.

Engineered aptamers for molecular imaging

Molecular imaging, including quantification and molecular interaction studies, plays a crucial role in visualizing and analysing molecular events occurring within cells or organisms, thus facilitating the understanding of biological processes. Moreover, molecular imaging offers promising applications for early disease diagnosis and therapeutic evaluation. Aptamers are oligonucleotides that can recognize targets with a high affinity and specificity by folding themselves into various three-dimensional structures, thus serving as ideal molecular recognition elements in molecular imaging. This review summarizes the commonly employed aptamers in molecular imaging and outlines the prevalent design approaches for their applications. Furthermore, it highlights the successful application of aptamers to a wide range of targets and imaging modalities. Finally, the review concludes with a forward-looking perspective on future advancements in aptamer-based molecular imaging.

Modulating hierarchical self-assembly in thermoresponsive intrinsically disordered proteins through high-temperature incubation time

The cornerstone of structural biology is the unique relationship between protein sequence and the 3D structure at equilibrium. Although intrinsically disordered proteins (IDPs) do not fold into a specific 3D structure, breaking this paradigm, some IDPs exhibit large-scale organization, such as liquid-liquid phase separation. In such cases, the structural plasticity has the potential to form numerous self-assembled structures out of thermal equilibrium. Here, we report that high-temperature incubation time is a defining parameter for micro and nanoscale self-assembly of resilin-like IDPs. Interestingly, high-resolution scanning electron microscopy micrographs reveal that an extended incubation time leads to the formation of micron-size rods and ellipsoids that depend on the amino acid sequence. More surprisingly, a prolonged incubation time also induces amino acid composition-dependent formation of short-range nanoscale order, such as periodic lamellar nanostructures. We, therefore, suggest that regulating the period of high-temperature incubation, in the one-phase regime, can serve as a unique method of controlling the hierarchical self-assembly mechanism of structurally disordered proteins.

Heterochromatin organization and phase separation

The eukaryotic nucleus displays a variety of membraneless compartments with distinct biomolecular composition and specific cellular activities. Emerging evidence indicates that protein-based liquid-liquid phase separation (LLPS) plays an essential role in the formation and dynamic regulation of heterochromatin compartmentalization. This feature is especially conspicuous at the pericentric heterochromatin domains. In this review, we will describe our understanding of heterochromatin organization and LLPS. In addition, we will highlight the increasing importance of multivalent weak homo- and heteromolecular interactions in LLPS-mediated heterochromatin compartmentalization in the complex environment inside living cells.

RNA-Binding Proteins: A Role in Neurotoxicity?

Despite sustained efforts to treat neurodegenerative diseases, little is known at the molecular level to understand and generate novel therapeutic approaches for these malignancies. Therefore, it is not surprising that neurogenerative diseases are among the leading causes of death in the aged population. Neurons require sophisticated cellular mechanisms to maintain proper protein homeostasis. These cells are generally sensitive to loss of gene expression control at the post-transcriptional level. Post-translational control responds to signals that can arise from intracellular processes or environmental factors that can be regulated through RNA-binding proteins. These proteins recognize RNA through one or more RNA-binding domains and form ribonucleoproteins that are critically involved in the regulation of post-transcriptional processes from splicing to the regulation of association of the translation machinery allowing a relatively rapid and precise modulation of the transcriptome. Neurotoxicity is the result of the biological, chemical, or physical interaction of agents with an adverse effect on the structure and function of the central nervous system. The disruption of the proper levels or function of RBPs in neurons and glial cells triggers neurotoxic events that are linked to neurodegenerative diseases such as spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS), fragile X syndrome (FXS), and frontotemporal dementia (FTD) among many others. The connection between RBPs and neurodegenerative diseases opens a new landscape for potentially novel therapeutic targets for the intervention of these neurodegenerative pathologies. In this contribution, a summary of the recent findings of the molecular mechanisms involved in the plausible role of RBPs in RNA processing in neurodegenerative disease is discussed.

Functional unfoldomics: Roles of intrinsic disorder in protein (multi)functionality

Intrinsically disordered proteins (IDPs), which are functional proteins without stable tertiary structure, and hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) constitute prominent parts of all proteomes collectively known as unfoldomes. IDPs/IDRs exist as highly dynamic structural ensembles of rapidly interconverting conformations and are characterized by the exceptional structural heterogeneity, where their different parts are (dis)ordered to different degree, and their overall structure represents a complex mosaic of foldons, inducible foldons, inducible morphing foldons, non-foldons, semifoldons, and even unfoldons. Despite their lack of unique 3D structures, IDPs/IDRs play crucial roles in the control of various biological processes and the regulation of different cellular pathways and are commonly involved in recognition and signaling, indicating that the disorder-based functional repertoire is complementary to the functions of ordered proteins. Furthermore, IDPs/IDRs are frequently multifunctional, and this multifunctionality is defined by their structural flexibility and heterogeneity. Intrinsic disorder phenomenon is at the roots of the structure-function continuum model, where the structure continuum is defined by the presence of differently (dis)ordered regions, and the function continuum arises from the ability of all these differently (dis)ordered parts to have different functions. In their everyday life, IDPs/IDRs utilize a broad spectrum of interaction mechanisms thereby acting as interaction specialists. They are crucial for the biogenesis of numerous proteinaceous membrane-less organelles driven by the liquid-liquid phase separation. This review introduces functional unfoldomics by representing some aspects of the intrinsic disorder-based functionality.

Charge Distribution Patterns of IA3 Impact Conformational Expansion and Hydration Diffusivity of the Disordered Ensemble

IA3 is a 68 amino acid natural peptide/protein inhibitor of yeast aspartic proteinase A (YPRA) that is intrinsically disordered in solution with induced N-terminal helicity when in the protein complex with YPRA. Based on the intrinsically disordered protein (IDP) parameters of fractional net charge (FNC), net charge density per residue (NCPR), and charge patterning (κ), the two domains of IA3 are defined to occupy different domains within conformationally based subclasses of IDPs, thus making IA3 a bimodal domain IDP. Site-directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy and low-field Overhauser dynamic nuclear polarization (ODNP) spectroscopy results show that these two domains possess different degrees of compaction and hydration diffusivity behavior. This work suggests that SDSL EPR line shapes, analyzed in terms of their local tumbling volume (VL), provide insights into the compaction of the unstructured IDP ensemble in solution and that protein sequence and net charge distribution patterns within a conformational subclass can impact bound water hydration dynamics, thus possibly offering an alternative thermodynamic property that can encode conformational binding and behavior of IDPs and liquid-liquid phase separations.

Structure of Single-Chain Nanoparticles under Crowding Conditions: A Random Phase Approximation Approach

The conformation of poly(methyl methacrylate) (PMMA)-based single-chain nanoparticles (SCNPs) and their corresponding linear precursors in the presence of deuterated linear PMMA in deuterated dimethylformamide (DMF) solutions has been studied by small-angle neutron scattering (SANS). The SANS profiles were analyzed in terms of a three-component random phase approximation (RPA) model. The RPA approach described well the scattering profiles in dilute and crowded solutions. Considering all the contributions of the RPA leads to an accurate estimation of the single chain form factor parameters and the Flory-Huggins interaction parameter between PMMA and DMF. The value of the latter in the dilute regime indicates that the precursors and the SCNPs are in good solvent conditions, while in crowding conditions, the polymer becomes less soluble.

Identification of potential molecular mimicry in pathogen-host interactions

Pathogens have evolved sophisticated strategies to manipulate host signaling pathways, including the phenomenon of molecular mimicry, where pathogen-derived biomolecules imitate host biomolecules. In this study, we resurrected, updated, and optimized a sequence-based bioinformatics pipeline to identify potential molecular mimicry candidates between humans and 32 pathogenic species whose proteomes' 3D structure predictions were available at the start of this study. We observed considerable variation in the number of mimicry candidates across pathogenic species, with pathogenic bacteria exhibiting fewer candidates compared to fungi and protozoans. Further analysis revealed that the candidate mimicry regions were enriched in solvent-accessible regions, highlighting their potential functional relevance. We identified a total of 1,878 mimicked regions in 1,439 human proteins, and clustering analysis indicated diverse target proteins across pathogen species. The human proteins containing mimicked regions revealed significant associations between these proteins and various biological processes, with an emphasis on host extracellular matrix organization and cytoskeletal processes. However, immune-related proteins were underrepresented as targets of mimicry. Our findings provide insights into the broad range of host-pathogen interactions mediated by molecular mimicry and highlight potential targets for further investigation. This comprehensive analysis contributes to our understanding of the complex mechanisms employed by pathogens to subvert host defenses and we provide a resource to assist researchers in the development of novel therapeutic strategies.

CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions

Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions. The resulting CoMemMoRFPred method is available as an easy-to-use webserver at http://biomine.cs.vcu.edu/servers/CoMemMoRFPred. This tool will aid future studies of MemMoRFs in the context of exploring their abundance, cellular functions, and roles in pathologic phenomena.

IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models

Intrinsically disordered proteins (IDPs) and regions (IDRs) are a class of functionally important proteins and regions that lack stable three-dimensional structures under the native physiologic conditions. They participate in critical biological processes and thus are associated with the pathogenesis of many severe human diseases. Identifying the IDPs/IDRs and their functions will be helpful for a comprehensive understanding of protein structures and functions, and inform studies of rational drug design. Over the past decades, the exponential growth in the number of proteins with sequence information has deepened the gap between uncharacterized and annotated disordered sequences. Protein language models have recently demonstrated their powerful abilities to capture complex structural and functional information from the enormous quantity of unlabelled protein sequences, providing opportunities to apply protein language models to uncover the intrinsic disorders and their biological properties from the amino acid sequences. In this study, we proposed a computational predictor called IDP-LM for predicting intrinsic disorder and disorder functions by leveraging the pre-trained protein language models. IDP-LM takes the embeddings extracted from three pre-trained protein language models as the exclusive inputs, including ProtBERT, ProtT5 and a disorder specific language model (IDP-BERT). The ablation analysis shown that the IDP-BERT provided fine-grained feature representations of disorder, and the combination of three language models is the key to the performance improvement of IDP-LM. The evaluation results on independent test datasets demonstrated that the IDP-LM provided high-quality prediction results for intrinsic disorder and four common disordered functions.

Allosterically Driven Assembly of a Multisite Cage-Based [2]Semirotaxane

The assembly of a [2]semirotaxane from a half-dumbbell endowed with a pyrazine coordination site and a bis-Zn(II) porphyrin cage as a multisite ring is reported. The threading is allosterically driven by the coordination of silver(I) ions to the multiple binding sites of the cage linkers, as shown by NMR studies. Addition of chloride ions destabilizes [2]semirotaxane, leading to its disassembly into its cage and half-dumbbell components.

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein

The Atg12 protein in yeast is an indispensable polypeptide in the highly conserved ubiquitin-like conjugation system operating in the macroautophagy/autophagy pathway. Atg12 is covalently conjugated to Atg5 through the action of Atg7 and Atg10; the Atg12-Atg5 conjugate binds Atg16 to form an E3 ligase that functions in a separate conjugation pathway involving Atg8. Atg12 is comprised of a ubiquitin-like (UBL) domain preceded at the N terminus by an intrinsically disordered protein region (IDPR), a domain that comprises a major portion of the protein but remains elusive in its conformation and function. Here, we show that the IDPR in unconjugated Atg12 is positioned in proximity to the UBL domain, a configuration that is important for the functional structure of the protein. A major deletion in the IDPR disrupts intactness of the UBL domain at the unconjugated C terminus, and a mutation in the predicted α0 helix in the IDPR prevents Atg12 from binding to Atg7 and Atg10, which ultimately affects the protein function in the ubiquitin-like conjugation cascade. These findings provide evidence that the IDPR is an indispensable part of the Atg12 protein from yeast.

Fluctuation-Dominated Ligand Binding in Molten Globule Protein

A molten globule (MG) state is an intermediate state of protein observed during the unfolding of the native structure. In MG states, milk protein α-lactalbumin (aLA) binds to oleic acid (OLA). This MG-aLA-OLA complex, popularly known as XAMLET, performs cytotoxic activities against cancer cell lines. However, the microscopic understanding of ligand recognition ability in the MG state of the protein has not yet been explored. Motivated by this, we explore the binding of bovine aLA with OLA using all-atom molecular dynamics (MD) simulations. We find the binding mode between MG-aLA and OLA using the conformational thermodynamics method. We also estimate the binding free energy using the umbrella sampling (US) method for both the MG state and the neutral state. We find that the binding free energy obtained from US is comparable with earlier experimental results. We characterize the dihedral fluctuations as the ligand is liberated from the active site of the protein using steered MD. The low energy fluctuations occur near the ligand binding site, which eventually transfer toward the Ca2+-binding site as the ligand is taken away from the protein.

In silico design and identification of new peptides for mitigating hIAPP aggregation in type 2 diabetes

The aberrant misfolding and self-aggregation of human islet amyloid polypeptide (hIAPP or amylin) into cytotoxic aggregates are implicated in the pathogenesis of type 2 diabetes (T2D). Among various inhibitors, short peptides derived from the amyloidogenic regions of hIAPP have been employed as hIAPP aggregation inhibitors due to their low immunogenicity, biocompatibility, and high chemical diversity. Recently, hIAPP fragment HSSNN18-22 was identified as an amyloidogenic sequence and displayed higher antiproliferative activity to RIN-5F cells. Various hIAPP aggregation inhibitors have been designed by chemical modifications of the highly amyloidogenic sequence (NFGAIL) of hIAPP. In this work, a library of pentapeptides based on fragment HSSNN18-22 was designed and assessed for their efficacy in blocking hIAPP aggregation using an integrated computational screening approach. The binding free energy calculations by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method identified HSSQN and HSSNQ that bind to hIAPP monomer with a binding affinity of -21.25 ± 4.90 and -19.73 ± 3.10 kcal/mol, respectively, which is notably higher as compared to HSSNN (-11.90 ± 4.12 kcal/mol). The sampling of the non aggregation-prone helical conformation was notably increased from 23.5 ± 3.0 in the hIAPP monomer to 38.1 ± 3.6, and 33.8 ± 3.0% on the incorporation of HSSQN, and HSSNQ, respectively, which indicate reduced aggregation propensity of hIAPP monomer. The pentapeptides, HSSQN and HSSNQ, identified as hIAPP aggregation inhibitors in this work can be further conjugated with various metal chelating peptides to yield more efficacious and clinically relevant multifunctional modulators for targeting various pathological hallmarks of T2D.

Determining the Sequence Dependency of Self-Assembly of Elastin-Like Peptides Using Short Peptide Analogues with Shuffled Repetitive Sequences

Synthetic elastin-like peptides (ELPs) that possess characteristic tropoelastin-derived hydrophobic repetitive sequences, such as (VPGVG)n, exhibit thermoresponsive reversible self-assembly. Although their thermoresponsive properties have been well-studied, the sequence-dependent and structural requirements for self-assembly remain ambiguous. In particular, it is still unclear whether the amino acid sequences derived from tropoelastin are necessary for self-assembly. In this study, 11 sequence-shuffled ELP analogues based on (FPGVG)5, which is a previously developed short ELP (sELP), were designed to elucidate the sequence-dependent and structural requirements for their self-assembly. Among them, eight shuffled peptides exhibited self-assembling properties, whereas the other three peptides were difficult to dissolve in water. Structural analyses revealed that the structural characteristics of the three insoluble peptides were different from those of their thermoresponsive analogues. Furthermore, the secondary structures of the peptide analogues possessing the self-assembly abilities were different from each other. These results suggest that the potential for self-assembly and water solubility of sELPs depend on the primary structure in each repeated unit. Moreover, several shuffled analogues exhibited more potent self-assembling properties than the original (FPGVG)5, indicating that shorter ELPs can be obtained using their novel motifs as repetitive units. We also observed that the presence of Pro-Gly sequence in the repeating units was advantageous in terms of peptide solubility. Although further analysis will be necessary to elucidate the molecular mechanism underlying the self-assembly of these sELPs, this study provides insights into the relationship between the amino acid sequence and the self-assembling ability of the peptides for developing new sELPs for various applications.

Studies of proline conformational dynamics in IDPs by 13C-detected cross-correlated NMR relaxation

Intrinsically disordered proteins (IDPs) are significantly enriched in proline residues, which can populate specific local secondary structural elements called PPII helices, characterized by small packing densities. Proline is often thought to promote disorder, but it can participate in specific π·CH interactions with aromatic side chains resulting in reduced conformational flexibilities of the polypeptide. Differential local motional dynamics are relevant for the stabilization of preformed structural elements and can serve as nucleation sites for the establishment of long-range interactions. NMR experiments to probe the dynamics of proline ring systems would thus be highly desirable. Here we present a pulse scheme based on 13C detection to quantify dipole-dipole cross-correlated relaxation (CCR) rates at methylene CH2 groups in proline residues. Applying 13C-CON detection strategy provides exquisite spectral resolution allowing applications also to high molecular weight IDPs even in conditions approaching the physiological ones. The pulse scheme is illustrated with an application to the 220 amino acids long protein Osteopontin, an extracellular cytokine involved in inflammation and cancer progression, and a construct in which three proline-aromatic sequence patches have been mutated.

Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics

Before the controversial approval of humanized monoclonal antibody lecanemab, which binds to the soluble amyloid-β protofibrils, all the treatments available earlier, for Alzheimer's disease (AD) were symptomatic. The researchers are still struggling to find a breakthrough in AD therapeutic medicine, which is partially attributable to lack in understanding of the structural information associated with the intrinsically disordered proteins and amyloids. One of the major challenges in this area of research is to understand the structural diversity of intrinsically disordered proteins under in vitro conditions. Therefore, in this review, we have summarized the in vitro applications of biophysical methods, which are aimed to shed some light on the heterogeneity, pathogenicity, structures and mechanisms of the intrinsically disordered protein aggregates associated with proteinopathies including AD. This review will also rationalize some of the strategies in modulating disease-relevant pathogenic protein entities by small molecules using structural biology approaches and biophysical characterization. We have also highlighted tools and techniques to simulate the in vivo conditions for native and cytotoxic tau/amyloids assemblies, urge new chemical approaches to replicate tau/amyloids assemblies similar to those in vivo conditions, in addition to designing novel potential drugs.

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

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

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

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

The Unique N-Terminal Domain of Chlamydial Bactofilin Mediates Its Membrane Localization and Ring-Forming Properties

Chlamydia trachomatis is an obligate intracellular bacterial pathogen. In evolving to the intracellular niche, Chlamydia has reduced its genome size compared to other bacteria and, as a consequence, has a number of unique features. For example, Chlamydia engages the actin-like protein MreB, rather than the tubulin-like protein FtsZ, to direct peptidoglycan (PG) synthesis exclusively at the septum of cells undergoing polarized cell division. Interestingly, Chlamydia possesses another cytoskeletal element-a bactofilin ortholog, BacA. Recently, we reported BacA is a cell size-determining protein that forms dynamic membrane-associated ring structures in Chlamydia that have not been observed in other bacteria with bactofilins. Chlamydial BacA possesses a unique N-terminal domain, and we hypothesized this domain imparts the membrane-binding and ring-forming properties of BacA. We show that different truncations of the N terminus result in distinct phenotypes: removal of the first 50 amino acids (ΔN50) results in large ring structures at the membrane whereas removal of the first 81 amino acids (ΔN81) results in an inability to form filaments and rings and a loss of membrane association. Overexpression of the ΔN50 isoform altered cell size, similar to loss of BacA, suggesting that the dynamic properties of BacA are essential for the regulation of cell size. We further show that the region from amino acid 51 to 81 imparts membrane association as appending it to green fluorescent protein (GFP) resulted in the relocalization of GFP from the cytosol to the membrane. Overall, our findings suggest two important functions for the unique N-terminal domain of BacA and help explain its role as a cell size determinant. IMPORTANCE Bacteria use a variety of filament-forming cytoskeletal proteins to regulate and control various aspects of their physiology. For example, the tubulin-like FtsZ recruits division proteins to the septum whereas the actin-like MreB recruits peptidoglycan (PG) synthases to generate the cell wall in rod-shaped bacteria. Recently, a third class of cytoskeletal protein has been identified in bacteria-bactofilins. These proteins have been primarily linked to spatially localized PG synthesis. Interestingly, Chlamydia, an obligate intracellular bacterium, does not have PG in its cell wall and yet possesses a bactofilin ortholog. In this study, we characterize a unique N-terminal domain of chlamydial bactofilin and show that this domain controls two important functions that affect cell size: its ring-forming and membrane-associating properties.

Per Aspera ad Chaos: Vladimir Uversky's Odyssey through the Strange World of Intrinsically Disordered Proteins

Until the late 1990s, we believed that protein function required a unique, well-defined 3D structure encrypted in the amino acid sequence [...].

Assembly of the Tripartite and RNA Condensates of the Respiratory Syncytial Virus Factory Proteins In Vitro: Role of the Transcription Antiterminator M2-1

A wide variety of viruses replicate in liquid-like viral factories. Non-segmented negative stranded RNA viruses share a nucleoprotein (N) and a phosphoprotein (P) that together emerge as the main drivers of liquid-liquid phase separation. The respiratory syncytial virus includes the transcription antiterminator M_2-1, which binds RNA and maximizes RNA transcriptase processivity. We recapitulate the assembly mechanism of condensates of the three proteins and the role played by RNA. M_2-1 displays a strong propensity for condensation by itself and with RNA through the formation of electrostatically driven protein-RNA coacervates based on the amphiphilic behavior of M_2-1 and finely tuned by stoichiometry. M_2-1 incorporates into tripartite condensates with N and P, modulating their size through an interplay with P, where M_2-1 is both client and modulator. RNA is incorporated into the tripartite condensates adopting a heterogeneous distribution, reminiscent of the M_2-1-RNA IBAG granules within the viral factories. Ionic strength dependence indicates that M_2-1 behaves differently in the protein phase as opposed to the protein-RNA phase, in line with the subcompartmentalization observed in viral factories. This work dissects the biochemical grounds for the formation and fate of the RSV condensates in vitro and provides clues to interrogate the mechanism under the highly complex infection context.

Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins

We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 s compared to 1200 s for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1 = 0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

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

Fibril formation and ordering of disordered FUS LC driven by hydrophobic interactions

Biomolecular condensates, protein-rich and dynamic membrane-less organelles, play critical roles in a range of subcellular processes, including membrane trafficking and transcriptional regulation. However, aberrant phase transitions of intrinsically disordered proteins in biomolecular condensates can lead to the formation of irreversible fibrils and aggregates that are linked to neurodegenerative diseases. Despite the implications, the interactions underlying such transitions remain obscure. Here we investigate the role of hydrophobic interactions by studying the low-complexity domain of the disordered 'fused in sarcoma' (FUS) protein at the air/water interface. Using surface-specific microscopic and spectroscopic techniques, we find that a hydrophobic interface drives fibril formation and molecular ordering of FUS, resulting in solid-like film formation. This phase transition occurs at 600-fold lower FUS concentration than required for the canonical FUS low-complexity liquid droplet formation in bulk. These observations highlight the importance of hydrophobic effects for protein phase separation and suggest that interfacial properties drive distinct protein phase-separated structures.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

Phase separation propensity of the intrinsically disordered AB region of human RXRβ

RXRβ is one of three subtypes of human retinoid X receptor (RXR), a transcription factor that belongs to the nuclear receptor superfamily. Its expression can be detected in almost all tissues. In contrast to other subtypes - RXRα and RXRγ - RXRβ has the longest and unique N-terminal sequence called the AB region, which harbors a ligand-independent activation function. In contrast to the functional properties of this sequence, the molecular properties of the AB region of human RXRβ (AB_hRXRB) have not yet been characterized. Here, we present a systematic biochemical and biophysical analysis of recombinant AB_hRXRB, along with in silico examinations, which demonstrate that AB_hRXRB exhibits properties of a coil-like intrinsically disordered region. AB_hRXRB possesses a flexible structure that is able to adopt a more ordered conformation under the influence of different environmental factors. Interestingly, AB_hRXRB promotes the formation of liquid-liquid phase separation (LLPS), a phenomenon previously observed for the AB region of another human subtype of RXR - RXRγ (AB_hRXRG). Although both AB regions seem to be similar in terms of their ability to induce phase separation, they clearly differ in the sensitivity to factors driving and regulating LLPS. This distinct LLPS response to environmental factors driven by the unique amino acid compositions of AB_hRXRB and AB_hRXRG can be significant for the specific modulation of the transcriptional activation of target genes by different subtypes of RXR. Video Abstract.

A link between STK signalling and capsular polysaccharide synthesis in Streptococcus suis

Synthesis of capsular polysaccharide (CPS), an important virulence factor of pathogenic bacteria, is modulated by the CpsBCD phosphoregulatory system in Streptococcus. Serine/threonine kinases (STKs, e.g. Stk1) can also regulate CPS synthesis, but the underlying mechanisms are unclear. Here, we identify a protein (CcpS) that is phosphorylated by Stk1 and modulates the activity of phosphatase CpsB in Streptococcus suis, thus linking Stk1 to CPS synthesis. The crystal structure of CcpS shows an intrinsically disordered region at its N-terminus, including two threonine residues that are phosphorylated by Stk1. The activity of phosphatase CpsB is inhibited when bound to non-phosphorylated CcpS. Thus, CcpS modulates the activity of phosphatase CpsB thereby altering CpsD phosphorylation, which in turn modulates the expression of the Wzx-Wzy pathway and thus CPS production.

In-Silico Analysis Highlights the Existence in Members of Burkholderia cepacia Complex of a New Class of Adhesins Possessing Collagen-like Domains

Burkholderia cenocepacia is a multi-drug-resistant lung pathogen. This species synthesizes various virulence factors, among which cell-surface components (adhesins) are critical for establishing the contact with host cells. This work in the first part focuses on the current knowledge about the adhesion molecules described in this species. In the second part, through in silico approaches, we perform a comprehensive analysis of a group of unique bacterial proteins possessing collagen-like domains (CLDs) that are strikingly overrepresented in the Burkholderia species, representing a new putative class of adhesins. We identified 75 CLD-containing proteins in Burkholderia cepacia complex (Bcc) members (Bcc-CLPs). The phylogenetic analysis of Bcc-CLPs revealed the evolution of the core domain denominated "Bacterial collagen-like, middle region". Our analysis remarkably shows that these proteins are formed by extensive sets of compositionally biased residues located within intrinsically disordered regions (IDR). Here, we discuss how IDR functions may increase their efficiency as adhesion factors. Finally, we provided an analysis of a set of five homologs identified in B. cenocepacia J2315. Thus, we propose the existence in Bcc of a new type of adhesion factors distinct from the described collagen-like proteins (CLPs) found in Gram-positive bacteria.

SPEADI: Accelerated Analysis of IDP-Ion Interactions from MD-Trajectories

The disordered nature of Intrinsically Disordered Proteins (IDPs) makes their structural ensembles particularly susceptible to changes in chemical environmental conditions, often leading to an alteration of their normal functions. A Radial Distribution Function (RDF) is considered a standard method for characterizing the chemical environment surrounding particles during atomistic simulations, commonly averaged over an entire or part of a trajectory. Given their high structural variability, such averaged information might not be reliable for IDPs. We introduce the Time-Resolved Radial Distribution Function (TRRDF), implemented in our open-source Python package SPEADI, which is able to characterize dynamic environments around IDPs. We use SPEADI to characterize the dynamic distribution of ions around the IDPs Alpha-Synuclein (AS) and Humanin (HN) from Molecular Dynamics (MD) simulations, and some of their selected mutants, showing that local ion-residue interactions play an important role in the structures and behaviors of IDPs.

TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning

Disordered flexible linkers (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs, leading to a high falsepositive rate (FPR) and low prediction accuracy. Previous studies have shown that DFLs are extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [P(D) > 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in IDRs or prediction of DFLs in entire proteins) showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed at http://bliulab.net/TransDFL/.