Net charge per residue modulates conformational ensembles of intrinsically disordered proteins

Evolution of Virus-like Features and Intrinsically Disordered Regions in Retrotransposon-derived Mammalian Genes

Several mammalian genes have originated from the domestication of retrotransposons, selfish mobile elements related to retroviruses. Some of the proteins encoded by these genes have maintained virus-like features; including self-processing, capsid structure formation, and the generation of different isoforms through -1 programmed ribosomal frameshifting. Using quantitative approaches in molecular evolution and biophysical analyses, we studied 28 retrotransposon-derived genes, with a focus on the evolution of virus-like features. By analyzing the rate of synonymous substitutions, we show that the -1 programmed ribosomal frameshifting mechanism in three of these genes (PEG10, PNMA3, and PNMA5) is conserved across mammals and originates alternative proteins. These genes were targets of positive selection in primates, and one of the positively selected sites affects a B-cell epitope on the spike domain of the PNMA5 capsid, a finding reminiscent of observations in infectious viruses. More generally, we found that retrotransposon-derived proteins vary in their intrinsically disordered region content and this is directly associated with their evolutionary rates. Most positively selected sites in these proteins are located in intrinsically disordered regions and some of them impact protein posttranslational modifications, such as autocleavage and phosphorylation. Detailed analyses of the biophysical properties of intrinsically disordered regions showed that positive selection preferentially targeted regions with lower conformational entropy. Furthermore, positive selection introduces variation in binary sequence patterns across orthologues, as well as in chain compaction. Our results shed light on the evolutionary trajectories of a unique class of mammalian genes and suggest a novel approach to study how intrinsically disordered region biophysical characteristics are affected by evolution.

Effects of charge asymmetry on the liquid-liquid phase separation of polyampholytes and their condensate properties

Liquid-liquid phase separation (LLPS) is the mechanism underlying the formation of bio-molecular condensates which are important compartments regulating intra- and extra-cellular functions. Electrostatic interactions are some of the important driving forces of the LLPS behaviors of biomolecules. However, the understanding of the electrostatic interactions is still limited, especially in the mixtures of biomolecules with different charge patterns. Here, we focus on the electrostatic interactions in mixtures of charge-asymmetric and charge-symmetric polyampholytes and their roles in the phase separation behaviors. We build charge-asymmetric and charge-symmetric model proteins consisting of both glutamic acid (E, negatively charged) and lysine (K, positively charged), i.e. polyampholytes of E35K15 (charge asymmetric) and E25K25 (charge symmetric). Pure E25K25 can undergo LLPS. To investigate the effects of charge-asymmetric polyampholytes on the mixtures of E25K25/E35K15, we perform coarse-grained simulations to determine their phase separation. The charge-asymmetric polyampholyte E35K15 is resistant to the LLPS of the mixtures of E25K25/E35K15. The condensate density decreases with the molar fraction of E35K15 increasing to 0.4, and no LLPS occurs at the molar fraction of 0.5 and above. This can be attributed to the electrostatic repulsion between the negatively charged E35K15 polymers. We further investigate the effects of charge asymmetry on the conformations and properties of the condensates. The E35K15 polymers in the condensates exhibit a more collapsed state as the molar fraction of E35K15 increases. However, the conformation of E25K25 polymers changes slightly across different condensates. The surface tensions of condensates decline with the increase of the molar fraction of E35K15 polymers, while the diffusivity of polymers in the condensed phases is enhanced. This work elucidates the role of charge-asymmetric polyampholytes in determining the LLPS behaviours of binary mixtures of charge-symmetric and charge-asymmetric proteins as well as the properties of condensed phases.

Modulation of biophysical properties of nucleocapsid protein in the mutant spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also observe functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

The Streptococcus phage protein paratox is an intrinsically disordered protein

The bacteriophage protein paratox (Prx) blocks quorum sensing in its streptococcal host by directly binding the signal receptor and transcription factor ComR. This reduces the ability of Streptococcus to uptake environmental DNA and protects phage DNA from damage by recombination. Past work characterizing the Prx:ComR molecular interaction revealed that paratox adopts a well-ordered globular fold when bound to ComR. However, solution-state biophysical measurements suggested that Prx may be conformationally dynamic. To address this discrepancy, we investigated the stability and dynamic properties of Prx in solution using circular dichroism, nuclear magnetic resonance, and several fluorescence-based protein folding assays. Our work shows that under dilute buffer conditions Prx is intrinsically disordered. We also show that the addition of kosmotropic salts or protein stabilizing osmolytes induces Prx folding. However, the solute stabilized fold is different from the conformation Prx adopts when it is bound to ComR. Furthermore, we have characterized Prx folding thermodynamics and folding kinetics through steady-state fluorescence and stopped flow kinetic measurements. Our results show that Prx is a highly dynamic protein in dilute solution, folding and refolding within the 10 ms timescale. Overall, our results demonstrate that the streptococcal phage protein Prx is an intrinsically disordered protein in a two-state equilibrium with a solute-stabilized folded form. Furthermore, the solute-stabilized fold is likely the predominant form of Prx in a solute-crowded bacterial cell. Finally, our work suggests that Prx binds and inhibits ComR, and thus quorum sensing in Streptococcus, by a combination of conformational selection and induced-fit binding mechanisms.

Hydrodynamic Radii of Intrinsically Disordered Proteins: Fast Prediction by Minimum Dissipation Approximation and Experimental Validation

The diffusion coefficients of globular and fully unfolded proteins can be predicted with high accuracy solely from their mass or chain length. However, this approach fails for intrinsically disordered proteins (IDPs) containing structural domains. We propose a rapid predictive methodology for estimating the diffusion coefficients of IDPs. The methodology uses accelerated conformational sampling based on self-avoiding random walks and includes hydrodynamic interactions between coarse-grained protein subunits, modeled using the generalized Rotne-Prager-Yamakawa approximation. To estimate the hydrodynamic radius, we rely on the minimum dissipation approximation recently introduced by Cichocki et al. Using a large set of experimentally measured hydrodynamic radii of IDPs over a wide range of chain lengths and domain contributions, we demonstrate that our predictions are more accurate than the Kirkwood approximation and phenomenological approaches. Our technique may prove to be valuable in predicting the hydrodynamic properties of both fully unstructured and multidomain disordered proteins.

Quantum Defect Sensitization via Phase-Changing Supercharged Antibody Fragments

Quantum defects in single-walled carbon nanotubes promote exciton localization, which enables potential applications in biodevices and quantum light sources. However, the effects of local electric fields on the emissive energy states of quantum defects and how they can be controlled are unexplored. Here, we investigate quantum defect sensitization by engineering an intrinsically disordered protein to undergo a phase change at a quantum defect site. We designed a supercharged single-chain antibody fragment (scFv) to enable a full ligand-induced folding transition from an intrinsically disordered state to a compact folded state in the presence of a cytokine. The supercharged scFv was conjugated to a quantum defect to induce a substantial local electric change upon ligand binding. Employing the detection of a proinflammatory biomarker, interleukin-6, as a representative model system, supercharged scFv-coupled quantum defects exhibited robust fluorescence wavelength shifts concomitant with the protein folding transition. Quantum chemical simulations suggest that the quantum defects amplify the optical response to the localization of charges produced upon the antigen-induced folding of the proteins, which is difficult to achieve in unmodified nanotubes. These findings portend new approaches to modulate quantum defect emission for biomarker sensing and protein biophysics and to engineer proteins to modulate binding signal transduction.

Transferable deep generative modeling of intrinsically disordered protein conformations

Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.

Lipids and proteins: Insights into the dynamics of assembly, recognition, condensate formation. What is still missing?

Lipid membranes and proteins, which are part of us throughout our lives, have been studied for decades. However, every year, new discoveries show how little we know about them. In a reader-friendly manner for people not involved in the field, this paper tries to serve as a bridge between physicists and biologists and new young researchers diving into the field to show its relevance, pointing out just some of the plethora of lines of research yet to be unraveled. It illustrates how new ways, from experimental to theoretical approaches, are needed in order to understand the structures and interactions that take place in a single lipid, protein, or multicomponent system, as we are still only scratching the surface.

How hydrophobicity, side chains, and salt affect the dimensions of disordered proteins

Despite the generally accepted role of the hydrophobic effect as the driving force for folding, many intrinsically disordered proteins (IDPs), including those with hydrophobic content typical of foldable proteins, behave nearly as self-avoiding random walks (SARWs) under physiological conditions. Here, we tested how temperature and ionic conditions influence the dimensions of the N-terminal domain of pertactin (PNt), an IDP with an amino acid composition typical of folded proteins. While PNt contracts somewhat with temperature, it nevertheless remains expanded over 10-58°C, with a Flory exponent, ν, >0.50. Both low and high ionic strength also produce contraction in PNt, but this contraction is mitigated by reducing charge segregation. With 46% glycine and low hydrophobicity, the reduced form of snow flea anti-freeze protein (red-sfAFP) is unaffected by temperature and ionic strength and persists as a near-SARW, ν ~ 0.54, arguing that the thermal contraction of PNt is due to stronger interactions between hydrophobic side chains. Additionally, red-sfAFP is a proxy for the polypeptide backbone, which has been thought to collapse in water. Increasing the glycine segregation in red-sfAFP had minimal effect on ν. Water remained a good solvent even with 21 consecutive glycine residues (ν > 0.5), and red-sfAFP variants lacked stable backbone hydrogen bonds according to hydrogen exchange. Similarly, changing glycine segregation has little impact on ν in other glycine-rich proteins. These findings underscore the generality that many disordered states can be expanded and unstructured, and that the hydrophobic effect alone is insufficient to drive significant chain collapse for typical protein sequences.

HYPK: A marginally disordered protein sensitive to charge decoration

Intrinsically disordered proteins (IDPs) that lie close to the empirical boundary separating IDPs and folded proteins in Uversky's charge-hydropathy plot may behave as "marginal IDPs" and sensitively switch conformation upon changes in environment (temperature, crowding, and charge screening), sequence, or both. In our search for such a marginal IDP, we selected Huntingtin-interacting protein K (HYPK) near that boundary as a candidate; PKIα, also near that boundary, has lower secondary structure propensity; and Crk1, just across the boundary on the folded side, has higher secondary structure propensity. We used a qualitative Förster resonance energy transfer-based assay together with circular dichroism to simultaneously probe global and local conformation. HYPK shows several unique features indicating marginality: a cooperative transition in end-to-end distance with temperature, like Crk1 and folded proteins, but unlike PKIα; enhanced secondary structure upon crowding, in contrast to Crk1 and PKIα; and a cross-over from salt-induced expansion to compaction at high temperature, likely due to a structure-to-disorder transition not seen in Crk1 and PKIα. We then tested HYPK's sensitivity to charge patterning by designing charge-flipped variants including two specific sequences with identical amino acid composition that markedly differ in their predicted size and response to salt. The experimentally observed trends, also including mutants of PKIα, verify the predictions from sequence charge decoration metrics. Marginal proteins like HYPK show features of both folded and disordered proteins that make them sensitive to physicochemical perturbations and structural control by charge patterning.

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.

Protein structure-function continuum model: Emerging nexuses between specificity, evolution, and structure

The rationale for replacing the old binary of structure-function with the trinity of structure, disorder, and function has gained considerable ground in recent years. A continuum model based on the expanded form of the existing paradigm can now subsume importance of both conformational flexibility and intrinsic disorder in protein function. The disorder is actually critical for understanding the protein-protein interactions in many regulatory processes, formation of membrane-less organelles, and our revised notions of specificity as amply illustrated by moonlighting proteins. While its importance in formation of amyloids and function of prions is often discussed, the roles of intrinsic disorder in infectious diseases and protein function under extreme conditions are also becoming clear. This review is an attempt to discuss how our current understanding of protein function, specificity, and evolution fit better with the continuum model. This integration of structure and disorder under a single model may bring greater clarity in our continuing quest for understanding proteins and molecular mechanisms of their functionality.

Direct prediction of intrinsically disordered protein conformational properties from sequence

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

The molecular basis for cellular function of intrinsically disordered protein regions

Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.

Transferable deep generative modeling of intrinsically disordered protein conformations

Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.

Mesophiles vs. Thermophiles: Untangling the Hot Mess of Intrinsically Disordered Proteins and Growth Temperature of Bacteria

The dynamic structures and varying functions of intrinsically disordered proteins (IDPs) have made them fascinating subjects in molecular biology. Investigating IDP abundance in different bacterial species is crucial for understanding adaptive strategies in diverse environments. Notably, thermophilic bacteria have lower IDP abundance than mesophiles, and a negative correlation with optimal growth temperature (OGT) has been observed. However, the factors driving these trends are yet to be fully understood. We examined the types of IDPs present in both mesophiles and thermophiles alongside those unique to just mesophiles. The shared group of IDPs exhibits similar disorder levels in the two groups of species, suggesting that certain IDPs unique to mesophiles may contribute to the observed decrease in IDP abundance as OGT increases. Subsequently, we used quasi-independent contrasts to explore the relationship between OGT and IDP abundance evolution. Interestingly, we found no significant relationship between OGT and IDP abundance contrasts, suggesting that the evolution of lower IDP abundance in thermophiles may not be solely linked to OGT. This study provides a foundation for future research into the intricate relationship between IDP evolution and environmental adaptation. Our findings support further research on the adaptive significance of intrinsic disorder in bacterial species.

Conformational ensembles of the human intrinsically disordered proteome

Intrinsically disordered proteins and regions (collectively, IDRs) are pervasive across proteomes in all kingdoms of life, help to shape biological functions and are involved in numerous diseases. IDRs populate a diverse set of transiently formed structures and defy conventional sequence-structure-function relationships1. Developments in protein science have made it possible to predict the three-dimensional structures of folded proteins at the proteome scale2. By contrast, there is a lack of knowledge about the conformational properties of IDRs, partly because the sequences of disordered proteins are poorly conserved and also because only a few of these proteins have been characterized experimentally. The inability to predict structural properties of IDRs across the proteome has limited our understanding of the functional roles of IDRs and how evolution shapes them. As a supplement to previous structural studies of individual IDRs3, we developed an efficient molecular model to generate conformational ensembles of IDRs and thereby to predict their conformational properties from sequences4,5. Here we use this model to simulate nearly all of the IDRs in the human proteome. Examining conformational ensembles of 28,058 IDRs, we show how chain compaction is correlated with cellular function and localization. We provide insights into how sequence features relate to chain compaction and, using a machine-learning model trained on our simulation data, show the conservation of conformational properties across orthologues. Our results recapitulate observations from previous studies of individual protein systems and exemplify how to link-at the proteome scale-conformational ensembles with cellular function and localization, amino acid sequence, evolutionary conservation and disease variants. Our freely available database of conformational properties will encourage further experimental investigation and enable the generation of hypotheses about the biological roles and evolution of IDRs.

Design of functional intrinsically disordered proteins

Many proteins do not fold into a fixed three-dimensional structure, but rather function in a highly disordered state. These intrinsically disordered proteins pose a unique challenge to protein engineering and design: How can proteins be designed de novo if not by tailoring their structure? Here, we will review the nascent field of design of intrinsically disordered proteins with focus on applications in biotechnology and medicine. The design goals should not necessarily be the same as for de novo design of folded proteins as disordered proteins have unique functional strengths and limitations. We focus on functions where intrinsically disordered proteins are uniquely suited including disordered linkers, desiccation chaperones, sensors of the chemical environment, delivery of pharmaceuticals, and constituents of biomolecular condensates. Design of functional intrinsically disordered proteins relies on a combination of computational tools and heuristics gleaned from sequence-function studies. There are few cases where intrinsically disordered proteins have made it into industrial applications. However, we argue that disordered proteins can perform many roles currently performed by organic polymers, and that these proteins might be more designable due to their modularity.

Sequence-Dependent Conformational Transitions of Disordered Proteins During Condensation

Intrinsically disordered proteins (IDPs) can form biomolecular condensates through phase separation. It is recognized that the conformation of IDPs in the dense and dilute phases as well as at the interfaces of condensates can critically impact the resulting properties associated with their functionality. However, a comprehensive understanding of the conformational transitions of IDPs during condensation remains elusive. In this study, we employ a coarse-grained polyampholyte model, comprising an equal number of oppositely charged residues-glutamic acid and lysine-whereby conformations and phase behavior can be readily tuned by altering the protein sequence. By manipulating the sequence patterns from perfectly alternating to block-like, we obtain chains with ideal-like conformations to semi-compact structures in the dilute phase, while in the dense phase, the chain conformation is approximately that of an ideal chain, irrespective of the protein sequence. By performing simulations at different concentrations, we find that the chains assemble from the dilute phase through small oligomeric clusters to the dense phase, accompanied by a gradual swelling of the individual chains. We further demonstrate that these findings are applicable to several naturally occurring proteins involved in the formation of biological condensates. Concurrently, we delve deeper into the chain conformations within the condensate, revealing that chains at the interface show a strong sequence dependence, but remain more collapsed than those in the bulk-like dense phase. This study addresses critical gaps in our knowledge of IDP conformations within condensates as a function of protein sequence.

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.

Active learning of the thermodynamics-dynamics trade-off in protein condensates

Phase-separated biomolecular condensates exhibit a wide range of dynamic properties, which depend on the sequences of the constituent proteins and RNAs. However, it is unclear to what extent condensate dynamics can be tuned without also changing the thermodynamic properties that govern phase separation. Using coarse-grained simulations of intrinsically disordered proteins, we show that the dynamics and thermodynamics of homopolymer condensates are strongly correlated, with increased condensate stability being coincident with low mobilities and high viscosities. We then apply an "active learning" strategy to identify heteropolymer sequences that break this correlation. This data-driven approach and accompanying analysis reveal how heterogeneous amino acid compositions and nonuniform sequence patterning map to a range of independently tunable dynamic and thermodynamic properties of biomolecular condensates. Our results highlight key molecular determinants governing the physical properties of biomolecular condensates and establish design rules for the development of stimuli-responsive biomaterials.

Using NMR diffusion data to validate MD models of disordered proteins: Test case of N-terminal tail of histone H4

MD simulations can provide uniquely detailed models of intrinsically disordered proteins (IDPs). However, these models need careful experimental validation. The coefficient of translational diffusion Dtr, measurable by pulsed field gradient NMR, offers a potentially useful piece of experimental information related to the compactness of the IDP's conformational ensemble. Here, we investigate, both experimentally and via the MD modeling, the translational diffusion of a 25-residue N-terminal fragment from histone H4 (N-H4). We found that the predicted values of Dtr, as obtained from mean-square displacement of the peptide in the MD simulations, are largely determined by the viscosity of the MD water (which has been reinvestigated as a part of our study). Beyond that, our analysis of the diffusion data indicates that MD simulations of N-H4 in the TIP4P-Ew water give rise to an overly compact conformational ensemble for this peptide. In contrast, TIP4P-D and OPC simulations produce the ensembles that are consistent with the experimental Dtr result. These observations are supported by the analyses of the 15N spin relaxation rates. We also tested a number of empirical methods to predict Dtr based on IDP's coordinates extracted from the MD snapshots. In particular, we show that the popular approach involving the program HYDROPRO can produce misleading results. This happens because HYDROPRO is not intended to predict the diffusion properties of highly flexible biopolymers such as IDPs. Likewise, recent empirical schemes that exploit the relationship between the small-angle x-ray scattering-informed conformational ensembles of IDPs and the respective experimental Dtr values also prove to be problematic. In this sense, the first-principle calculations of Dtr from the MD simulations, such as demonstrated in this work, should provide a useful benchmark for future efforts in this area.

Machine-learning-based methods to generate conformational ensembles of disordered proteins

Intrinsically disordered proteins are characterized by a conformational ensemble. While computational approaches such as molecular dynamics simulations have been used to generate such ensembles, their computational costs can be prohibitive. An alternative approach is to learn from data and train machine-learning models to generate conformational ensembles of disordered proteins. This has been a relatively unexplored approach, and in this work we demonstrate a proof-of-principle approach to do so. Specifically, we devised a two-stage computational pipeline: in the first stage, we employed supervised machine-learning models to predict ensemble-derived two-dimensional (2D) properties of a sequence, given the conformational ensemble of a closely related sequence. In the second stage, we used denoising diffusion models to generate three-dimensional (3D) coarse-grained conformational ensembles, given the two-dimensional predictions outputted by the first stage. We trained our models on a data set of coarse-grained molecular dynamics simulations of thousands of rationally designed synthetic sequences. The accuracy of our 2D and 3D predictions was validated across multiple metrics, and our work demonstrates the applicability of machine-learning techniques to predicting higher-dimensional properties of disordered proteins.

Electrostatics and hydrophobicity in the dynamics of intrinsically disordered proteins

Internal friction is a major contribution to the dynamics of intrinsically disordered proteins (IDPs). Yet, the molecular origin of internal friction has so far been elusive. Here, we investigate whether attractive electrostatic interactions in IDPs modulate internal friction differently than the hydrophobic effect. To this end, we used nanosecond fluorescence correlation spectroscopy (nsFCS) and single-molecule Förster resonance energy transfer (FRET) to quantify the conformation and dynamics of the disordered DNA-binding domains Myc, Max and Mad at different salt concentrations. We find that internal friction effects are stronger when the chain is compacted by electrostatic attractions compared to the hydrophobic effect. Although the effect is moderate, the results show that the heteropolymeric nature of IDPs is reflected in their dynamics.

Transcription factor condensates and signaling driven transcription

Transcription Factor (TF) condensates are a heterogenous mix of RNA, DNA, and multiple co-factor proteins capable of modulating the transcriptional response of the cell. The dynamic nature and the spatial location of TF-condensates in the 3D nuclear space is believed to provide a fast response, which is on the same pace as the signaling cascade and yet ever-so-specific in the crowded environment of the nucleus. However, the current understanding of how TF-condensates can achieve these feet so quickly and efficiently is still unclear. In this review, we draw parallels with other protein condensates and share our speculations on how the nucleus uses these TF-condensates to achieve high transcriptional specificity and fidelity. We discuss the various constituents of TF-condensates, their properties, and the known and unknown functions of TF-condensates with a particular focus on steroid signaling-induced transcriptional programs.

Intrinsically disordered regions are poised to act as sensors of cellular chemistry

Intrinsically disordered proteins and protein regions (IDRs) are abundant in eukaryotic proteomes and play a wide variety of essential roles. Instead of folding into a stable structure, IDRs exist in an ensemble of interconverting conformations whose structure is biased by sequence-dependent interactions. The absence of a stable 3D structure, combined with high solvent accessibility, means that IDR conformational biases are inherently sensitive to changes in their environment. Here, we argue that IDRs are ideally poised to act as sensors and actuators of cellular physicochemistry. We review the physical principles that underlie IDR sensitivity, the molecular mechanisms that translate this sensitivity to function, and recent studies where environmental sensing by IDRs may play a key role in their downstream function.

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.

Sequence-ensemble-function relationships for disordered proteins in live cells

Intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life and play a variety of essential cellular roles. IDRs exist in a collection of structurally distinct conformers known as an ensemble. An IDR's amino acid sequence determines its ensemble, which in turn can play an important role in dictating molecular function. Yet a clear link connecting IDR sequence, its ensemble properties, and its molecular function in living cells has not been directly established. Here, we set out to test this sequence-ensemble-function paradigm using a novel computational method (GOOSE) that enables the rational design of libraries of IDRs by systematically varying specific sequence properties. Using ensemble FRET, we measured the ensemble dimensions of a library of rationally designed IDRs in human-derived cell lines, revealing how IDR sequence influences ensemble dimensions in situ. Furthermore, we show that the interplay between sequence and ensemble can tune an IDR's ability to sense changes in cell volume - a de novo molecular function for these synthetic sequences. Our results establish biophysical rules for intracellular sequence-ensemble relationships, enable a new route for understanding how IDR sequences map to function in live cells, and set the ground for the design of synthetic IDRs with de novo function.

Sequence-ensemble-function relationships for disordered proteins in live cells

Intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life and play a variety of essential cellular roles. IDRs exist in a collection of structurally distinct conformers known as an ensemble. IDR amino acid sequence determines its ensemble, which in turn can play an important role in dictating molecular function. Yet a clear link connecting IDR sequence, its ensemble properties, and its molecular function in living cells has not been systematically established. Here, we set out to test this sequence-ensemble-function paradigm using a novel computational method (GOOSE) that enables the rational design of libraries of IDRs by systematically varying specific sequence properties. Using ensemble FRET, we measured the ensemble dimensions of a library of rationally designed IDRs in human-derived cell lines, revealing how IDR sequence influences ensemble dimensions in situ. Furthermore, we show that the interplay between sequence and ensemble can tune an IDR's ability to sense changes in cell volume - a de novomolecular function for these synthetic sequences. Our results establish biophysical rules for intracellular sequence-ensemble relationships, enable a new route for understanding how IDR sequences map to function in live cells, and set the ground for the design of synthetic IDRs with de novo function.

Multi-scale molecular simulation of random peptide phase separation and its extended-to-compact structure transition driven by hydrophobic interactions

Intrinsically disordered proteins (IDPs) often undergo liquid-liquid phase separation (LLPS) and form membraneless organelles or protein condensates. One of the core problems is how do electrostatic repulsion and hydrophobic interactions in peptides regulate the phase separation process? To answer this question, this study uses random peptides composed of positively charged arginine (Arg, R) and hydrophobic isoleucine (Ile, I) as the model systems, and conduct large-scale simulations using all atom and coarse-grained model multi-scale simulation methods. In this article, we investigate the phase separation of different sequences using a coarse-grained model. It is found that the stronger the electrostatic repulsion in the system, the more extended the single-chain structure, and the more likely the system forms a low-density homogeneous phase. In contrast, the stronger the hydrophobic effect of the system, the more compact the single-chain structure, the easier phase separation, and the higher the critical temperature of phase separation. Overall, by taking the random polypeptides composed of two types of amino acid residues as model systems, this study discusses the relationship between the protein sequence and phase behaviour, and provides theoretical insights into the interactions within or between proteins. It is expected to provide essential physical information for the sequence design of functional IDPs, as well as data to support the diagnosis and treatment of the LLPS-associated diseases.

Disorder and amino acid composition in proteins: their potential role in the adaptation of extracellular pilins to the acidic media, where Acidithiobacillus thiooxidans grows

There are few biophysical studies or structural characterizations of the type IV pilin system of extremophile bacteria, such as the acidophilic Acidithiobacillus thiooxidans. We set out to analyze their pili-comprising proteins, pilins, because these extracellular proteins are in constant interaction with protons of the acidic medium in which At. thiooxidans grows. We used the web server Operon Mapper to analyze and identify the cluster codified by the minor pilin of At. thiooxidans. In addition, we carried an in-silico characterization of such pilins using the VL-XT algorithm of PONDR® server. Our results showed that structural disorder prevails more in pilins of At. thiooxidans than in non-acidophilic bacteria. Further computational characterization showed that the pilins of At. thiooxidans are significantly enriched in hydroxy (serine and threonine) and amide (glutamine and asparagine) residues, and significantly reduced in charged residues (aspartic acid, glutamic acid, arginine and lysine). Similar results were obtained when comparing pilins from other Acidithiobacillus and other acidophilic bacteria from another genus versus neutrophilic bacteria, suggesting that these properties are intrinsic to pilins from acidic environments, most likely by maintaining solubility and stability in harsh conditions. These results give guidelines for the application of extracellular proteins of acidophiles in protein engineering.

Pervasive, conserved secondary structure in highly charged protein regions

Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder-high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length-are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.

Defining the condensate landscape of fusion oncoproteins

Fusion oncoproteins (FOs) arise from chromosomal translocations in ~17% of cancers and are often oncogenic drivers. Although some FOs can promote oncogenesis by undergoing liquid-liquid phase separation (LLPS) to form aberrant biomolecular condensates, the generality of this phenomenon is unknown. We explored this question by testing 166 FOs in HeLa cells and found that 58% formed condensates. The condensate-forming FOs displayed physicochemical features distinct from those of condensate-negative FOs and segregated into distinct feature-based groups that aligned with their sub-cellular localization and biological function. Using Machine Learning, we developed a predictor of FO condensation behavior, and discovered that 67% of ~3000 additional FOs likely form condensates, with 35% of those predicted to function by altering gene expression. 47% of the predicted condensate-negative FOs were associated with cell signaling functions, suggesting a functional dichotomy between condensate-positive and -negative FOs. Our Datasets and reagents are rich resources to interrogate FO condensation in the future.

Conformational Manifold Sampled by Two Short Linear Motif Segments Probed by Circular Dichroism, Vibrational, and Nuclear Magnetic Resonance Spectroscopy

Disordered protein segments called short linear motifs (SLiM) serve as recognition sites for a variety of biological processes and act as targeting signals, modification, and ligand binding sites. While SLiMs do not adopt one of the known regular secondary structures, the conformational distribution might still reflect the structural propensities of their amino acid residues and possible interactions between them. In the past, conformational analyses of short peptides provided compelling evidence for the notion that individual residues are less conformationally flexible than locally expected for a random coil. Here, we combined various spectroscopies (NMR, IR, vibrational, and UV circular dichroism) to determine the Ramachandran plots of two SLiM motifs, i.e., GRRDSG and GRRTSG. They are two representatives of RxxS motifs that are capable of being phosphorylated by protein kinase A, an enzyme that plays a fundamental role in a variety of biological processes. Our results reveal that the nearest and non-nearest interactions between residues cause redistributions between polyproline II and β-strand basins while concomitantly stabilizing extended relative to turn-forming and helical structures. They also cause shifts in basin positions. With increasing temperature, β-strand populations become more populated at the expense of polyproline II. While molecular dynamics simulations with Amber ff14SB and CHARMM 36m force fields indicate residue-residue interactions, they do not account for the observed structural changes.

Flanking regions, amyloid cores, and polymorphism: the potential interplay underlying structural diversity

The β-sheet-rich amyloid core is the defining feature of protein aggregates associated with neurodegenerative disorders. Recent investigations have revealed that there exist multiple examples of the same protein, with the same sequence, forming a variety of amyloid cores with distinct structural characteristics. These structural variants, termed as polymorphs, are hypothesized to influence the pathological profile and the progression of different neurodegenerative diseases, giving rise to unique phenotypic differences. Thus, identifying the origin and properties of these structural variants remain a focus of studies, as a preliminary step in the development of therapeutic strategies. Here, we review the potential role of the flanking regions of amyloid cores in inducing polymorphism. These regions, adjacent to the amyloid cores, show a preponderance for being structurally disordered, imbuing them with functional promiscuity. The dynamic nature of the flanking regions can then manifest in the form of conformational polymorphism of the aggregates. We take a closer look at the sequences flanking the amyloid cores, followed by a review of the polymorphic aggregates of the well-characterized proteins amyloid-β, α-synuclein, Tau, and TDP-43. We also consider different factors that can potentially influence aggregate structure and how these regions can be viewed as novel targets for therapeutic strategies by utilizing their unique structural properties.

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

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

SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins

Conformational heterogeneity is a defining hallmark of intrinsically disordered proteins and protein regions (IDRs). The functions of IDRs and the emergent cellular phenotypes they control are associated with sequence-specific conformational ensembles. Simulations of conformational ensembles that are based on atomistic and coarse-grained models are routinely used to uncover the sequence-specific interactions that may contribute to IDR functions. These simulations are performed either independently or in conjunction with data from experiments. Functionally relevant features of IDRs can span a range of length scales. Extracting these features requires analysis routines that quantify a range of properties. Here, we describe a new analysis suite simulation analysis of unfolded regions of proteins (SOURSOP), an object-oriented and open-source toolkit designed for the analysis of simulated conformational ensembles of IDRs. SOURSOP implements several analysis routines motivated by principles in polymer physics, offering a unique collection of simple-to-use functions to characterize IDR ensembles. As an extendable framework, SOURSOP supports the development and implementation of new analysis routines that can be easily packaged and shared.

Liquid-liquid Phase Separation in Viral Function

An emerging set of results suggests that liquid-liquid phase separation (LLPS) is the basis for the formation of membrane-less compartments in cells. Evidence is now mounting that various types of virus-induced membrane-less compartments and organelles are also assembled via LLPS. Specifically, viruses appear to use intracellular phase transitions to form subcellular microenvironments known as viral factories, inclusion bodies, or viroplasms. These compartments - collectively referred to as viral biomolecular condensates - can be used to concentrate replicase proteins, viral genomes, and host proteins that are required for virus replication. They can also be used to subvert or avoid the intracellular immune response. This review examines how certain DNA or RNA viruses drive the formation of viral condensates, the possible biological functions of those condensates, and the biophysical and biochemical basis for their assembly.

Decoupling of Interactions between Model-Charged Peptides Reveals Key Factors Responsible for Liquid-Liquid Phase Separation

Liquid-liquid phase separation (LLPS) by disordered proteins has been shown to govern biological processes and cause numerous diseases. Therefore, a deeper understanding of the interactions and their variation with external factors is key to modulating the LLPS behavior of different systems and protecting proteins from pathological aggregation. In this context, we have looked at interactions between similarly charged peptides to understand the molecular features that may drive or prevent condensate formation under various conditions. We have studied dimer formation for model peptides where charged and noncharged amino acids have been placed alternatively. Using arginine and glutamic acid as the charged residues and varying the other residues with glycine, alanine, and proline to alter hydrophobicity, we have obtained the free-energy surface (FES) for the dimer formation for these systems under high salt concentration at two different temperatures using all-atom molecular dynamics simulations and the well-tempered metadynamics method. Our results indicate that a combination of effects such as hydrophobicity, arginine-arginine interactions, or water release from the solvation shell makes dimerization free energy more favorable for the positively charged peptides with lower flexibility. For the negatively charged peptides, the crucial role of water has been found in governing the FES. Systems having charged residues and phenylalanine in the peptide sequence also have been studied at high salt concentrations using unbiased simulations. In this case, only the positively charged peptides were found to aggregate through temperature-dependent hydrophobic and cation-π interactions. Overall, our study indicates that the negatively charged peptides are more likely to remain in the dilute phase under various conditions compared to the positively charged systems. The findings from our study would be helpful in designing and controlling systems to obtain LLPS behavior for therapeutic usage.

Phase Transitions of Associative Biomacromolecules

Multivalent proteins and nucleic acids, collectively referred to as multivalent associative biomacromolecules, provide the driving forces for the formation and compositional regulation of biomolecular condensates. Here, we review the key concepts of phase transitions of aqueous solutions of associative biomacromolecules, specifically proteins that include folded domains and intrinsically disordered regions. The phase transitions of these systems come under the rubric of coupled associative and segregative transitions. The concepts underlying these processes are presented, and their relevance to biomolecular condensates is discussed.

Intramolecular structural heterogeneity altered by long-range contacts in an intrinsically disordered protein

Short-range interactions and long-range contacts drive the 3D folding of structured proteins. The proteins' structure has a direct impact on their biological function. However, nearly 40% of the eukaryotes proteome is composed of intrinsically disordered proteins (IDPs) and protein regions that fluctuate between ensembles of numerous conformations. Therefore, to understand their biological function, it is critical to depict how the structural ensemble statistics correlate to the IDPs' amino acid sequence. Here, using small-angle X-ray scattering and time-resolved Förster resonance energy transfer (trFRET), we study the intramolecular structural heterogeneity of the neurofilament low intrinsically disordered tail domain (NFLt). Using theoretical results of polymer physics, we find that the Flory scaling exponent of NFLt subsegments correlates linearly with their net charge, ranging from statistics of ideal to self-avoiding chains. Surprisingly, measuring the same segments in the context of the whole NFLt protein, we find that regardless of the peptide sequence, the segments' structural statistics are more expanded than when measured independently. Our findings show that while polymer physics can, to some level, relate the IDP's sequence to its ensemble conformations, long-range contacts between distant amino acids play a crucial role in determining intramolecular structures. This emphasizes the necessity of advanced polymer theories to fully describe IDPs ensembles with the hope that it will allow us to model their biological function.

The molecular grammar of protein disorder guiding genome-binding locations

Intrinsically disordered regions (IDRs) direct transcription factors (TFs) towards selected genomic occurrences of their binding motif, as exemplified by budding yeast's Msn2. However, the sequence basis of IDR-directed TF binding selectivity remains unknown. To reveal this sequence grammar, we analyze the genomic localizations of >100 designed IDR mutants, each carrying up to 122 mutations within this 567-AA region. Our data points at multivalent interactions, carried by hydrophobic-mostly aliphatic-residues dispersed within a disordered environment and independent of linear sequence motifs, as the key determinants of Msn2 genomic localization. The implications of our results for the mechanistic basis of IDR-based TF binding preferences are discussed.

Novel Functional Features of cGMP Substrate Proteins IRAG1 and IRAG2

The inositol triphosphate-associated proteins IRAG1 and IRAG2 are cGMP kinase substrate proteins that regulate intracellular Ca²⁺. Previously, IRAG1 was discovered as a 125 kDa membrane protein at the endoplasmic reticulum, which is associated with the intracellular Ca²⁺ channel IP₃R-I and the PKGIβ and inhibits IP₃R-I upon PKGIβ-mediated phosphorylation. IRAG2 is a 75 kDa membrane protein homolog of IRAG1 and was recently also determined as a PKGI substrate. Several (patho-)physiological functions of IRAG1 and IRAG2 were meanwhile elucidated in a variety of human and murine tissues, e.g., of IRAG1 in various smooth muscles, heart, platelets, and other blood cells, of IRAG2 in the pancreas, heart, platelets, and taste cells. Hence, lack of IRAG1 or IRAG2 leads to diverse phenotypes in these organs, e.g., smooth muscle and platelet disorders or secretory deficiency, respectively. This review aims to highlight the recent research regarding these two regulatory proteins to envision their molecular and (patho-)physiological tasks and to unravel their functional interplay as possible (patho-)physiological counterparts.

Theory and Simulation of Multiphase Coexistence in Biomolecular Mixtures

Biomolecular condensates constitute a newly recognized form of spatial organization in living cells. Although many condensates are believed to form as a result of phase separation, the physicochemical properties that determine the phase behavior of heterogeneous biomolecular mixtures are only beginning to be explored. Theory and simulation provide invaluable tools for probing the relationship between molecular determinants, such as protein and RNA sequences, and the emergence of phase-separated condensates in such complex environments. This review covers recent advances in the prediction and computational design of biomolecular mixtures that phase-separate into many coexisting phases. First, we review efforts to understand the phase behavior of mixtures with hundreds or thousands of species using theoretical models and statistical approaches. We then describe progress in developing analytical theories and coarse-grained simulation models to predict multiphase condensates with the molecular detail required to make contact with biophysical experiments. We conclude by summarizing the challenges ahead for modeling the inhomogeneous spatial organization of biomolecular mixtures in living cells.

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

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

Conformation and Affinity Modulations by Multiple Phosphorylation Occurring in the BIN1 SH3 Domain Binding Site of the Tau Protein Proline-Rich Region

An increase in phosphorylation of the Tau protein is associated with Alzheimer's disease (AD) progression through unclear molecular mechanisms. In general, phosphorylation modifies the interaction of intrinsically disordered proteins, such as Tau, with other proteins; however, elucidating the structural basis of this regulation mechanism remains challenging. The bridging integrator-1 gene is an AD genetic determinant whose gene product, BIN1, directly interacts with Tau. The proline-rich motif recognized within a Tau(210-240) peptide by the SH3 domain of BIN1 (BIN1 SH3) is defined as ₂₁₆PTPP₂₁₉, and this interaction is modulated by phosphorylation. Phosphorylation of T217 within the Tau(210-240) peptide led to a 6-fold reduction in the affinity, while single phosphorylation at either T212, T231, or S235 had no effect on the interaction. Nonetheless, combined phosphorylation of T231 and S235 led to a 3-fold reduction in the affinity, although these phosphorylations are not within the BIN1 SH3-bound region of the Tau peptide. Using nuclear magnetic resonance (NMR) spectroscopy, these phosphorylations were shown to affect the local secondary structure and dynamics of the Tau(210-240) peptide. Models of the (un)phosphorylated peptides were obtained from molecular dynamics (MD) simulation validated by experimental data and showed compaction of the phosphorylated peptide due to increased salt bridge formation. This dynamic folding might indirectly impact the BIN1 SH3 binding by a decreased accessibility of the binding site. Regulation of the binding might thus not only be due to local electrostatic or steric effects from phosphorylation but also to the modification of the conformational properties of Tau.

Critical Assessment of Self-Consistency Checks in the All-Atom Molecular Dynamics Simulation of Intrinsically Disordered Proteins

All atom simulations can be used to quantify conformational properties of Intrinsically Disordered Proteins (IDP). However, simulations must satisfy convergence checks to ensure observables computed from simulation are reliable and reproducible. While absolute convergence is purely a theoretical concept requiring infinitely long simulation, a more practical, yet rigorous, approach is to impose Self Consistency Checks (SCCs) to gain confidence in the simulated data. Currently there is no study of SCCs in IDPs, unlike their folded counterparts. In this paper, we introduce different criteria for self-consistency checks for IDPs. Next, we impose these SCCs to critically assess the performance of different simulation protocols using the N terminal domain of HIV Integrase and the linker region of SARS-CoV-2 Nucleoprotein as two model IDPs. All simulation protocols begin with all-atom implicit solvent Monte Carlo (MC) simulation and subsequent clustering of MC generated conformations to create the representative structures of the IDPs. These representative structures serve as the initial structure for subsequent molecular dynamics (MD) runs with explicit solvent. We conclude that generating multiple short (∼3 μs) MD simulation trajectories─all starting from the most representative MC generated conformation─and merging them is the protocol of choice due to (i) its ability to satisfy multiple SCCs, (ii) consistently reproducing experimental data, and (iii) the efficiency of running independent trajectories in parallel by harnessing multiple cores available in modern GPU clusters. Running one long trajectory (greater than 20 μs) can also satisfy the first two criteria but is less desirable due to prohibitive computation time. These findings help resolve the challenge of identifying a usable starting configuration, provide an objective measure of SCC, and establish rigorous criteria to determine the minimum length (for one long simulation) or number of trajectories needed in all-atom simulation of IDPs.

The relevance of short peptides for an understanding of unfolded and intrinsically disordered proteins

Over the last thirty years the unfolded state of proteins has attracted considerable interest owing to the discovery of intrinsically disordered proteins which perform a plethora of functions despite resembling unfolded proteins to a significant extent. Research on both, unfolded and disordered proteins has revealed that their conformational properties can deviate locally from random coil behavior. In this context results from work on short oligopeptides suggest that individual amino acid residues sample the sterically allowed fraction of the Ramachandran plot to a different extent. Alanine has been found to exhibit a peculiarity in that it has a very high propensity for adopting polyproline II like conformations. This Perspectives article reviews work on short peptides aimed at exploring the Ramachandran distributions of amino acid residues in different contexts with experimental and computational means. Based on the thus provided overview the article discussed to what extent short peptides can serve as tools for exploring unfolded and disordered proteins and as benchmarks for the development of a molecular dynamics force field.

Controversial Properties of Amyloidogenic Proteins and Peptides: New Data in the COVID Era

For a long time, studies of amyloidogenic proteins and peptides (amyloidogenic PPs) have been focused basically on their harmful properties and association with diseases. A vast amount of research has investigated the structure of pathogenic amyloids forming fibrous deposits within or around cells and the mechanisms of their detrimental actions. Much less has been known about the physiologic functions and beneficial properties of amyloidogenic PPs. At the same time, amyloidogenic PPs have various useful properties. For example, they may render neurons resistant to viral infection and propagation and stimulate autophagy. We discuss here some of amyloidogenic PPs' detrimental and beneficial properties using as examples beta-amyloid (β-amyloid), implicated in the pathogenesis of Alzheimer's disease (AD), and α-synuclein-one of the hallmarks of Parkinson's disease (PD). Recently amyloidogenic PPs' antiviral and antimicrobial properties have attracted attention because of the COVID-19 pandemic and the growing threat of other viral and bacterial-induced diseases. Importantly, several COVID-19 viral proteins, e.g., spike, nucleocapsid, and envelope proteins, may become amyloidogenic after infection and combine their harmful action with the effect of endogenous APPs. A central area of current investigations is the study of the structural properties of amyloidogenic PPs, defining their beneficial and harmful properties, and identifying triggers that transform physiologically important amyloidogenic PPs into vicious substances. These directions are of paramount importance during the current SARS-CoV-2 global health crisis.