Direct prediction of intrinsically disordered protein conformational properties from sequence

Programmability and biomedical utility of intrinsically-disordered protein polymers

Intrinsically disordered proteins (IDPs) exhibit molecular-level conformational dynamics that are functionally harnessed across a wide range of fascinating biological phenomena. The low sequence complexity of IDPs has led to the design and development of intrinsically-disordered protein polymers (IDPPs), a class of engineered repeat IDPs with stimuli-responsive properties. The perfect repetitive architecture of IDPPs allows for repeat-level encoding of tunable protein functionality. Designer IDPPs can be modeled on endogenous IDPs or engineered de novo as protein polymers with dual biophysical and biological functionality. Their properties can be rationally tailored to access enigmatic IDP biology and to create programmable smart biomaterials. With the goal of inspiring the bioengineering of multifunctional IDP-based materials, here we synthesize recent multidisciplinary progress in programming and exploiting the bio-functionality of IDPPs and IDPP-containing proteins. Collectively, expanding beyond the traditional sequence space of extracellular IDPs, emergent sequence-level control of IDPP functionality is fueling the bioengineering of self-assembling biomaterials, advanced drug delivery systems, tissue scaffolds, and biomolecular condensates -genetically encoded organelle-like structures. Looking forward, we emphasize open challenges and emerging opportunities, arguing that the intracellular behaviors of IDPPs represent a rich space for biomedical discovery and innovation. Combined with the intense focus on IDP biology, the growing landscape of IDPPs and their biomedical applications set the stage for the accelerated engineering of high-value biotechnologies and biomaterials.

Extending computational protein design to intrinsically disordered proteins

Advances in the accuracy and throughput of molecular simulations usher in a new era in the structural biology of disordered proteins.

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a broad range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Computational design of IDPs with specific conformational properties has, however, been difficult because of their substantial dynamics and structural complexity. We describe a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs that differ in compaction, long-range contacts, and propensity to phase separate. We experimentally tested and validated our designs and analyzed the sequence features that determine conformations. We show how our results are captured by a machine learning model, enabling us to speed up the algorithm. Our work expands the toolbox for computational protein design and will facilitate the design of proteins whose functions exploit the many properties afforded by protein disorder.

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously adapt upon binding with specific interaction partners and/or certain alterations in environmental conditions. This malleability is foundational for their role as tunable interaction hubs in core cellular processes such as signaling, transcription, and translation. Tracing the conformational ensemble of an IDP/IDR and its perturbation in response to regulatory cues is thus paramount for illuminating its function. However, the conformational heterogeneity of IDP/IDRs poses several challenges. Here, we review experimental and computational methods devised to disentangle the conformational landscape of IDP/IDRs, highlighting recent computational advances that permit proteome-wide scans of IDP/IDRs conformations. We briefly evaluate selected computational methods using the disordered N-terminal of the human copper transporter 1 as a test case and outline further challenges in IDP/IDRs ensemble prediction.

Differential Effects of Sequence-Local versus Nonlocal Charge Patterns on Phase Separation and Conformational Dimensions of Polyampholytes as Model Intrinsically Disordered Proteins

Conformational properties of intrinsically disordered proteins (IDPs) are governed by a sequence-ensemble relationship. To differentiate the impact of sequence-local versus sequence-nonlocal features of an IDP's charge pattern on its conformational dimensions and its phase-separation propensity, the charge "blockiness" κ and the nonlocality-weighted sequence charge decoration (SCD) parameters are compared for their correlations with isolated-chain radii of gyration (Rgs) and upper critical solution temperatures (UCSTs) of polyampholytes modeled by random phase approximation, field-theoretic simulation, and coarse-grained molecular dynamics. SCD is superior to κ in predicting Rg because SCD accounts for effects of contact order, i.e., nonlocality, on dimensions of isolated chains. In contrast, κ and SCD are comparably good, though nonideal, predictors of UCST because frequencies of interchain contacts in the multiple-chain condensed phase are less sensitive to sequence positions than frequencies of intrachain contacts of an isolated chain, as reflected by κ correlating better with condensed-phase interaction energy than SCD.

Identification of plant transcriptional activation domains

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

Autoregulatory mechanism of enzyme activity by the nuclear localization signal of lysine-specific demethylase 1

The N-terminal region of the human lysine-specific demethylase 1 (LSD1) has no predicted structural elements, contains a nuclear localization signal (NLS), undergoes multiple posttranslational modifications (PTMs), and acts as a protein-protein interaction hub. This intrinsically disordered region (IDR) extends from core LSD1 structure, resides atop the catalytic active site, and is known to be dispensable for catalysis. Here, we show differential nucleosome binding between the full-length and an N terminus deleted LSD1 and identify that a conserved NLS and PTM containing element of the N terminus contains an alpha helical structure, and that this conserved element impacts demethylation. Enzyme assays reveal that LSD1's own electropositive NLS amino acids 107 to 120 inhibit demethylation activity on a model histone 3 lysine 4 dimethyl (H3K4me2) peptide (Kiapp ∼ 3.3 μM) and histone 3 lysine 4 dimethyl nucleosome substrates (IC50 ∼ 30.4 μM), likely mimicking the histone H3 tail. Further, when the identical, inhibitory NLS region contains phosphomimetic modifications, inhibition is partially relieved. Based upon these results and biophysical data, a regulatory mechanism for the LSD1-catalyzed demethylation reaction is proposed whereby NLS-mediated autoinhibition can occur through electrostatic interactions, and be partially relieved through phosphorylation that occurs proximal to the NLS. Taken together, the results highlight a dynamic and synergistic role for PTMs, intrinsically disordered regions, and structured regions near LSD1 active site and introduces the notion that phosphorylated mediated NLS regions can function to fine-tune chromatin modifying enzyme activity.

Protein surface chemistry encodes an adaptive resistance to desiccation

Cellular desiccation - the loss of nearly all water from the cell - is a recurring stress in an increasing number of ecosystems that can drive proteome-wide protein unfolding and aggregation. For cells to survive this stress, at least some of the proteome must disaggregate and resume function upon rehydration. The molecular determinants that underlie the ability of proteins to do this remain largely unknown. Here, we apply quantitative and structural proteomic mass spectrometry to desiccated and rehydrated yeast extracts to show that some proteins possess an innate capacity to survive extreme water loss. Structural analysis correlates the ability of proteins to resist desiccation with their surface chemistry. Remarkably, highly resistant proteins are responsible for the production of the cell's building blocks - amino acids, metabolites, and sugars. Conversely, those proteins that are most desiccation-sensitive are involved in ribosome biogenesis and other energy consuming processes. As a result, the rehydrated proteome is preferentially enriched with metabolite and small molecule producers and depleted of some of the cell's heaviest consumers. We propose this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration.

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

Molecular insights into the interaction between a disordered protein and a folded RNA

Intrinsically disordered protein regions (IDRs) are well-established as contributors to intermolecular interactions and the formation of biomolecular condensates. In particular, RNA-binding proteins (RBPs) often harbor IDRs in addition to folded RNA-binding domains that contribute to RBP function. To understand the dynamic interactions of an IDR-RNA complex, we characterized the RNA-binding features of a small (68 residues), positively charged IDR-containing protein, SERF. At high concentrations, SERF and RNA undergo charge-driven associative phase separation to form a protein- and RNA-rich dense phase. A key advantage of this model system is that this threshold for demixing is sufficiently high that we could use solution-state biophysical methods to interrogate the stoichiometric complexes of SERF with RNA in the one-phase regime. Herein, we describe our comprehensive characterization of SERF alone and in complex with a small fragment of the HIV-1 TAR RNA (TAR) with complementary biophysical methods and molecular simulations. We find that this binding event is not accompanied by the acquisition of structure by either molecule; however, we see evidence for a modest global compaction of the SERF ensemble when bound to RNA. This behavior likely reflects attenuated charge repulsion within SERF via binding to the polyanionic RNA and provides a rationale for the higher-order assembly of SERF in the context of RNA. We envision that the SERF-RNA system will lower the barrier to accessing the details that support IDR-RNA interactions and likewise deepen our understanding of the role of IDR-RNA contacts in complex formation and liquid-liquid phase separation.

Entering the Next Phase: Predicting Biological Effects of Biomolecular Condensates

Biomolecular condensates are increasingly recognized as important drivers of cellular function; their dysregulation leads to pathology and disease. We discuss three questions in terms of the impending utility of data-driven techniques to predict condensate-driven biological outcomes, i.e., the impact of cellular state changes on condensates, the effect of condensates on biochemical processes within, and condensate properties that result in cellular dysregulation and disease.

Direct prediction of intermolecular interactions driven by disordered regions

Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.

Conserved molecular recognition by an intrinsically disordered region in the absence of sequence conservation

Intrinsically disordered regions (IDRs) are critical for cellular function yet often appear to lack sequence conservation when assessed by multiple sequence alignments. This raises the question of if and how function can be encoded and preserved in these regions despite massive sequence variation. To address this question, we have applied coarse-grained molecular dynamics simulations to investigate non-specific RNA binding of coronavirus nucleocapsid proteins. Coronavirus nucleocapsid proteins consist of multiple interspersed disordered and folded domains that bind RNA. Here, we focus on the first two domains of coronavirus nucleocapsid proteins: the disordered N-terminal domain (NTD) and the folded RNA binding domain (RBD). While the NTD is highly variable across evolution, the RBD is structurally conserved. This combination makes the NTD-RBD a convenient model system for exploring the interplay between an IDR adjacent to a folded domain and how changes in IDR sequence can influence molecular recognition of a partner. Our results reveal a surprising degree of sequence-specificity encoded by both the composition and the precise order of the amino acids in the NTD. The presence of an NTD can - depending on the sequence - either suppress or enhance RNA binding. Despite this sensitivity, large-scale variation in NTD sequences is possible while certain sequence features are retained. Consequently, a conformationally-conserved dynamic and disordered RNA:protein complex is found across nucleocapsid protein orthologs despite large-scale changes in both NTD sequence and RBD surface chemistry. Taken together, these insights shed light on the ability of disordered regions to preserve functional characteristics despite their sequence variability.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Charge block-driven liquid-liquid phase separation - mechanism and biological roles

Liquid-liquid phase separation (LLPS) has increasingly been found to play pivotal roles in a number of intracellular events and reactions, and has introduced a new paradigm in cell biology to explain protein-protein and enzyme-ligand interactions beyond conventional molecular and biochemical theories. LLPS is driven by the cumulative effects of weak and promiscuous interactions, including electrostatic, hydrophobic and cation-π interactions, among polypeptides containing intrinsically disordered regions (IDRs) and describes the macroscopic behaviours of IDR-containing proteins in an intracellular milieu. Recent studies have revealed that interactions between 'charge blocks' - clusters of like charges along the polypeptide chain - strongly induce LLPS and play fundamental roles in its spatiotemporal regulation. Introducing a new parameter, termed 'charge blockiness', into physicochemical models of disordered polypeptides has yielded a better understanding of how the intrinsic amino acid sequence of a polypeptide determines the spatiotemporal occurrence of LLPS within a cell. Charge blockiness might also explain why some post-translational modifications segregate within IDRs and how they regulate LLPS. In this Review, we summarise recent progress towards understanding the mechanism and biological roles of charge block-driven LLPS and discuss how this new characteristic parameter of polypeptides offers new possibilities in the fields of structural biology and cell biology.

Structural dynamics of the intrinsically disordered linker region of cardiac troponin T

The cardiac troponin complex, composed of troponins I, T, and C, plays a central role in regulating the calcium-dependent interactions between myosin and the thin filament. Mutations in troponin can cause cardiomyopathies; however, it is still a major challenge for the field to connect how changes in sequence affect troponin's function. Recent high-resolution structures of the thin filament revealed critical insights into the structure-function relationship of the troponin complex, but there remain large, unresolved segments of troponin, including the troponin-T linker region that is a hotspot for several cardiomyopathy mutations. This unresolved yet functionally-significant linker region has been proposed to be intrinsically disordered, with behaviors that are not well described by traditional structural approaches; however, this proposal has not been experimentally verified. Here, we used a combination of single-molecule Förster resonance energy transfer (FRET), molecular dynamics simulations, and functional reconstitution assays to investigate the troponin-T linker region. We experimentally and computationally show that in the context of both isolated troponin and the fully regulated troponin complex, the linker behaves as a dynamic, intrinsically disordered region. This region undergoes polyampholyte expansion in the presence of high salt and distinct conformational changes during the assembly of the troponin complex. We also examine the ΔE160 hypertrophic cardiomyopathy mutation in the linker, and we demonstrate that this mutation does not affect the conformational dynamics of the linker, rather it allosterically affects interactions with other subunits of the troponin complex, leading to increased molecular contractility. Taken together, our data clearly demonstrate the importance of disorder within the troponin-T linker and provide new insights into the molecular mechanisms controlling the pathogenesis of cardiomyopathies.

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.

Nuclear receptor interdomain communication is mediated by the hinge with ligand specificity

Nuclear receptors are ligand-induced transcription factors that bind directly to target genes and regulate their expression. Ligand binding initiates conformational changes that propagate to other domains, allosterically regulating their activity. The nature of this interdomain communication in nuclear receptors is poorly understood, largely owing to the difficulty of experimentally characterizing full-length structures. We have applied computational modeling approaches to describe and study the structure of the full length farnesoid X receptor (FXR), approximated by the DNA binding domain (DBD) and ligand binding domain (LBD) connected by the flexible hinge region. Using extended molecular dynamics simulations (> 10 microseconds) and enhanced sampling simulations, we provide evidence that ligands selectively induce domain rearrangement, leading to interdomain contact. We use protein-protein interaction assays to provide experimental evidence of these interactions, identifying a critical role of the hinge in mediating interdomain contact. Our results illuminate previously unknown aspects of interdomain communication in FXR and provide a framework to enable characterization of other full length nuclear receptors.

Global Release of Translational Repression Across Plasmodium's Host-to-Vector Transmission Event

Malaria parasites must be able to respond quickly to changes in their environment, including during their transmission between mammalian hosts and mosquito vectors. Therefore, before transmission, female gametocytes proactively produce and translationally repress mRNAs that encode essential proteins that the zygote requires to establish a new infection. This essential regulatory control requires the orthologues of DDX6 (DOZI), LSM14a (CITH), and ALBA proteins to form a translationally repressive complex in female gametocytes that associates with many of the affected mRNAs. However, while the release of translational repression of individual mRNAs has been documented, the details of the global release of translational repression have not. Moreover, the changes in spatial arrangement and composition of the DOZI/CITH/ALBA complex that contribute to translational control are also not known. Therefore, we have conducted the first quantitative, comparative transcriptomics and DIA-MS proteomics of Plasmodium parasites across the host-to-vector transmission event to document the global release of translational repression. Using female gametocytes and zygotes of P. yoelii, we found that nearly 200 transcripts are released for translation soon after fertilization, including those with essential functions for the zygote. However, we also observed that some transcripts remain repressed beyond this point. In addition, we have used TurboID-based proximity proteomics to interrogate the spatial and compositional changes in the DOZI/CITH/ALBA complex across this transmission event. Consistent with recent models of translational control, proteins that associate with either the 5' or 3' end of mRNAs are in close proximity to one another during translational repression in female gametocytes and then dissociate upon release of repression in zygotes. This observation is cross-validated for several protein colocalizations in female gametocytes via ultrastructure expansion microscopy and structured illumination microscopy. Moreover, DOZI exchanges its interaction from NOT1-G in female gametocytes to the canonical NOT1 in zygotes, providing a model for a trigger for the release of mRNAs from DOZI. Finally, unenriched phosphoproteomics revealed the modification of key translational control proteins in the zygote. Together, these data provide a model for the essential translational control mechanisms used by malaria parasites to promote their efficient transmission from their mammalian host to their mosquito vector.

Expanding the molecular grammar of polar residues and arginine in FUS prion-like domain phase separation and aggregation

A molecular grammar governing low-complexity prion-like domains phase separation (PS) has been proposed based on mutagenesis experiments that identified tyrosine and arginine as primary drivers of phase separation via aromatic-aromatic and aromatic-arginine interactions. Here we show that additional residues make direct favorable contacts that contribute to phase separation, highlighting the need to account for these contributions in PS theories and models. We find that tyrosine and arginine make important contacts beyond only tyrosine-tyrosine and tyrosine-arginine, including arginine-arginine contacts. Among polar residues, glutamine in particular contributes to phase separation with sequence/position-specificity, making contacts with both tyrosine and arginine as well as other residues, both before phase separation and in condensed phases. For glycine, its flexibility, not its small solvation volume, favors phase separation by allowing favorable contacts between other residues and inhibits the liquid-to-solid (LST) transition. Polar residue types also make sequence-specific contributions to aggregation that go beyond simple rules, which for serine positions is linked to formation of an amyloid-core structure by the FUS low-complexity domain. Hence, here we propose a revised molecular grammar expanding the role of arginine and polar residues in prion-like domain protein phase separation and aggregation.

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.