A Data-Driven Hydrophobicity Scale for Predicting Liquid-Liquid Phase Separation of Proteins

Inhibition of tau aggregation by the CCT3 and CCT7 apical domains

The eukaryotic chaperonin containing t-complex polypeptide 1 (CCT/TRiC) is a molecular chaperone that assists protein folding in an ATP-driven manner. It consists of two stacked identical rings that are each made up of eight distinct subunits. Here, we show that the apical domains of subunits CCT3 and CCT7 from humans are strong inhibitors of tau aggregation, which is associated with several neurological disorders such as Alzheimer's and Parkinson's diseases. Kinetic analyses and negative-stain electron microscopy indicate that the mechanism of inhibition of tau aggregation by the apical domains of subunits CCT3 and CCT7 differ. Aggregation of tau alone, or in the presence of the apical domain of subunit CCT7, can be described by a fragmentation model whereas in the presence of the apical domain of subunit CCT3, it fits a saturating elongation and fragmentation mechanism. Coarse-grained molecular dynamics simulations show that tau interacts with different regions in the apical domains of subunits CCT3 and CCT7, in agreement with their different inhibition mechanisms.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Heterogeneous condensates of transcription factors in embryonic stem cells: Molecular simulations

Biomolecular condensates formed via liquid-liquid phase separation are ubiquitous in cells, especially in the nucleus. While condensates containing one or two kinds of biomolecules have been relatively well characterized, those with more heterogeneous biomolecular components and interactions between biomolecules inside are largely unknown. This study used residue-resolution molecular dynamics simulations to investigate heterogeneous protein assemblies that include four master transcription factors in mammalian embryonic stem cells: Oct4, Sox2, Klf4, and Nanog. Molecular dynamics simulations of the mixture systems showed highly heterogeneous and dynamic behaviors; protein condensates mainly contain Sox2, Klf4, and Nanog, while most Oct4 are dissolved into the dilute phase. The condensate forms loosely interacting clusters where Klf4 is the most abundant, suggesting that Klf4 serves as a scaffold of the condensate, and Sox2 and Nanog are bound to Klf4 for stabilizing the condensate. Oct4 is moderately recruited to the condensate, serving as a client mainly via its interaction with Sox2. This study highlights the importance of intermolecular interaction between different transcription factors on the condensate formations with heterogeneous biomolecular components.

Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates

Phase separation is a fundamental process that enables cellular organization by forming biomolecular condensates. These assemblies regulate diverse functions by creating distinct environments, influencing reaction kinetics, and facilitating processes such as genome organization, signal transduction, and RNA metabolism. Recent studies highlight the complexity of condensate properties, shaped by intrinsic molecular features and external factors such as temperature and pH. Molecular simulations serve as an effective approach to establishing a comprehensive framework for analyzing these influences, offering high-resolution insights into condensate stability, dynamics, and material properties. This review evaluates recent advancements in biomolecular condensate simulations, with a particular focus on coarse-grained 1-bead-per-amino-acid (1BPA) protein models, and emphasizes OpenABC, a tool designed to simplify and streamline condensate simulations. OpenABC supports the implementation of various coarse-grained force fields, enabling their performance evaluation. Our benchmarking identifies inconsistencies in phase behavior predictions across force fields, even though these models accurately capture single-chain statistics. This finding underscores the need for enhanced force field accuracy, achievable through enriched training data sets, many-body potentials, and advanced optimization techniques. Such refinements could significantly improve the predictive capacity of coarse-grained models, bridging molecular details with emergent condensate behaviors.

Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning

We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP sequences from MobiDB database of length 20-300 to obtain gyration radii from BD simulation on a coarse-grained single-bead amino acid model (HPS2 model) used by us and others [Dignon, G. L. PLoS Comput. Biol. 2018, 14, e1005941,Tesei, G. Proc. Natl. Acad. Sci. U.S.A. 2021, 118, e2111696118,Seth, S. J. Chem. Phys. 2024, 160, 014902] to generate the training sets for the DL algorithm. Using the gyration radii ⟨Rg⟩ of the simulated IDPs as the training set, we develop a multilayer perceptron neural net (NN) architecture that predicts the gyration radii of 33 IDPs previously studied by using BD simulation with 97% accuracy from the sequence and the corresponding parameters from the HPS model. We now utilize this NN to predict gyration radii of every permutation of missense mutations in IDPs. Our approach successfully identifies mutation-prone regions that induce significant alterations in the radius of gyration when compared to the wild-type IDP sequence. We further validate the prediction by running BD simulations on the subset of identified mutants. The neural network yields a (104-106)-fold faster computation in the search space for potentially harmful mutations. Our findings have substantial implications for rapid identification and understanding of diseases related to missense mutations in IDPs and for the development of potential therapeutic interventions. The method can be extended to accurate predictions of other mutation effects in disordered proteins.

Prediction of phase-separation propensities of disordered proteins from sequence

Phase separation is one possible mechanism governing the selective cellular enrichment of biomolecular constituents for processes such as transcriptional activation, mRNA regulation, and immune signaling. Phase separation is mediated by multivalent interactions of macromolecules including intrinsically disordered proteins and regions (IDRs). Despite considerable advances in experiments, theory, and simulations, the prediction of the thermodynamics of IDR phase behavior remains challenging. We combined coarse-grained molecular dynamics simulations and active learning to develop a fast and accurate machine learning model to predict the free energy and saturation concentration for phase separation directly from sequence. We validate the model using computational and previously measured experimental data, as well as new experimental data for six proteins. We apply our model to all 27,663 IDRs of chain length up to 800 residues in the human proteome and find that 1,420 of these (5%) are predicted to undergo homotypic phase separation with transfer free energies < -2 kBT. We use our model to understand the relationship between single-chain compaction and phase separation and find that changes from charge- to hydrophobicity-mediated interactions can break the symmetry between intra- and intermolecular interactions. We also provide proof of principle for how the model can be used in force field refinement. Our work refines and quantifies the established rules governing the connection between sequence features and phase-separation propensities, and our prediction models will be useful for interpreting and designing cellular experiments on the role of phase separation, and for the design of IDRs with specific phase-separation propensities.

Microphase separation produces interfacial environment within diblock biomolecular condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, that is, the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELPs). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Intrinsic hydrophobicity of IDP-based biomolecular condensates drives their partial drying on membrane surfaces

The localization of biomolecular condensates to intracellular membrane surfaces has emerged as an important feature of sub-cellular organization. In this work, we study the wetting behavior of biomolecular condensates on various substrates. We use confocal microscopy to measure the contact angles of model condensates formed by intrinsically disordered protein Ddx4N. We show the importance of taking optical aberrations into account, as these impact apparent contact angle measurements. Ddx4N condensates are seen to partially dry (contact angles above 90°) a model membrane, with little dependence on the magnitude of charge on, or tyrosine content of, Ddx4N. Further contact angle measurements on surfaces of varying hydrophilicity reveal a preference of Ddx4N condensates for hydrophobic surfaces, suggesting an intrinsic repulsion between protein condensates and hydrophilic membrane surfaces. This observation is in line with previous studies relating protein adsorption to surface hydrophilicity. Our work advances the understanding of the molecular details governing the localization of biomolecular condensates.

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins

Recent years have seen tremendous developments in the use of machine learning models to link amino-acid sequence, structure, and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of sequence-ensemble-function relationships of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains.

Coarse-Grained Model of Disordered RNA for Simulations of Biomolecular Condensates

Protein-RNA condensates are involved in a range of cellular activities. Coarse-grained molecular models of intrinsically disordered proteins have been developed to shed light on and predict single-chain properties and phase separation. An RNA model compatible with such models for disordered proteins would enable the study of complex biomolecular mixtures involving RNA. Here, we present a sequence-independent coarse-grained, two-beads-per-nucleotide model of disordered, flexible RNA based on a hydropathy scale. We parametrize the model, which we term CALVADOS-RNA, using a combination of bottom-up and top-down approaches to reproduce local RNA geometry and intramolecular interactions based on atomistic simulations and in vitro experiments. The model semiquantitatively captures several aspects of RNA-RNA and RNA-protein interactions. We examined RNA-RNA interactions by comparing calculated and experimental virial coefficients and nonspecific RNA-protein interaction by studying the reentrant phase behavior of protein-RNA mixtures. We demonstrate the utility of the model by simulating the formation of mixed condensates consisting of the disordered region of MED1 and RNA chains and the selective partitioning of disordered regions from transcription factors into these and compare the results to experiments. Despite the simplicity of our model, we show that it captures several key aspects of protein-RNA interactions and may therefore be used as a baseline model to study several aspects of the biophysics and biology of protein-RNA condensates.

Prediction of small-molecule partitioning into biomolecular condensates from simulation

Predicting small-molecule partitioning into biomolecular condensates is key to developing drugs that selectively target aberrant condensates. However, the molecular mechanisms underlying small-molecule partitioning remain largely unknown. Here, we first exploit atomistic molecular dynamics simulations of model condensates to elucidate physicochemical rules governing small-molecule partitioning. We find that while hydrophobicity is a major determinant, solubility becomes a stronger regulator of partitioning in more polar condensates. Additionally, more polar condensates exhibit selectivity toward certain compounds, suggesting that condensate-specific therapeutics can be engineered. Building on these insights, we develop minimal models (MAPPS) for efficient prediction of small-molecule partitioning into biologically relevant condensates. We demonstrate that this approach reproduces atomistic partition coefficients in both model systems and condensates composed of the low complexity domain (LCD) of FUS. Applying MAPPS to various LCD-based condensates shows that protein sequence can exert a selective pressure, thereby influencing small-molecule partitioning. Collectively, our findings reveal that partitioning is driven by both small-molecule-protein affinity and the complex interplay between the compounds and the condensate chemical environment.

Accurate prediction of thermoresponsive phase behavior of disordered proteins

Protein responses to environmental stress, particularly temperature fluctuations, have long been a subject of investigation, with a focus on how proteins maintain homeostasis and exhibit thermoresponsive properties. While UCST-type (upper critical solution temperature) phase behavior has been studied extensively and can now be predicted reliably using computational models, LCST-type (lower critical solution temperature) phase transitions remain less explored, with a lack of computational models capable of accurate prediction. This gap limits our ability to probe fully how proteins undergo phase transitions in response to temperature changes. Here, we introduce Mpipi-T, a residue-level coarse-grained model designed to predict LCST-type phase behavior of proteins. Parametrized using both atomistic simulations and experimental data, Mpipi-T accounts for entropically driven protein phase separation that occurs upon heating. Accordingly, Mpipi-T predicts temperature-driven protein behavior quantitatively in both single- and multi-chain systems. Beyond its predictive capabilities, we demonstrate that Mpipi-T provides a framework for uncovering the molecular mechanisms underlying heat stress responses, offering new insights into how proteins sense and adapt to thermal changes in biological systems.

Electrostatics of salt-dependent reentrant phase behaviors highlights diverse roles of ATP in biomolecular condensates

Liquid-liquid phase separation (LLPS) involving intrinsically disordered protein regions (IDRs) is a major physical mechanism for biological membraneless compartmentalization. The multifaceted electrostatic effects in these biomolecular condensates are exemplified here by experimental and theoretical investigations of the different salt- and ATP-dependent LLPSs of an IDR of messenger RNA-regulating protein Caprin1 and its phosphorylated variant pY-Caprin1, exhibiting, for example, reentrant behaviors in some instances but not others. Experimental data are rationalized by physical modeling using analytical theory, molecular dynamics, and polymer field-theoretic simulations, indicating that interchain ion bridges enhance LLPS of polyelectrolytes such as Caprin1 and the high valency of ATP-magnesium is a significant factor for its colocalization with the condensed phases, as similar trends are observed for other IDRs. The electrostatic nature of these features complements ATP's involvement in π-related interactions and as an amphiphilic hydrotrope, underscoring a general role of biomolecular condensates in modulating ion concentrations and its functional ramifications.

Chemically Informed Coarse-Graining of Electrostatic Forces in Charge-Rich Biomolecular Condensates

Biomolecular condensates composed of highly charged biomolecules, such as DNA, RNA, chromatin, and nucleic-acid binding proteins, are ubiquitous in the cell nucleus. The biophysical properties of these charge-rich condensates are largely regulated by electrostatic interactions. Residue-resolution coarse-grained models that describe solvent and ions implicitly are widely used to gain mechanistic insights into the biophysical properties of condensates, offering transferability, computational efficiency, and accurate predictions for multiple systems. However, their predictive accuracy diminishes for charge-rich condensates due to the implicit treatment of solvent and ions. Here, we present Mpipi-Recharged, a residue-resolution coarse-grained model that improves the description of charge effects in biomolecular condensates containing disordered proteins, multidomain proteins, and/or disordered single-stranded RNAs. Mpipi-Recharged introduces a pair-specific asymmetric Yukawa electrostatic potential, informed by atomistic simulations. We show that this asymmetric coarse-graining of electrostatic forces captures intricate effects, such as charge blockiness, stoichiometry variations in complex coacervates, and modulation of salt concentration, without requiring explicit solvation. Mpipi-Recharged provides excellent agreement with experiments in predicting the phase behavior of highly charged condensates. Overall, Mpipi-Recharged improves the computational tools available to investigate the physicochemical mechanisms regulating biomolecular condensates, enhancing the scope of computer simulations in this field.

COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

Liquid-liquid phase separation of intrinsically disordered proteins: Effect of osmolytes and crowders

The formation of membraneless organelles is vital for the intracellular organization of macromolecules and in regulating many cellular processes. The membraneless organelles are formed by liquid-liquid phase separation (LLPS) mainly constituted of proteins and polynucleotides. The primary factor driving the liquid demixing into two phases is the multivalency of the proteins involved, a general characteristic of intrinsically disordered proteins (IDPs) or proteins with intrinsically disordered regions (IDRs). This chapter discusses the role of IDP/IDRs in biomolecular condensate formation and the physical characteristics of these states. Further, the LLPS formation of individual proteins induced by molecular crowding and its relevance to physiological conditions are presented. The studies on the effects of small molecular osmolytes and a hydrotrope, ATP on the phase separation temperature, protein concentration, and reentrant behavior are discussed. The advancements and limitations of the computational methods to predict the phase separation behavior of IDPs, and to analyze the interactions and dynamics of the proteins in condensates are presented. The roles of phase separation in cancer, neurological disorders, and cardiovascular diseases are highlighted.

The molecular mechanism of temperature-dependent phase separation of heat shock factor 1

Heat shock factor 1 (HSF1) is the critical orchestrator of cell responses to heat shock, and its dysfunction is linked to various diseases. HSF1 undergoes phase separation upon heat shock, and its activity is regulated by post-translational modifications (PTMs). The molecular details underlying HSF1 phase separation, temperature sensing and PTM regulation remain poorly understood. Here, we discovered that HSF1 exhibits temperature-dependent phase separation with a lower critical solution temperature behavior, providing a new conceptual mechanism accounting for HSF1 activation. We revealed the residue-level molecular details of the interactions driving the phase separation of wild-type HSF1 and its distinct PTM patterns at various temperatures. The mapped interfaces were validated experimentally and accounted for the reported HSF1 functions. Importantly, the molecular grammar of temperature-dependent HSF1 phase separation is species specific and physiologically relevant. These findings delineate a chemical code that integrates accurate phase separation with physiological body temperature control in animals.

Benchmarking residue-resolution protein coarse-grained models for simulations of biomolecular condensates

Intracellular liquid-liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)-a protein implicated in the pathological liquid-to-solid transition of stress granules. Specifically, we assess the HPS, HPS-cation-π, HPS-Urry, CALVADOS2, Mpipi, and Mpipi-Recharged models in their predictions of the condensate saturation concentration, critical solution temperature, and condensate viscosity of the A1-LCD variants. Our analyses demonstrate that, among the tested models, Mpipi, Mpipi-Recharged, and CALVADOS2 provide accurate descriptions of the critical solution temperatures and saturation concentrations for the multiple A1-LCD variants tested. Regarding the prediction of material properties for condensates of A1-LCD and its variants, Mpipi-Recharged stands out as the most reliable model. Overall, this study benchmarks a range of residue-resolution coarse-grained models for the study of the thermodynamic stability and material properties of condensates and establishes a direct link between their performance and the ranking of intermolecular interactions these models consider.

Recent Progress in Modeling and Simulation of Biomolecular Crowding and Condensation Inside Cells

Macromolecular crowding in the cellular cytoplasm can potentially impact diffusion rates of proteins, their intrinsic structural stability, binding of proteins to their corresponding partners as well as biomolecular organization and phase separation. While such intracellular crowding can have a large impact on biomolecular structure and function, the molecular mechanisms and driving forces that determine the effect of crowding on dynamics and conformations of macromolecules are so far not well understood. At a molecular level, computational methods can provide a unique lens to investigate the effect of macromolecular crowding on biomolecular behavior, providing us with a resolution that is challenging to reach with experimental techniques alone. In this review, we focus on the various physics-based and data-driven computational methods developed in the past few years to investigate macromolecular crowding and intracellular protein condensation. We review recent progress in modeling and simulation of biomolecular systems of varying sizes, ranging from single protein molecules to the entire cellular cytoplasm. We further discuss the effects of macromolecular crowding on different phenomena, such as diffusion, protein-ligand binding, and mechanical and viscoelastic properties, such as surface tension of condensates. Finally, we discuss some of the outstanding challenges that we anticipate the community addressing in the next few years in order to investigate biological phenomena in model cellular environments by reproducing in vivo conditions as accurately as possible.

Identifying Sequence Effects on Chain Dimensions of Disordered Proteins by Integrating Experiments and Simulations

It has become increasingly evident that the conformational distributions of intrinsically disordered proteins or regions are strongly dependent on their amino acid compositions and sequence. To facilitate a systematic investigation of these sequence-ensemble relationships, we selected a set of 16 naturally occurring intrinsically disordered regions of identical length but with large differences in amino acid composition, hydrophobicity, and charge patterning. We probed their conformational ensembles with single-molecule Förster resonance energy transfer (FRET), complemented by circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy as well as small-angle X-ray scattering (SAXS). The set of disordered proteins shows a strong dependence of the chain dimensions on sequence composition, with chain volumes differing by up to a factor of 6. The residue-specific intrachain interaction networks that underlie these pronounced differences were identified using atomistic simulations combined with ensemble reweighting, revealing the important role of charged, aromatic, and polar residues. To advance a transferable description of disordered protein regions, we further employed the experimental data to parametrize a coarse-grained model for disordered proteins that includes an explicit representation of the FRET fluorophores and successfully describes experiments with different dye pairs. Our findings demonstrate the value of integrating experiments and simulations for advancing our quantitative understanding of the sequence features that determine the conformational ensembles of intrinsically disordered proteins.

Effects of All-Atom and Coarse-Grained Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of a Tau K18 Monomer

To propose new mechanism-based therapeutics for Alzheimer's disease (AD), it is crucial to study the kinetics and oligomerization/aggregation mechanisms of the hallmark tau proteins, which have various isoforms and are intrinsically disordered. In this study, multiple all-atom (AA) and coarse-grained (CG) force fields (FFs) have been benchmarked on molecular dynamics (MD) simulations of K18 tau (M243-E372), which is a truncated form (130 residues) of full-length tau (441 residues). FF19SB is first excluded because the dynamics are too slow, and the conformations are too stable. All other benchmarked AAFFs (Charmm36m, FF14SB, Gromos54A7, and OPLS-AA) and CGFFs (Martini3 and Sirah2.0) exhibit a trend of shrinking K18 tau into compact structures with the radius of gyration (ROG) around 2.0 nm, which is much smaller than the experimental value of 3.8 nm, within 200 ns of AA-MD or 2000 ns of CG-MD. Gromos54A7, OPLS-AA, and Martini3 shrink much faster than the other FFs. To perform meaningful postanalysis of various properties, we propose a strategy of selecting snapshots with 2.5 < ROG < 4.5 nm, instead of using all sampled snapshots. The calculated chemical shifts of all C, CA, and CB atoms have very good and close root-mean-square error (RMSE) values, while Charmm36m and Sirah2.0 exhibit better chemical shifts of N than other FFs. Comparing the calculated distributions of the distance between the CA atoms of CYS291 and CYS322 with the results of the FRET experiment demonstrates that Charmm36m is a perfect match with the experiment while other FFs exhibit limitations. In summary, Charmm36m is recommended as the best AAFF, and Sirah2.0 is recommended as an excellent CGFF for simulating tau K18.

Amino Acid Transfer Free Energies Reveal Thermodynamic Driving Forces in Biomolecular Condensate Formation

The self-assembly of intrinsically disordered proteins into biomolecular condensates shows a dependence on the primary sequence of the protein, leading to sequence-dependent phase separation. Methods to investigate this sequence-dependent phase separation rely on effective residue-level interaction potentials that quantify the propensity for the residues to remain in the dilute phase versus the dense phase. The most direct measure of these effective potentials are the distribution coefficients of the different amino acids between the two phases, but due to the lack of availability of these coefficients, proxies, most notably hydropathy, have been used. However, recent work has demonstrated the limitations of the assumption of hydropathy-driven phase separation. In this work, we address this fundamental gap by calculating the transfer free energies associated with transferring each amino acid side chain analog from the dilute phase to the dense phase of a model biomolecular condensate. We uncover an interplay between favorable protein-mediated and unfavorable water-mediated contributions to the overall free energies of transfer. We further uncover an asymmetry between the contributions of positive and negative charges in the driving forces for condensate formation. The results presented in this work provide an explanation for several non-trivial trends observed in the literature and will aid in the interpretation of experiments aimed at elucidating the sequence-dependent driving forces underlying the formation of biomolecular condensates.

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

Sequence-Encoded Spatiotemporal Dependence of Viscoelasticity of Protein Condensates Using Computational Microrheology

Many biomolecular condensates act as viscoelastic complex fluids with distinct cellular functions. Deciphering the viscoelastic behavior of biomolecular condensates can provide insights into their spatiotemporal organization and physiological roles within cells. Although there is significant interest in defining the role of condensate dynamics and rheology in physiological functions, the quantification of their time-dependent viscoelastic properties is limited and is mostly done through experimental rheological methods. Here, we demonstrate that a computational passive probe microrheology technique, coupled with continuum mechanics, can accurately characterize the linear viscoelasticity of condensates formed by intrinsically disordered proteins (IDPs). Using a transferable coarse-grained protein model, we first provide a physical basis for choosing optimal values that define the attributes of the probe particle, namely, its size and interaction strength with the residues in an IDP chain. We show that the technique captures the sequence-dependent viscoelasticity of heteropolymeric IDPs that differ in either sequence charge patterning or sequence hydrophobicity. We also illustrate the technique's potential in quantifying the spatial dependence of viscoelasticity in heterogeneous IDP condensates. The computational microrheology technique has important implications for investigating the time-dependent rheology of complex biomolecular architectures, resulting in the sequence-rheology-function relationship for condensates.

CELF2 promotes tau exon 10 inclusion via hinge domain-mediated nuclear condensation

Alternative splicing is a fundamental process that contributes to the functional diversity and complexity of proteins. The regulation of each alternative splicing event involves the coordinated action of multiple RNA-binding proteins, creating a diverse array of alternatively spliced products. Dysregulation of alternative splicing is associated with various diseases, including neurodegeneration. Here we demonstrate that CELF2, a splicing regulator and a GWAS-identified risk factor for Alzheimer's disease, binds to mRNAs associated with neurodegenerative diseases, with a specific interaction observed in the intron adjacent to exon 10 on Tau mRNA. Loss of CELF2 in the mouse brain results in a decreased inclusion of Tau exon 10, leading to a reduced 4R:3R ratio. Further exploration shows that the hinge domain of CELF2 possesses an intrinsically disordered region (IDR), which mediates CELF2 condensation and function. The functionality of IDR in regulating CELF2 function is underscored by its substitutability with IDRs from FUS and TAF15. Using TurboID we identified proteins that interact with CELF2 through its IDR. We revealed that CELF2 co-condensate with NOVA2 and SFPQ, which coordinate with CELF2 to regulate the alternative splicing of Tau exon 10. A negatively charged residue within the IDR (D388), which is conserved among CELF proteins, is critical for CELF2 condensate formation, interactions with NOVA2 and SFPQ, and function in regulating tau exon 10 splicing. Our data allow us to propose that CELF2 regulates Tau alternative splicing by forming condensates through its IDR with other splicing factors, and that the composition of the proteins within the condensates determines the outcomes of alternative splicing events.

COCOMO2: A coarse-grained model for interacting folded and disordered proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parameterized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multi-scale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

A coarse-grained model for disordered and multi-domain proteins

Many proteins contain more than one folded domain, and such modular multi-domain proteins help expand the functional repertoire of proteins. Because of their larger size and often substantial dynamics, it may be difficult to characterize the conformational ensembles of multi-domain proteins by simulations. Here, we present a coarse-grained model for multi-domain proteins that is both fast and provides an accurate description of the global conformational properties in solution. We show that the accuracy of a one-bead-per-residue coarse-grained model depends on how the interaction sites in the folded domains are represented. Specifically, we find excessive domain-domain interactions if the interaction sites are located at the position of the Cα atoms. We also show that if the interaction sites are located at the center of mass of the residue, we obtain good agreement between simulations and experiments across a wide range of proteins. We then optimize our previously described CALVADOS model using this center-of-mass representation, and validate the resulting model using independent data. Finally, we use our revised model to simulate phase separation of both disordered and multi-domain proteins, and to examine how the stability of folded domains may differ between the dilute and dense phases. Our results provide a starting point for understanding interactions between folded and disordered regions in proteins, and how these regions affect the propensity of proteins to self-associate and undergo phase separation.

A pH-Dependent Coarse-Grained Model for Disordered Proteins: Histidine Interactions Modulate Conformational Ensembles

Histidine (His) presents a unique challenge for modeling disordered protein conformations, as it is versatile and occurs in both the neutral (His0) and positively charged (His+) states. These His charge states, which are enabled by its imidazole side chain, influence the electrostatic and short-range interactions of His residues, which potentially engage in cation-π, π-π, and charge-charge interactions. Existing coarse-grained (CG) models often simplify His representation by assigning it an average charge, thereby neglecting these potential short-range interactions. To address this gap, we developed a model for intrinsically disordered proteins (IDPs) that accounts for the properties of histidine (H). The resulting IDPH model is a 21-amino acid CG model incorporating both His charge states. We show that interactions involving previously neglected His0 are critical for accurate modeling at high pH, where they significantly influence the compaction of His-rich IDPs such as Histatin-5 and CPEB4. These interactions contribute to structural stabilizations primarily via His0-His0 and His0-Arg interactions, which are overlooked in models focusing solely on the charged His+ state.

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.

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.

Coarse-Grained Modeling of Liquid-Liquid Phase Separation in Cells: Challenges and Opportunities

Liquid-liquid phase separation (LLPS) within cells gives rise to membraneless organelles, which play pivotal roles in numerous cellular functions. A comprehensive understanding of the functional aspects of intrinsically disordered protein (IDP) condensates necessitates elucidating their inherent structures and establishing correlations with biological functions. Coarse-grained (CG) molecular dynamics (MD) simulations present a promising avenue for gaining insights into LLPS mechanisms of biomacromolecules. Essential to this endeavor is the development of tailored CG force fields for MD simulations, incorporating the full spectrum of biomolecules involved in the formation of condensates and accounting for real-time biochemical reactions coupled to the LLPS. Moreover, developing accurate theoretical frameworks and establishing links between condensate structure and its function are imperative for a thorough comprehension of LLPS of biological systems.

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Microphase Separation Produces Interfacial Environment within Diblock Biomolecular Condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, i.e., the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELP). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Molecular dynamics simulation on regulation of liquid-liquid phase separation of repetitive peptides

Understanding the intricate interactions governing protein and peptide behavior in liquid-liquid phase separation (LLPS) is crucial for unraveling biological functions and dysfunctions. This study employs a residue-leveled coarse-grained molecular dynamics approach to simulate the phase separation of repetitive polyproline and polyarginine peptides (poly PR) with varying lengths and sequences in solution, considering different concentrations and temperatures. Our findings highlight the crucial role of sequence order in promoting LLPS in peptides with identical lengths of repetitive sequences. Interestingly, repetitive peptides containing fewer than 10 polyarginine repeats exhibit no LLPS, even at salt concentrations up to 3 M. Notably, our simulations align with experimental observations, pinpointing a salt concentration of 2.7 M for PR25-induced LLPS. Utilizing the same methodology, we predict the required salt concentrations for LLPS induction as 1.2 M, 1.5 M, and 2.7 M for PR12, PR15, and PR35, respectively. These predictions demonstrate good agreement with experimental results. Extending our investigation to include the peptide glutamine and arginine (GR15) in DNA solution, our simulations mirror experimental observations of phase separation. To unveil the molecular forces steering peptide phase separation, we introduce a dielectric constant modifier and hydrophobicity disruptor into poly PR systems. Our coarse-grained analysis includes an examination of temperature effects, leading to the inference that both hydrophobic and electrostatic interactions drive phase separation in peptide systems.

Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures

Multiphasic architectures are found ubiquitously in biomolecular condensates and are thought to have important implications for the organization of multiple chemical reactions within the same compartment. Many of these multiphasic condensates contain RNA in addition to proteins. Here, we investigate the importance of different interactions in multiphasic condensates comprising two different proteins and RNA using computer simulations with a residue-resolution coarse-grained model of proteins and RNA. We find that in multilayered condensates containing RNA in both phases, protein-RNA interactions dominate, with aromatic residues and arginine forming the key stabilizing interactions. The total aromatic and arginine content of the two proteins must be appreciably different for distinct phases to form, and we show that this difference increases as the system is driven toward greater multiphasicity. Using the trends observed in the different interaction energies of this system, we demonstrate that we can also construct multilayered condensates with RNA preferentially concentrated in one phase. The "rules" identified can thus enable the design of synthetic multiphasic condensates to facilitate further study of their organization and function.

Deciphering the liquid-liquid phase separation induced modulation in the structure, dynamics, and enzymatic activity of an ordered protein β-lactoglobulin

Owing to the significant role in the subcellular organization of biomolecules, physiology, and the realm of biomimetic materials, studies related to biomolecular condensates formed through liquid-liquid phase separation (LLPS) have emerged as a growing area of research. Despite valuable contributions of prior research, there is untapped potential in exploring the influence of phase separation on the conformational dynamics and enzymatic activities of native proteins. Herein, we investigate the LLPS of β-lactoglobulin (β-LG), a non-intrinsically disordered protein, under crowded conditions. In-depth characterization through spectroscopic and microscopic techniques revealed the formation of dynamic liquid-like droplets, distinct from protein aggregates, driven by hydrophobic interactions. Our analyses revealed that phase separation can alter structural flexibility and photophysical properties. Importantly, the phase-separated β-LG exhibited efficient enzymatic activity as an esterase; a characteristic seemingly exclusive to β-LG droplets. The droplets acted as robust catalytic crucibles, providing an ideal environment for efficient ester hydrolysis. Further investigation into the catalytic mechanism suggested the involvement of specific amino acid residues, rather than general acid or base catalysis. Also, the alteration in conformational distribution caused by phase separation unveils the latent functionality. Our study delineates the understanding of protein phase separation and insights into the diverse catalytic strategies employed by proteins. It opens exciting possibilities for designing functional artificial compartments based on phase-separated biomolecules.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Backbone interactions and secondary structures in phase separation of disordered proteins

Intrinsically disordered proteins (IDPs) are one of the major drivers behind the formation and characteristics of biomolecular condensates. Due to their inherent flexibility, the backbones of IDPs are significantly exposed, rendering them highly influential and susceptible to biomolecular phase separation. In densely packed condensates, exposed backbones have a heightened capacity to interact with neighboring protein chains, which might lead to strong coupling between the secondary structures and phase separation and further modulate the subsequent transitions of the condensates, such as aging and fibrillization. In this mini-review, we provide an overview of backbone-mediated interactions and secondary structures within biomolecular condensates to underscore the importance of protein backbones in phase separation. We further focus on recent advances in experimental techniques and molecular dynamics simulation methods for probing and exploring the roles of backbone interactions and secondary structures in biomolecular phase separation involving IDPs.

Optimizing the Martini 3 Force Field Reveals the Effects of the Intricate Balance between Protein-Water Interaction Strength and Salt Concentration on Biomolecular Condensate Formation

Condensation/dissolution has become a widely acknowledged biological macromolecular assembly phenomenon in subcellular compartmentalization. The MARTINI force field offers a coarse-grained protein model with a resolution that preserves molecular details with an explicit (CG) solvent. Despite its relatively higher resolution, it can still achieve condensate formation in a reasonable computing time with explicit solvent and ionic species. Therefore, it is highly desirable to tune this force field to be able to reproduce the experimentally observed properties of the condensate formation. In this work, we studied the condensate formation of the low-sequence complexity domain of fused in sarcoma protein using a MARTINI 3 force field by systematically modifying (increasing) the protein-water interaction strength and varying the salt concentration. We found that the condensate formation is sensitive both to the protein-water interaction strength and the presence of salt. While the unmodified MARTINI force field yields a complete collapse of proteins into one dense phase (i.e., no dilute phase), we reported a range of modified protein-water interaction strength that is capable of capturing the experimentally found transfer free energy between dense and dilute phases. We also found that the condensates lose their spherical shape upon the addition of salt, especially when the protein-water interactions are weak. Interchain amino acid contact map analysis showed one explanation for this observation: the protein-protein contact fraction reduces as salt is added to systems (when the protein-water interactions are weak), consistent with electrostatic screening effects. This reduction might be responsible for the condensates becoming nonspherical upon the addition of salt by reducing the need for minimizing the interfacial area. However, as the protein-water interactions become stronger to the extent that makes the transfer free energy agree well with experimentally observed transfer free energy, we found an increase in the protein-protein contact fraction upon the addition of salt, consistent with the salting-out effects. Therefore, we concluded that there is an intricate balance between screening effects and salting-out effects upon the addition of salt and this balance is highly sensitive to the strength of protein-water interactions.

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.

Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation

Intrinsically disordered proteins (IDPs) frequently mediate phase separation that underlies the formation of a biomolecular condensate. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding the sequence-specific phase separation of IDPs. However, the widely used Cα-only models are limited in capturing the peptide nature of IDPs, particularly backbone-mediated interactions and effects of secondary structures, in phase separation. Here, we describe a hybrid resolution (HyRes) protein model toward a more accurate description of the backbone and transient secondary structures in phase separation. With an atomistic backbone and coarse-grained side chains, HyRes can semiquantitatively capture the residue helical propensity and overall chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for the direct simulation of spontaneous phase separation and, at the same time, appears accurate enough to resolve the effects of single His to Lys mutations. HyRes simulations also successfully predict increased β-structure formation in the condensate, consistent with available experimental CD data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate the phase separation propensity as measured by the saturation concentration. The simulations successfully recapitulate the effect of these mutants on the helicity and phase separation propensity of TDP-43 CR. Analyses reveal that the balance between backbone and side chain-mediated interactions, but not helicity itself, actually determines phase separation propensity. These results support that HyRes represents an effective protein model for molecular simulation of IDP phase separation and will help to elucidate the coupling between transient secondary structures and phase separation.

Multi-eGO: Model Improvements toward the Study of Complex Self-Assembly Processes

Structure-based models have been instrumental in simulating protein folding and suggesting hypotheses about the mechanisms involved. Nowadays, at least for fast-folding proteins, folding can be simulated in explicit solvent using classical molecular dynamics. However, other self-assembly processes, such as protein aggregation, are still far from being accessible. Recently, we proposed that a hybrid multistate structure-based model, multi-eGO, could help to bridge the gap toward the simulation of out-of-equilibrium, concentration-dependent self-assembly processes. Here, we further improve the model and show how multi-eGO can effectively and accurately learn the conformational ensemble of the amyloid β42 intrinsically disordered peptide, reproduce the well-established folding mechanism of the B1 immunoglobulin-binding domain of streptococcal protein G, and reproduce the aggregation as a function of the concentration of the transthyretin 105-115 amyloidogenic peptide. We envision that by learning from the dynamics of a few minima, multi-eGO can become a platform for simulating processes inaccessible to other simulation techniques.

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.

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.

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a wide range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Designing IDPs with specific structural or functional properties has, however, been difficult, in part because determining accurate conformational ensembles of IDPs generally requires a combination of computational modelling and experiments. Motivated by recent advancements in efficient physics-based models for simulations of IDPs, we have developed a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs with different levels of compaction and that vary more than 100 fold in their propensity to undergo phase separation, even while keeping a fixed amino acid composition. We experimentally tested designs of variants of the low-complexity domain of hnRNPA1 and find high accuracy in our computational predictions, both in terms of single-chain compaction and propensity to undergo phase separation. We analyze the sequence features that determine changes in compaction and propensity to phase separate and find an overall good agreement with previous findings for naturally occurring sequences. Our general, physics-based method enables the design of disordered sequences with specified conformational properties. Our algorithm thus expands the toolbox for protein design to include also the most flexible proteins and will enable the design of proteins whose functions exploit the many properties afforded by protein disorder.

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.

ATP Acts as a Hydrotrope to Regulate the Phase Separation of NBDY Clusters

Nonannotated P-body dissociating polypeptide (NBDY) is a recently discovered human microprotein that has been found to be a novel component of the mRNA decapping complex. Previous studies have shown that the phosphorylation of NBDY promotes the liquid phase of the NBDY remixing in vitro. Typically, during the process of phosphorylation, a phosphate group is added to the protein through adenosine triphosphate (ATP) hydrolysis. It has been shown that ATP acts as a biological hydrotrope, affecting the phase separation of proteins in solution. In this study, we utilized simulation methods to investigate the dynamic properties of the NBDY clusters at various ATP concentrations. Our findings demonstrate that ATP can regulate the phase separation of NBDY clusters. Specifically, we identified a critical point in the concentration ratio between ATP and NBDY that exhibits a dual effect on the phase separation of NBDY. We observed that the nonsaturated ATP concentration can facilitate the formation of phase separation, while oversaturated ATP concentration promotes the diffusion of NBDY, and the oversaturated ATP-NBDY interaction impedes the phase separation of NBDY. Additionally, we found that ATPs can bind to the protein surface by aggregating into ATP clusters, which further hinders the diffusion of NBDY clusters. Our work provides general insight into the role of ATP in the phase separation of protein condensates.

OpenABC enables flexible, simplified, and efficient GPU accelerated simulations of biomolecular condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.

Not Only Expansion: Proline Content and Density Also Induce Disordered Protein Conformation Compaction

Intrinsically disordered proteins (IDPs) adopt a wide array of different conformations that can be constrained by the presence of proline residues, which are frequently found in IDPs. To assess the effects of proline, we designed a series of peptides that differ with respect to the number of prolines in the sequence and their organization. Using high-resolution atomistic molecular dynamics simulations, we found that accounting for whether the proline residues are clustered or isolated contributed significantly to explaining deviations in the experimentally-determined gyration radii of IDPs from the values expected based on the Flory scaling-law. By contrast, total proline content makes smaller contribution to explaining the effect of prolines on IDP conformation. Proline residues exhibit opposing effects depending on their organizational pattern in the IDP sequence. Clustered prolines (i.e., prolines with ≤2 intervening non-proline residues) result in expanded peptide conformations whereas isolated prolines (i.e., prolines with >2 intervening non-proline residues) impose compacted conformations. Clustered prolines were estimated to induce an expansion of ∼20% in IDP dimension (via formation of PPII structural elements) whereas isolated prolines were estimated to induce a compaction of ∼10% in IDP dimension (via the formation of backbone turns). This dual role of prolines provides a mechanism for conformational switching that does not rely on the kinetically much slower isomerization of cis proline to the trans form. Bioinformatic analysis demonstrates high populations of both isolated and clustered prolines and implementing them in coarse-grained molecular dynamics models illustrates that they improve the characterization of the conformational ensembles of IDPs.

Accurate Simulation of Coupling between Protein Secondary Structure and Liquid-Liquid Phase Separation

Intrinsically disordered proteins (IDPs) frequently mediate liquid-liquid phase separation (LLPS) that underlies the formation of membraneless organelles. Together with theory and experiment, efficient coarse-grained (CG) simulations have been instrumental in understanding sequence-specific phase separation of IDPs. However, the widely-used Cα-only models are severely limited in capturing the peptide nature of IDPs, including backbone-mediated interactions and effects of secondary structures, in LLPS. Here, we describe a hybrid resolution (HyRes) protein model for accurate description of the backbone and transient secondary structures in LLPS. With an atomistic backbone and coarse-grained side chains, HyRes accurately predicts the residue helical propensity and chain dimension of monomeric IDPs. Using GY-23 as a model system, we show that HyRes is efficient enough for direct simulation of spontaneous phase separation, and at the same time accurate enough to resolve the effects of single mutations. HyRes simulations also successfully predict increased beta-sheet formation in the condensate, consistent with available experimental data. We further utilize HyRes to study the phase separation of TPD-43, where several disease-related mutants in the conserved region (CR) have been shown to affect residual helicities and modulate LLPS propensity. The simulations successfully recapitulate the effect of these mutants on the helicity and LLPS propensity of TDP-43 CR. Analyses reveal that the balance between backbone and sidechain-mediated interactions, but not helicity itself, actually determines LLPS propensity. We believe that the HyRes model represents an important advance in the molecular simulation of LLPS and will help elucidate the coupling between IDP transient secondary structures and phase separation.