Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy

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

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

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.

Optimizing Force Fields with Experimental Data Using Ensemble Reweighting and Potential Contrasting

Despite force field improvements over the past decades, we still encounter situations where simulation results disagree with experiments due to force field inaccuracies. Such situations provide opportunities to improve force fields. In this study, we introduce a novel framework for optimizing force fields using experimental data. The unique feature of this framework is that it aims to optimize force fields to match experiments while minimizing the perturbation made to the original force field. To achieve this, we combine ensemble reweighting techniques with the potential contrasting method. Ensemble reweighting is used to reweight an ensemble of conformations generated using an existing force field to match experimental data while minimizing the perturbation to the original ensemble. Potential contrasting is then utilized to optimize force field parameters to reproduce the reweighted ensemble. We demonstrate the framework's effectiveness by optimizing a coarse-grained force field for intrinsically disordered proteins using experimental radius of gyration data.

Refined Bonded Terms in Coarse-Grained Models for Intrinsically Disordered Proteins Improve Backbone Conformations

Coarse-grained models designed for intrinsically disordered proteins and regions (IDP/Rs) usually omit some bonded potentials (e.g., angular and dihedral potentials) as a conventional strategy to enhance backbone flexibility. However, a notable drawback of this approach is the generation of inaccurate backbone conformations. Here, we addressed this problem by introducing residue-specific angular, refined dihedral, and correction map (CMAP) potentials, derived based on the statistics from a customized coil database. These bonded potentials were integrated into the existing Mpipi model, resulting in a new model, denoted as the "Mpipi+" model. Results show that the Mpipi+ model can improve backbone conformations. More importantly, it can markedly improve the secondary structure propensity (SSP) based on the experimental chemical shift and, consequently, succeed in capturing transient secondary structures. Moreover, the Mpipi+ model preserves the liquid-liquid phase separation (LLPS) propensities of IDPs.

Fundamental Aspects of Phase-Separated Biomolecular Condensates

Biomolecular condensates, formed through phase separation, are upending our understanding in much of molecular, cell, and developmental biology. There is an urgent need to elucidate the physicochemical foundations of the behaviors and properties of biomolecular condensates. Here we aim to fill this need by writing a comprehensive, critical, and accessible review on the fundamental aspects of phase-separated biomolecular condensates. We introduce the relevant theoretical background, present the theoretical basis for the computation and experimental measurement of condensate properties, and give mechanistic interpretations of condensate behaviors and properties in terms of interactions at the molecular and residue levels.

Capturing chemical reactions inside biomolecular condensates with reactive Martini simulations

Biomolecular condensates are phase separated systems that play an important role in the spatio-temporal organisation of cells. Their distinct physico-chemical nature offers a unique environment for chemical reactions to occur. The compartmentalisation of chemical reactions is also believed to be central to the development of early life. To demonstrate how molecular dynamics may be used to capture chemical reactions in condensates, here we perform reactive molecular dynamics simulations using the coarse-grained Martini forcefield. We focus on the formation of rings of benzene-1,3-dithiol inside a synthetic peptide-based condensate, and find that the ring size distribution shifts to larger macrocycles compared to when the reaction takes place in an aqueous environment. Moreover, reaction rates are noticeably increased when the peptides simultaneously undergo phase separation, hinting that condensates may act as chaperones in recruiting molecules to reaction hubs.

Tyrosine - a structural glue for hierarchical protein assembly

Protein self-assembly, guided by the interplay of sequence- and environment-dependent liquid-liquid phase separation (LLPS), constitutes a fundamental process in the assembly of numerous intrinsically disordered proteins. Heuristic examination of these proteins has underscored the role of tyrosine residues, evident in their conservation and pivotal involvement in initiating LLPS and subsequent liquid-solid phase transitions (LSPT). The development of tyrosine-templated constructs, designed to mimic their natural counterparts, emerges as a promising strategy for creating adaptive, self-assembling systems with diverse applications. This review explores the central role of tyrosine in orchestrating protein self-assembly, delving into key interactions and examining its potential in innovative applications, including responsive biomaterials and bioengineering.

Direct computations of viscoelastic moduli of biomolecular condensates

In vitro facsimiles of biomolecular condensates are formed by different types of intrinsically disordered proteins including prion-like low complexity domains (PLCDs). PLCD condensates are viscoelastic materials defined by time-dependent, sequence-specific complex shear moduli. Here, we show that viscoelastic moduli can be computed directly using a generalization of the Rouse model and information regarding intra- and inter-chain contacts that is extracted from equilibrium configurations of lattice-based Metropolis Monte Carlo (MMC) simulations. The key ingredient of the generalized Rouse model is the Zimm matrix that we compute from equilibrium MMC simulations. We compute two flavors of Zimm matrices, one referred to as the single-chain model that accounts only for intra-chain contacts, and the other referred to as a collective model, that accounts for inter-chain interactions. The single-chain model systematically overestimates the storage and loss moduli, whereas the collective model reproduces the measured moduli with greater fidelity. However, in the long time, low-frequency domain, a mixture of the two models proves to be most accurate. In line with the theory of Rouse, we find that a continuous distribution of relaxation times exists in condensates. The single crossover frequency between dominantly elastic versus dominantly viscous behaviors is influenced by the totality of the relaxation modes. Hence, our analysis suggests that viscoelastic fluid-like condensates are best described as generalized Maxwell fluids. Finally, we show that the complex shear moduli can be used to solve an inverse problem to obtain distributions of relaxation times that underlie the dynamics within condensates.

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.

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.

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.

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.

On the role of native contact cooperativity in protein folding

The consistency of energy landscape theory predictions with available experimental data, as well as direct evidence from molecular simulations, have shown that protein folding mechanisms are largely determined by the contacts present in the native structure. As expected, native contacts are generally energetically favorable. However, there are usually at least as many energetically favorable nonnative pairs owing to the greater number of possible nonnative interactions. This apparent frustration must therefore be reduced by the greater cooperativity of native interactions. In this work, we analyze the statistics of contacts in the unbiased all-atom folding trajectories obtained by Shaw and coworkers, focusing on the unfolded state. By computing mutual cooperativities between contacts formed in the unfolded state, we show that native contacts form the most cooperative pairs, while cooperativities among nonnative or between native and nonnative contacts are typically much less favorable or even anticooperative. Furthermore, we show that the largest network of cooperative interactions observed in the unfolded state consists mainly of native contacts, suggesting that this set of mutually reinforcing interactions has evolved to stabilize the native state.

Phase-separated droplets swim to their dissolution

Biological macromolecules can condense into liquid domains. In cells, these condensates form membraneless organelles that can organize chemical reactions. However, little is known about the physical consequences of chemical activity in and around condensates. Working with model bovine serum albumin (BSA) condensates, we show that droplets swim along chemical gradients. Active BSA droplets loaded with urease swim toward each other. Passive BSA droplets show diverse responses to externally applied gradients of the enzyme's substrate and products. In all these cases, droplets swim toward solvent conditions that favor their dissolution. We call this behavior "dialytaxis", and expect it to be generic, as conditions which favor dissolution typically reduce interfacial tension, whose gradients are well-known to drive droplet motion through the Marangoni effect. These results could potentially suggest alternative physical mechanisms for active transport in living cells, and may enable the design of fluid micro-robots.

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Interfacial exchange dynamics of biomolecular condensates are highly sensitive to client interactions

Phase separation of biomolecules can facilitate their spatiotemporally regulated self-assembly within living cells. Due to the selective yet dynamic exchange of biomolecules across condensate interfaces, condensates can function as reactive hubs by concentrating enzymatic components for faster kinetics. The principles governing this dynamic exchange between condensate phases, however, are poorly understood. In this work, we systematically investigate the influence of client-sticker interactions on the exchange dynamics of protein molecules across condensate interfaces. We show that increasing affinity between a model protein scaffold and its client molecules causes the exchange of protein chains between the dilute and dense phases to slow down and that beyond a threshold interaction strength, this slowdown in exchange becomes substantial. Investigating the impact of interaction symmetry, we found that chain exchange dynamics are also considerably slower when client molecules interact equally with different sticky residues in the protein. The slowdown of exchange is due to a sequestration effect, by which there are fewer unbound stickers available at the interface to which dilute phase chains may attach. These findings highlight the fundamental connection between client-scaffold interaction networks and condensate exchange dynamics.

Coiled-coil domains are sufficient to drive liquid-liquid phase separation in protein models

Liquid-liquid phase separation (LLPS) is thought to be a main driving force in the formation of membraneless organelles. Examples of such organelles include the centrosome, central spindle, and stress granules. Recently, it has been shown that coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin, spd-5, and centrosomin, might be capable of LLPS. CC domains have physical features that could make them the drivers of LLPS, but it is unknown if they play a direct role in the process. We developed a coarse-grained simulation framework for investigating the LLPS propensity of CC proteins, in which interactions that support LLPS arise solely from CC domains. We show, using this framework, that the physical features of CC domains are sufficient to drive LLPS of proteins. The framework is specifically designed to investigate how the number of CC domains, as well as the multimerization state of CC domains, can affect LLPS. We show that small model proteins with as few as two CC domains can phase separate. Increasing the number of CC domains up to four per protein can somewhat increase LLPS propensity. We demonstrate that trimer-forming and tetramer-forming CC domains have a dramatically higher LLPS propensity than dimer-forming coils, which shows that multimerization state has a greater effect on LLPS than the number of CC domains per protein. These data support the hypothesis of CC domains as drivers of protein LLPS, and have implications in future studies to identify the LLPS-driving regions of centrosomal and central spindle proteins.

Molecular Dynamics Simulations of Polyelectrolyte Complexes

Polyelectrolyte complexes (PECs) are currently of great interest due to their applications toward developing new adaptive materials and their relevance in membraneless organelles. These complexes emerge during phase separation when oppositely charged polymers are mixed in aqueous media. Peptide-based PECs are particularly useful toward developing new drug delivery methods due to their inherent biocompatibility. The underlying peptide sequence can be tuned to optimize specific material properties of the complex, such as interfacial tension and viscosity. Given their applicability, it would be advantageous to understand the underlying sequence-dependent phase behavior of oppositely charged peptides. Here, we report microsecond molecular dynamic simulations to characterize the effect of hydrophobicity on the sequence-dependent peptide conformation for model polypeptide sequences that were previously reported by Tabandeh et al. These sequences are designed with alternating chirality of the peptide backbone. We present microsecond simulations of six oppositely charged peptide pairs, characterizing the sequence-dependent effect on peptide size, degree of hydrogen bonding, secondary structure, and conformation. This analysis recapitulates sensible trends in peptide conformation and degree of hydrogen bonding, consistent with experimentally reported results. Ramachandran plots reveal that backbone conformation at the single amino acid level is highly influenced by the neighboring sequence in the chain. These results give insight into how subtle changes in hydrophobic side chain size and chirality influence the strength of hydrogen bonding between the chains and, ultimately, the secondary structure. Furthermore, principal component analysis reveals that the minimum energy structures may be subtly modulated by the underlying sequence.

Metastable condensates suppress conversion to amyloid fibrils

Stress granules form via co-condensation of RNA binding proteins with prion-like low complexity domains (PLCDs) and RNA molecules released by stress-induced polysomal runoff. Homotypic interactions among PLCDs can drive amyloid fibril formation and this is enhanced by ALS-associated mutations. We find that homotypic interactions that drive condensation versus fibril formation are separable for A1-LCD, the PLCD of hnRNPA1. These separable interactions lead to condensates that are metastable versus fibrils that are globally stable. Metastable condensates suppress fibril formation, and ALS-associated mutations enhance fibril formation by weakening condensate metastability. Mutations designed to enhance A1-LCD condensate metastability restore wild-type behaviors of stress granules in cells even when ALS-associated mutations are present. This suggests that fibril formation can be suppressed by enhancing condensate metastability through condensate-driving interactions.

Direct prediction of intrinsically disordered protein conformational properties from sequence

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

The molecular basis for cellular function of intrinsically disordered protein regions

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

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.

Elucidating the reversible and irreversible self-assembly mechanisms of low-complexity aromatic-rich kinked peptides and steric zipper peptides

Many RNA-binding proteins such as fused-in sarcoma (FUS) can self-assemble into reversible liquid droplets and fibrils through the self-association of their low-complexity (LC) domains. Recent experiments have revealed that SYG-rich segments in the FUS LC domains play critical roles in the reversible self-assembly behaviors of FUS. These FUS LC segments alone can self-assemble into reversible kinked fibrils, which are markedly different from the canonical irreversible steric zipper β-sheet fibrils. However, the molecular determinants underlying the reversible and irreversible self-assembly are poorly understood. Herein we conducted extensive all-atom and coarse-grained molecular dynamics simulations of four representative hexapeptides: two low-complexity aromatic-rich kinked peptides from the amyotrophic lateral sclerosis-related FUS protein, FUS37-42 (SYSGYS) and FUS54-59 (SYSSYG); and two steric zipper peptides from Alzheimer's-associated Aβ and Tau proteins, Aβ16-21 (KLVFFA) and Tau306-311 (VQIVYK). We dissected their reversible and irreversible self-assembly dynamics, predicted their phase separation behaviors, and elucidated the underpinning molecular interactions. Our simulations showed that alternating stickers (Tyr) and spacers (Gly and Ser) in FUS37-42 and FUS54-59 facilitate the formation of highly dynamic coil-rich oligomers and lead to reversible self-assembly, while consecutive hydrophobic residues of LVFF in Aβ16-21 and IVY in Tau306-311 act as hydrophobic patches, favoring the formation of stable β-sheet-rich oligomers and driving the irreversible self-assembly. Intriguingly, we found that FUS37-42 and FUS54-59 peptides, possessing the same amino acid composition and the same number of sticker and spacer residues, display differential self-assembly propensities. This finding suggests that the self-assembly behaviors of FUS peptides are fine-tuned by the site-specific patterning of spacer residues (Ser and Gly). This study provides significant mechanistic insights into reversible and irreversible peptide self-assembly, which would be helpful for understanding the molecular mechanisms underlying the formation of biological liquid condensates and pathological solid amyloid fibrils.

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.

Surface Charge Can Modulate Phase Separation of Multidomain Proteins

Phase separation has emerged as an important mechanism explaining the formation of certain biomolecular condensates. Biological phase separation is often driven by the multivalent interactions of modular protein domains. Beyond valency, the physical features of folded domains that promote phase separation are poorly understood. We used a model system─the small ubiquitin modifier (SUMO) and its peptide ligand, the SUMO interaction motif (SIM)─to examine how domain surface charge influences multivalency-driven phase separation. Phase separation of polySUMO and polySIM was altered by pH via a change in the protonation state of SUMO surface histidines. These effects were recapitulated by histidine mutations, which modulated SUMO solubility and polySUMO-polySIM phase separation in parallel and were quantitatively explained by atomistic modeling of weak interactions among proteins in the system. Thus, surface charge can tune the phase separation of multivalent proteins, suggesting a means of controlling phase separation biologically, evolutionarily, and therapeutically.

Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores

Phase separation of biomolecules underlies the formation and regulation of various membraneless condensates in cells. How condensates function reliably while surrounded by heterogeneous and dynamic mixtures of biomolecular components with specific and nonspecific interactions is yet to be understood. Studying multicomponent biomolecular mixtures with designer peptides has recently become an attractive avenue for learning about physicochemical principles governing cellular condensates. In this work, we employed long-timescale atomistic simulations of multicomponent tripeptide mixtures with all residue substitutions to illuminate the nature of direct and water-mediated interactions in a prototypical cellular condensate environment. We find that peptide mixtures form clusters with inverse hydrophobic order. Most multivalent and charged residues are localized in the cluster's core, with a large fraction of nonaromatic hydrophobic residues remaining on the surface. This inverse hydrophobic order in peptide clusters is partly driven by the expulsion of nonspecifically bound water molecules following peptide cluster growth. The growth of clusters is also accompanied by the formation of increasing numbers of specific water-mediated interactions between polar and charged residues. While the present study focused on the condensation of short peptide motifs, the general findings and analysis techniques should be helpful for future studies on larger peptides and protein condensation.

Self-assembly of temperature-responsive di-block polypeptides functionalized with unnatural amino acids

The incorporation of unnatural amino acids (uAAs) into protein-based polymers has emerged as a powerful methodology to expand their chemical repertoire. Recently, we demonstrated that incorporating uAAs into two temperature-responsive protein-based polymers-namely resilin- and elastin-like polypeptides (RLPs and ELPs, respectively)-can alter their properties. In this study, we incorporated aromatic uAAs into the protein sequence of RLP-ELP diblocks to yield new and diverse assemblies from a single DNA template. Specifically, we show that incorporating aromatic uAAs can modulate the phase-transition behaviors and self-assembly of the diblocks into various morphologies, including spherical and cylindrical micelles and single- and double-layered vesicles, with some constructs also demonstrating a temperature-responsive shape-shifting behavior. Next, we evaluated the ability of the RLP-ELP assemblies to encapsulate a chemotherapeutic drug, doxorubicin, and show how the identity of the incorporated uAAs and the morphology of the nanostructure affect the encapsulation efficiency. Taken together, our findings demonstrate that the multi-site incorporation of uAAs into temperature-responsive, amphiphilic protein-based diblock copolymers is a promising approach for the functionalization and tuning of self-assembled nanostructures.

Design of functional intrinsically disordered proteins

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

Adenosine Triphosphate Mediates Phase Separation of Disordered Basic Proteins by Bridging Intermolecular Interaction Networks

Adenosine triphosphate (ATP) is an abundant molecule with crucial cellular roles as the energy currency and a building block of nucleic acids and for protein phosphorylation. Here we show that ATP mediates the phase separation of basic intrinsically disordered proteins (bIDPs). In the resulting condensates, ATP is highly concentrated (apparent partition coefficients up to 7700) and serves as bridges between bIDP chains. These liquid-like droplets have some of the lowest interfacial tension (∼25 pN/μm) but high zero-shear viscosities (1-15 Pa s) due to the bridged protein networks, and yet their fusion has some of the highest speeds (∼1 μm/ms). The rapid fusion manifests extreme shear thinning, where the apparent viscosity is lower than zero-shear viscosity by over 100-fold, made possible by fast reformation of the ATP bridges. At still higher concentrations, ATP does not dissolve bIDP droplets but results in aggregates and fibrils.

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.

RNA regulates cohesiveness and porosity of a biological condensate

Biological condensates have emerged as key elements of a biological cell function, concentrating disparate biomolecules to accomplish specific biological tasks. RNA was identified as a key ingredient of such condensates, however, its effect on the physical properties of the condensate was found to depend on the condensate's composition while its effect on the microstructure has remained elusive. Here, we characterize the physical properties and the microstructure of a protein-RNA condensate by means of large-scale coarse-grained (CG) molecular dynamics simulations. By developing a custom CG model of RNA compatible with a popular CG model of proteins, we systematically investigate the structural, thermodynamic, and kinetic properties of condensate droplets containing thousands of individual protein and RNA molecules over a range of temperatures. While we find RNA to increase the condensate's cohesiveness, its effect on the condensate's fluidity is more nuanced with longer molecules compacting the condensate and making it less fluid. We show that a biological condensate has a sponge-like morphology of interconnected channels of size that increases with temperature and decreases in the presence of RNA. Our results suggest that longer RNA form a dynamic scaffold within a condensate, regulating not only its fluidity but also permeability to intruder molecules.

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

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

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

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

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Surface tension measurement and calculation of model biomolecular condensates

The surface tension of liquid-like protein-rich biomolecular condensates is an emerging physical principle governing the mesoscopic interior organisation of biological cells. In this study, we present a method to evaluate the surface tension of model biomolecular condensates, through straighforward sessile drop measurements of capillary lengths and condensate densities. Our approach bypasses the need for characterizing condensate viscosities, which was required in previously reported techniques. We demonstrate this method using model condensates comprising two mutants of the intrinsically disordered protein Ddx4N. Notably, we uncover a detrimental impact of increased protein net charge on the surface tension of Ddx4N condensates. Furthermore, we explore the application of Scheutjens-Fleer theory, calculating condensate surface tensions through a self-consistent mean-field framework using Flory-Huggins interaction parameters. This relatively simple theory provides semi-quantitative accuracy in predicting Ddx4N condensate surface tensions and enables the evaluation of molecular organisation at condensate surfaces. Our findings shed light on the molecular details of fluid-fluid interfaces in biomolecular condensates.

Principles of assembly and regulation of condensates of Polycomb repressive complex 1 through phase separation

Polycomb repressive complex 1 (PRC1) undergoes phase separation to form Polycomb condensates that are multi-component hubs for silencing Polycomb target genes. In this study, we demonstrate that formation and regulation of PRC1 condensates are consistent with the scaffold-client model, where the Chromobox 2 (CBX2) protein behaves as the scaffold while the other PRC1 proteins are clients. Such clients induce a re-entrant phase transition of CBX2 condensates. The composition of the multi-component PRC1 condensates (1) determines the dynamic properties of the scaffold protein; (2) selectively promotes the formation of CBX4-PRC1 condensates while dissolving condensates of CBX6-, CBX7-, and CBX8-PRC1; and (3) controls the enrichment of CBX4-, CBX7-, and CBX8-PRC1 in CBX2-PRC1 condensates and the exclusion of CBX6-PRC1 from CBX2-PRC1 condensates. Our findings uncover how multi-component PRC1 condensates are assembled via an intricate scaffold-client mechanism whereby the properties of the PRC1 condensates are sensitively regulated by its composition and stoichiometry.

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.

Cross-Talk of Cation-π Interactions with Electrostatic and Aromatic Interactions: A Salt-Dependent Trade-off in Biomolecular Condensates

Biomolecular condensates are essential for cellular functionality, yet the complex interplay among the diverse molecular interactions that mediate their formation remains poorly understood. Here, using coarse-grained molecular dynamics simulations, we address the contribution of cation-π interactions to the stability of condensates formed via liquid-liquid phase separation. We found greater stabilization of up to 80% via cation-π interactions in condensates formed from peptides with higher aromatic residue content or less charge clustering. The contribution of cation-π interactions to droplet stability increases with increasing ionic strength, suggesting a trade-off between cation-π and electrostatic interactions. Cation-π interactions, therefore, can compensate for reduced electrostatic interactions, such as occurs at higher salt concentrations and in sequences with less charged residue content or clustering. Designing condensates with desired biophysical characteristics therefore requires quantification not only of the individual interactions but also cross-talks involving charge-charge, π-π, and cation-π interactions.

Predictive scale-bridging simulations through active learning

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks

Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up.

Phase separation of protein mixtures is driven by the interplay of homotypic and heterotypic interactions

Prion-like low-complexity domains (PLCDs) are involved in the formation and regulation of distinct biomolecular condensates that form via phase separation coupled to percolation. Intracellular condensates often encompass numerous distinct proteins with PLCDs. Here, we combine simulations and experiments to study mixtures of PLCDs from two RNA-binding proteins, hnRNPA1 and FUS. Using simulations and experiments, we find that 1:1 mixtures of A1-LCD and FUS-LCD undergo phase separation more readily than either of the PLCDs on their own due to complementary electrostatic interactions. Tie line analysis reveals that stoichiometric ratios of different components and their sequence-encoded interactions contribute jointly to the driving forces for condensate formation. Simulations also show that the spatial organization of PLCDs within condensates is governed by relative strengths of homotypic versus heterotypic interactions. We uncover rules for how interaction strengths and sequence lengths modulate conformational preferences of molecules at interfaces of condensates formed by mixtures of proteins.

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.

Atomistic modeling of liquid-liquid phase equilibrium explains dependence of critical temperature on γ-crystallin sequence

Liquid-liquid phase separation of protein solutions has regained heightened attention for its biological importance and pathogenic relevance. Coarse-grained models are limited when explaining residue-level effects on phase equilibrium. Here we report phase diagrams for γ-crystallins using atomistic modeling. The calculations were made possible by combining our FMAP method for computing chemical potentials and Brownian dynamics simulations for configurational sampling of dense protein solutions, yielding the binodal and critic temperature (Tc). We obtain a higher Tc for a known high-Tc γ-crystallin, γF, than for a low-Tc paralog, γB. The difference in Tc is corroborated by a gap in second virial coefficient. Decomposition of inter-protein interactions reveals one amino-acid substitution between γB and γF, from Ser to Trp at position 130, as the major contributor to the difference in Tc. This type of analysis enables us to link phase equilibrium to amino-acid sequence and to design mutations for altering phase equilibrium.

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.

ATP Mediates Phase Separation of Disordered Basic Proteins by Bridging Intermolecular Interaction Networks

ATP is an abundant molecule with crucial cellular roles as the energy currency and a building block of nucleic acids and for protein phosphorylation. Here we show that ATP mediates the phase separation of basic intrinsically disordered proteins (bIDPs). In the resulting condensates, ATP is highly concentrated (apparent partition coefficients at 200-5000) and serves as bridges between bIDP chains. These liquid-like droplets have some of the lowest interfacial tension (~25 pN/μm) but high zero-shear viscosities (1-15 Pa s) due to the bridged protein networks, and yet their fusion has some of the highest speeds (~1 μm/ms). The rapid fusion manifests extreme shear thinning, where the apparent viscosity is lower than zero-shear viscosity by over 100-fold, made possible by fast reformation of the ATP bridges. At still higher concentrations, ATP does not dissolve bIDP droplets but results in aggregates and fibrils.