Computational Procedure for Predicting Excipient Effects on Protein-Protein Affinities.

Protein-protein interactions lie at the center of many biological processes and are a challenge in formulating biological drugs, such as antibodies. A key to mitigating protein association is to use small-molecule additives, i.e., excipients that can weaken protein-protein interactions. Here, we develop a computationally efficient model for predicting the viscosity-reducing effect of different excipient molecules by combining atomic-resolution MD simulations, binding polynomials, and a thermodynamic perturbation theory. In a proof of principle, this method successfully ranks the order of four types of excipients known to reduce the viscosity of solutions of a particular monoclonal antibody. This approach appears useful for predicting the effects of excipients on protein association and phase separation, as well as the effects of buffers on protein solutions.

A computational procedure for predicting excipient effects on protein-protein affinities.

Protein-protein interactions lie at the center of much biology and are a challenge in formulating biological drugs such as antibodies. A key to mitigating protein association is to use small molecule additives, i.e. excipients that can weaken protein-protein interactions. Here, we develop a computationally efficient model for predicting the viscosity-reducing effect of different excipient molecules by combining atomic-resolution MD simulations, binding polynomials and a thermodynamic perturbation theory. In a proof of principle, this method successfully rank orders four types of excipients known to reduce the viscosity of solutions of a particular monoclonal antibody. This approach appears useful for predicting effects of excipients on protein association and phase separation, as well as the effects of buffers on protein solutions.

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers.

Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Šerror, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD: tsim ∼ e0.023N for ML x MELD x MD vs tsim ∼ e0.168N for MD alone. This shows how machine learning information can be leveraged to advance physics-based modeling of proteins.

The Protein Folding Problem: The Role of Theory.

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

A predictive coarse-grained model for position-specific effects of post-translational modifications.

Biomolecules undergo liquid-liquid phase separation (LLPS), resulting in the formation of multicomponent protein-RNA membraneless organelles in cells. However, the physiological and pathological role of post-translational modifications (PTMs) on the biophysics of phase behavior is only beginning to be probed. To study the effect of PTMs on LLPS in silico, we extend our transferable coarse-grained model of intrinsically disordered proteins to include phosphorylated and acetylated amino acids. Using the parameters for modified amino acids available for fixed-charge atomistic force fields, we parameterize the size and atomistic hydropathy of the coarse-grained-modified amino acid beads and, hence, the interactions between the modified and natural amino acids. We then elucidate how the number and position of phosphorylated and acetylated residues alter the protein's single-chain compactness and its propensity to phase separate. We show that both the number and the position of phosphorylated threonines/serines or acetylated lysines can serve as a molecular on/off switch for phase separation in the well-studied disordered regions of Fused in Sarcoma (FUS) and DDX3X, respectively. We also compare modified residues to their commonly used PTM mimics for their impact on chain properties. Importantly, we show that the model can predict and capture experimentally measured differences in the phase behavior for position-specific modifications, showing that the position of modifications can dictate phase separation. In sum, this model will be useful for studying LLPS of post-translationally modified intrinsically disordered proteins and predicting how modifications control phase behavior with position-specific resolution.

Tyrosine phosphorylation regulates hnRNPA2 granule protein partitioning and reduces neurodegeneration.

mRNA transport in neurons requires formation of transport granules containing many protein components, and subsequent alterations in phosphorylation status can release transcripts for translation. Further, mutations in a structurally disordered domain of the transport granule protein hnRNPA2 increase its aggregation and cause hereditary proteinopathy of neurons, myocytes, and bone. We examine in vitro hnRNPA2 granule component phase separation, partitioning specificity, assembly/disassembly, and the link to neurodegeneration. Transport granule components hnRNPF and ch-TOG interact weakly with hnRNPA2 yet partition specifically into liquid phase droplets with the low complexity domain (LC) of hnRNPA2, but not FUS LC. In vitro hnRNPA2 tyrosine phosphorylation reduces hnRNPA2 phase separation, prevents partitioning of hnRNPF and ch-TOG into hnRNPA2 LC droplets, and decreases aggregation of hnRNPA2 disease variants. The expression of chimeric hnRNPA2 D290V in Caenorhabditis elegans results in stress-induced glutamatergic neurodegeneration; this neurodegeneration is rescued by loss of tdp-1, suggesting gain-of-function toxicity. The expression of Fyn, a tyrosine kinase that phosphorylates hnRNPA2, reduces neurodegeneration associated with chimeric hnRNPA2 D290V. These data suggest a model where phosphorylation alters LC interaction specificity, aggregation, and toxicity.

Sequence dependent phase separation of protein-polynucleotide mixtures elucidated using molecular simulations.

Ribonucleoprotein (RNP) granules are membraneless organelles (MLOs), which majorly consist of RNA and RNA-binding proteins and are formed via liquid-liquid phase separation (LLPS). Experimental studies investigating the drivers of LLPS have shown that intrinsically disordered proteins (IDPs) and nucleic acids like RNA and other polynucleotides play a key role in modulating protein phase separation. There is currently a dearth of modelling techniques which allow one to delve deeper into how polynucleotides play the role of a modulator/promoter of LLPS in cells using computational methods. Here, we present a coarse-grained polynucleotide model developed to fill this gap, which together with our recently developed HPS model for protein LLPS, allows us to capture the factors driving protein-polynucleotide phase separation. We explore the capabilities of the modelling framework with the LAF-1 RGG system which has been well studied in experiments and also with the HPS model previously. Further taking advantage of the fact that the HPS model maintains sequence specificity we explore the role of charge patterning on controlling polynucleotide incorporation into condensates. With increased charge patterning we observe formation of structured or patterned condensates which suggests the possible roles of polynucleotides in not only shifting the phase boundaries but also introducing microscopic organization in MLOs.

Molecular Details of Protein Condensates Probed by Microsecond Long Atomistic Simulations.

The formation of membraneless organelles in cells commonly occurs via liquid-liquid phase separation (LLPS) and is in many cases driven by multivalent interactions between intrinsically disordered proteins (IDPs). Investigating the nature of these interactions, and their effect on dynamics within the condensed phase, is therefore of critical importance but very challenging for either simulation or experiment. Here, we study these interactions and their dynamics by pairing a novel multiscale simulation strategy with microsecond all-atom MD simulations of a condensed, IDP-rich phase. We simulate two IDPs this way, the low complexity domain of FUS and the N-terminal disordered domain of LAF-1, and find good agreement with experimental information about average density, water content, and residue-residue contacts. We go significantly beyond what is known from experiments by showing that ion partitioning within the condensed phase is largely driven by the charge distribution of the proteins and-in the cases considered-shows little evidence of preferential interactions of the ions with the proteins. Furthermore, we can probe the microscopic diffusive dynamics within the condensed phase, showing that water and ions are in dynamic equilibrium between dense and dilute phases, and their diffusion is reduced in the dense phase. Despite their high concentration in the condensate, the protein molecules also remain mobile, explaining the observed liquid-like properties of this phase. We finally show that IDP self-association is driven by a combination of nonspecific hydrophobic interactions as well as hydrogen bonds, salt bridges, and π-π and cation-π interactions. The simulation approach presented here allows the structural and dynamical properties of biomolecular condensates to be studied in microscopic detail and is generally applicable to single- and multicomponent systems of proteins and nucleic acids involved in LLPS.

Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior.

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.

Biomolecular Phase Separation: From Molecular Driving Forces to Macroscopic Properties.

Biological phase separation is known to be important for cellular organization, which has recently been extended to a new class of biomolecules that form liquid-like droplets coexisting with the surrounding cellular or extracellular environment. These droplets are termed membraneless organelles, as they lack a dividing lipid membrane, and are formed through liquid-liquid phase separation (LLPS). Elucidating the molecular determinants of phase separation is a critical challenge for the field, as we are still at the early stages of understanding how cells may promote and regulate functions that are driven by LLPS. In this review, we discuss the role that disorder, perturbations to molecular interactions resulting from sequence, posttranslational modifications, and various regulatory stimuli play on protein LLPS, with a particular focus on insights that may be obtained from simulation and theory. We finally discuss how these molecular driving forces alter multicomponent phase separation and selectivity.

TDP-43 α-helical structure tunes liquid-liquid phase separation and function.

Liquid-liquid phase separation (LLPS) is involved in the formation of membraneless organelles (MLOs) associated with RNA processing. The RNA-binding protein TDP-43 is present in several MLOs, undergoes LLPS, and has been linked to the pathogenesis of amyotrophic lateral sclerosis (ALS). While some ALS-associated mutations in TDP-43 disrupt self-interaction and function, here we show that designed single mutations can enhance TDP-43 assembly and function via modulating helical structure. Using molecular simulation and NMR spectroscopy, we observe large structural changes upon dimerization of TDP-43. Two conserved glycine residues (G335 and G338) are potent inhibitors of helical extension and helix-helix interaction, which are removed in part by variants at these positions, including the ALS-associated G335D. Substitution to helix-enhancing alanine at either of these positions dramatically enhances phase separation in vitro and decreases fluidity of phase-separated TDP-43 reporter compartments in cells. Furthermore, G335A increases TDP-43 splicing function in a minigene assay. Therefore, the TDP-43 helical region serves as a short but uniquely tunable module where application of biophysical principles can precisely control assembly and function in cellular and synthetic biology applications of LLPS.

Molecular interactions underlying liquid-liquid phase separation of the FUS low-complexity domain.

The low-complexity domain of the RNA-binding protein FUS (FUS LC) mediates liquid-liquid phase separation (LLPS), but the interactions between the repetitive SYGQ-rich sequence of FUS LC that stabilize the liquid phase are not known in detail. By combining NMR and Raman spectroscopy, mutagenesis, and molecular simulation, we demonstrate that heterogeneous interactions involving all residue types underlie LLPS of human FUS LC. We find no evidence that FUS LC adopts conformations with traditional secondary structure elements in the condensed phase; rather, it maintains conformational heterogeneity. We show that hydrogen bonding, π/sp2, and hydrophobic interactions all contribute to stabilizing LLPS of FUS LC. In addition to contributions from tyrosine residues, we find that glutamine residues also participate in contacts leading to LLPS of FUS LC. These results support a model in which FUS LC forms dynamic, multivalent interactions via multiple residue types and remains disordered in the densely packed liquid phase.

Temperature-Controlled Liquid-Liquid Phase Separation of Disordered Proteins.

The liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) is a commonly observed phenomenon within the cell, and such condensates are also highly attractive for applications in biomaterials and drug delivery. A better understanding of the sequence-dependent thermoresponsive behavior is of immense interest as it will aid in the design of protein sequences with desirable properties and in the understanding of cellular response to heat stress. In this work, we use a transferable coarse-grained model to directly probe the sequence-dependent thermoresponsive phase behavior of IDPs. To achieve this goal, we develop a unique knowledge-based amino acid potential that accounts for the temperature-dependent effects on solvent-mediated interactions for different types of amino acids. Remarkably, we are able to distinguish between more than 35 IDPs with upper or lower critical solution temperatures at experimental conditions, thus providing direct evidence that incorporating the temperature-dependent solvent-mediated interactions to IDP assemblies can capture the difference in the shape of the resulting phase diagrams. Given the success of the model in predicting experimental behavior, we use it as a high-throughput screening framework to scan through millions of disordered sequences to characterize the composition dependence of protein phase separation.

Simulation methods for liquid-liquid phase separation of disordered proteins.

Liquid-liquid phase separation of intrinsically disordered proteins (IDPs) and other biomolecules is a highly complex but robust process used by living systems. Drawing inspiration from biology, phase separating proteins have been successfully utilized for promising applications in fields of materials design and drug delivery. These protein-based materials are advantageous due to the ability to finely tune their stimulus-responsive phase behavior and material properties, and the ability to encode biologically active motifs directly into the sequence. The number of possible protein sequences is virtually endless, which makes sequence-based design a rather daunting task, but also attractive due to the amount of control coming from exploration of this variable space. The use of computational methods in this field of research have come to the aid in several aspects, including interpreting experimental results, identifying important structural features and molecular mechanisms capable of explaining the phase behavior, and ultimately providing predictive frameworks for rational design of protein sequences. Here we provide an overview of computational studies focused on phase separating biomolecules and the tools that are available to researchers interested in this topic.

Relation between single-molecule properties and phase behavior of intrinsically disordered proteins.

Proteins that undergo liquid-liquid phase separation (LLPS) have been shown to play a critical role in many physiological functions through formation of condensed liquid-like assemblies that function as membraneless organelles within biological systems. To understand how different proteins may contribute differently to these assemblies and their functions, it is important to understand the molecular driving forces of phase separation and characterize their phase boundaries and material properties. Experimental studies have shown that intrinsically disordered regions of these proteins are a major driving force, as many of them undergo LLPS in isolation. Previous work on polymer solution phase behavior suggests a potential correspondence between intramolecular and intermolecular interactions that can be leveraged to discover relationships between single-molecule properties and phase boundaries. Here, we take advantage of a recently developed coarse-grained framework to calculate the θ temperature [Formula: see text], the Boyle temperature [Formula: see text], and the critical temperature [Formula: see text] for 20 diverse protein sequences, and we show that these three properties are highly correlated. We also highlight that these correlations are not specific to our model or simulation methodology by comparing between different pairwise potentials and with data from other work. We, therefore, suggest that smaller simulations or experiments to determine [Formula: see text] or [Formula: see text] can provide useful insights into the corresponding phase behavior.

Mechanistic View of hnRNPA2 Low-Complexity Domain Structure, Interactions, and Phase Separation Altered by Mutation and Arginine Methylation.

hnRNPA2, a component of RNA-processing membraneless organelles, forms inclusions when mutated in a syndrome characterized by the degeneration of neurons (bearing features of amyotrophic lateral sclerosis [ALS] and frontotemporal dementia), muscle, and bone. Here we provide a unified structural view of hnRNPA2 self-assembly, aggregation, and interaction and the distinct effects of small chemical changes-disease mutations and arginine methylation-on these assemblies. The hnRNPA2 low-complexity (LC) domain is compact and intrinsically disordered as a monomer, retaining predominant disorder in a liquid-liquid phase-separated form. Disease mutations D290V and P298L induce aggregation by enhancing and extending, respectively, the aggregation-prone region. Co-aggregating in disease inclusions, hnRNPA2 LC directly interacts with and induces phase separation of TDP-43. Conversely, arginine methylation reduces hnRNPA2 phase separation, disrupting arginine-mediated contacts. These results highlight the mechanistic role of specific LC domain interactions and modifications conserved across many hnRNP family members but altered by aggregation-causing pathological mutations.

Sequence determinants of protein phase behavior from a coarse-grained model.

Membraneless organelles important to intracellular compartmentalization have recently been shown to comprise assemblies of proteins which undergo liquid-liquid phase separation (LLPS). However, many proteins involved in this phase separation are at least partially disordered. The molecular mechanism and the sequence determinants of this process are challenging to determine experimentally owing to the disordered nature of the assemblies, motivating the use of theoretical and simulation methods. This work advances a computational framework for conducting simulations of LLPS with residue-level detail, and allows for the determination of phase diagrams and coexistence densities of proteins in the two phases. The model includes a short-range contact potential as well as a simplified treatment of electrostatic energy. Interaction parameters are optimized against experimentally determined radius of gyration data for multiple unfolded or intrinsically disordered proteins (IDPs). These models are applied to two systems which undergo LLPS: the low complexity domain of the RNA-binding protein FUS and the DEAD-box helicase protein LAF-1. We develop a novel simulation method to determine thermodynamic phase diagrams as a function of the total protein concentration and temperature. We show that the model is capable of capturing qualitative changes in the phase diagram due to phosphomimetic mutations of FUS and to the presence or absence of the large folded domain in LAF-1. We also explore the effects of chain-length, or multivalency, on the phase diagram, and obtain results consistent with Flory-Huggins theory for polymers. Most importantly, the methodology presented here is flexible so that it can be easily extended to other pair potentials, be used with other enhanced sampling methods, and may incorporate additional features for biological systems of interest.

Interplay Between Membrane Composition and Structural Stability of Membrane-Bound hIAPP.

Amyloid aggregates are characteristic of many serious diseases such as Alzheimer's disease, Parkinson's, and type 2 diabetes and commonly involve intrinsically disordered proteins (IDPs), those that populate an ensemble of conformations rather than a single folded structure. Human islet amyloid polypeptide (hIAPP or amylin) is an amyloidogenic IDP implicated in pancreatic ß-cell death during the pathogenesis of type 2 diabetes. The target of amylin's toxic activity is thought to be the cell's lipid membrane, which may also act as a catalyst for aggregation. Since amylin is intrinsically disordered, differing environments can have a large impact on its equilibrium conformational ensemble. We apply atomistic molecular dynamics simulations on multiple systems containing a full-length amylin monomer and a lipid bilayer to study the changes induced by the membrane. We observe stabilized helical conformations structurally similar to those determined by NMR experiments conducted in similar environments. We also find that bilayers of different compositions result in greatly different equilibrium ensembles of amylin. Finally, we discuss how a mixed bilayer containing zwitterionic and anionic lipid headgroups can allow for greater preference toward conformations which are adsorbed below the membrane surface through rearrangement of lipids for more favorable protein-lipid interactions.

Phosphorylation of the FUS low-complexity domain disrupts phase separation, aggregation, and toxicity.

Neuronal inclusions of aggregated RNA-binding protein fused in sarcoma (FUS) are hallmarks of ALS and frontotemporal dementia subtypes. Intriguingly, FUS's nearly uncharged, aggregation-prone, yeast prion-like, low sequence-complexity domain (LC) is known to be targeted for phosphorylation. Here we map in vitro and in-cell phosphorylation sites across FUS LC We show that both phosphorylation and phosphomimetic variants reduce its aggregation-prone/prion-like character, disrupting FUS phase separation in the presence of RNA or salt and reducing FUS propensity to aggregate. Nuclear magnetic resonance spectroscopy demonstrates the intrinsically disordered structure of FUS LC is preserved after phosphorylation; however, transient domain collapse and self-interaction are reduced by phosphomimetics. Moreover, we show that phosphomimetic FUS reduces aggregation in human and yeast cell models, and can ameliorate FUS-associated cytotoxicity. Hence, post-translational modification may be a mechanism by which cells control physiological assembly and prevent pathological protein aggregation, suggesting a potential treatment pathway amenable to pharmacologic modulation.