Biology subjects

Dignon, G. L.

Publications and source records attributed to Dignon, G. L..

12 recordsLinked to original sources

bioRxiv2026

Designed IDPs phase separate and mix or demix according to sequence designed parameters

Biomolecular condensates are condensed assemblies of biomolecules that form through the process of liquid phase separation. Condensates in biology typically function as membraneless organelles, providing compartmentalization in the absence of a dividing lipid membrane. The molecular make-up of different condensates includes diverse multivalent proteins and nucleic acids as the primary drivers, many of them including significant fractions of intrinsically disordered regions (IDRs). To date, the sequence to phase separation relationship of IDRs has focused largely on one protein at a time, studying single-component condensate formation, or single-component partitioning into condensates. The co-phase separation and mixing of two or more IDRs is considerably more complex as both sequences can vary widely in their self- and cross-interactions, as well as their relative abundance in solution. It is unclear that a rule which predicts how a single sequence behaves will also predict what happens when two sequences are mixed. In this study, we disentangle the influence of sequence from that of composition using a set of 18 LAF-1 RGG variants that keep the same length and amino-acid composition and change only the order of the residues. This lets us vary charge patterning and, to a lesser extent, hydropathy patterning while keeping protein composition fixed. By themselves, the sequences phase separation and single-chain compaction are controlled by their degree of charge and hydropathy patterning. Within single-component condensed phases, each sequence adopts a more extended conformational ensemble, due to a more favorable, selfsolvated environment. We find that mixing two IDRs together into a condensate causes this universal scaling behavior to break, impacted by the relative interactions of the two components and overall composition of the slab. We find two different qualitative behaviors, one characterized by cooperative co-condensation when both sequences are subcritical, and the other by scaffold-client behavior when one sequence is supercritical. The scaffold-client systems generally show a high degree of demixing, while the co-condensing systems are generally quite well-mixed in the dense phase. This is surprising because even in cases where both partners have significantly different patterning parameters, they still mix. Thus, the descriptor that predicts a sequence's behavior alone can help indicate whether it will mix with or separate from a second component, but it does not fully determine the outcome on its own.

biophysics↗

bioRxiv2026

Analysis and design of disordered polypeptides with optimized sequence patterning properties

Intrinsically disordered proteins (IDPs) exhibit phase separation behavior that is closely linked to their degree of single-chain compaction, which in turn is governed by both amino acid composition and sequence patterning. Existing metrics such as sequence charge decoration (SCD) and sequence hydropathy decoration (SHD) describe these effects but are largely limited to describing differences between sequences of similar length and overall composition. In this work, we present a shuffle-based normalization scheme for SCD and SHD, enabling comparison of sequence patterning between very different IDP sequences. Leveraging this normalization scheme toward design space, we develop a Monte Carlo based sequence design algorithm that generates novel IDPs with desired patterning features. Our design framework is further strengthened by incorporating additional metrics such as sequence aromatic decoration (SAD), compositional RMSD, and a previously developed sequence based {Delta}G predictor. We validate our approach through coarse-grained MD simulations, showing that the designed sequences exhibit tunable phase behavior. This strategy lays the groundwork for rational design of IDPs for biomedical and biotechnology applications, as well as basic biophysical research. Author summaryIntrinsically disordered proteins behave similar to polymers in solution, having no defined structure. Their behavior is dictated by the collection of shapes the protein adopts, known as its "conformational ensemble" which is tuned by its amino acid sequence and the solution environment. In this work, we have developed parameters to describe the patterning of charged and hydrophobic amino acids within these protein sequences, which are predictive of their ability to phase separate and form dense liquid-like droplets in solution. Importantly, the parameters we develop are motivated by physics and can be applied across a large number of amino acid sequences rapidly. This will enable researchers to rapidly predict the behavior of large libraries of protein sequences. We have additionally developed a software to design randomized amino acid sequences with desired amino acid composition and patterning properties. Finally, we have tested our design scheme and parameters by running simulations of designed IDP sequences and quantified each of their ability to phase separate.

biophysics↗

bioRxiv2025

Hydration free energy is a significant predictor of globular protein incorporation into condensates

Membraneless organelles (MLOs) are assemblies of biomolecules, which function without a dividing lipid membrane in a cellular environment. These MLOs, termed biomolecular condensates, are commonly formed by the thermodynamic process of liquid-liquid phase separation (LLPS) and assembly of large numbers of proteins, nucleic acids and co-solvent molecules. Within MLOs, certain biomolecule types are particularly causative of phase separation, and are termed "scaffolds" as they provide the major driving forces for self-assembly. Other molecules that are present in a condensate, but are less causative than the scaffold molecules are termed "clients". Much effort has recently decoded many of the molecular interactions underlying LLPS in search of predicting equilibrium concentrations and materials properties of condensates. In this work, we provide a simple computational approach to predict the partitioning of globular protein clients into condensates primarily composed of disordered protein scaffolds. Specifically, we use multiple methods to calculate hydration free energy of a series of globular proteins, and find that hydration free energy is relatively well-correlated with the partition coefficient of these proteins into condensates. We then provide a comparison of different hydration free energy predictors and discuss why some may provide a more accurate prediction of partitioning. Finally, we discuss the shortcomings of hydration free energy as a predictor by identifying other possible confounding factors such as specific interactions, charge matching, and differential solvation inside a condensate, which will aid in making more robust predictions in future studies trained on more diverse data sets. Significance StatementUnderstanding the extent to which molecules can partition into biomolecular condensates is crucial for deciphering cellular organization and function. This study introduces a simple computational approach to predict the partitioning of globular proteins into condensates using hydration free energy as a key predictor, which can be calculated from static globular protein structures. By comparing different hydration free energy predictors, we also higlight the limitations of relying solely on this hydration free energy, emphasizing the need to consider other molecular factors. We finally analyze the shortcomings of the hydration free energy precitions, and discuss other factors that contribute to partitioning of clients into condensates, namely specific interactions and net charge of the scaffold molecules, and different properties of water in the condensate.

biophysics↗

bioRxiv2024

Controlled and orthogonal partitioning of large particles into biomolecular condensates

Biomolecular condensates arising from liquid-liquid phase separation contribute to diverse cellular processes, such as gene expression. Partitioning of client molecules into condensates is critical to regulating the composition and function of condensates. Previous studies suggest that client size limits partitioning, with dextrans >5 nm excluded from condensates. Here, we asked whether larger particles, such as macromolecular complexes, can partition into condensates based on particle-condensate interactions. We sought to discover the biophysical principles that govern particle inclusion in or exclusion from condensates using polymer nanoparticles with tailored surface chemistries as models of macromolecular complexes. Particles coated with polyethylene glycol (PEG) did not partition into condensates. We next leveraged the PEGylated particles as an inert platform to which we conjugated specific adhesive moieties. Particles functionalized with biotin partitioned into condensates containing streptavidin, driven by high-affinity biotin-streptavidin binding. Oligonucleotide-decorated particles exhibited varying degrees of partitioning into condensates, depending on condensate composition. Partitioning of oligonucleotide-coated particles was tuned by altering salt concentration, oligonucleotide length, and oligonucleotide surface density. Remarkably, beads with distinct surface chemistries partitioned orthogonally into immiscible condensates. Based on our experiments, we conclude that arbitrarily large particles can controllably partition into biomolecular condensates given sufficiently strong condensate-particle interactions, a conclusion also supported by our coarse-grained molecular dynamics simulations and theory. These findings may provide insights into how various cellular processes are achieved based on partitioning of large clients into biomolecular condensates, as well as offer design principles for the development of drug delivery systems that selectively target disease-related biomolecular condensates. Significance StatementBiomolecular condensates are subcellular compartments that selectively recruit or exclude client molecules, even though condensates lack an enclosing membrane. Many biochemical reconstitution experiments have investigated mechanisms by which membraneless organelles control partitioning, modeling how cells spatiotemporally recruit components into condensates to regulate cellular functions. One outstanding question is whether partitioning is strictly limited by client size. In this work, we engineered nanoparticles with various sizes and surface functionalities and measured how these variables determine partitioning. We observed controlled and orthogonal partitioning of large particles into several condensate types, driven by strong particle-condensate interactions. Molecular simulations recapitulated key results. Our work advances understanding of how condensate composition is regulated, and our nanoparticle toolbox may also inspire a platform for drug delivery.

biophysics↗

bioRxiv2023

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. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/573113v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@10fc9e1org.highwire.dtl.DTLVardef@18ee2b5org.highwire.dtl.DTLVardef@1c5fcc0org.highwire.dtl.DTLVardef@154b5a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

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). Molecular simulations can reveal the specific amino acid interactions driving LLPS, which is hard to obtain from experiment. Coarse-grained simulations have been used to directly observe the sequence determinants of phase separation but have limited spatial resolution, while all-atom simulations have yet to be applied to LLPS due to the challenges of large system sizes and long time scales relevant to phase separation. We present a novel multiscale computational framework by obtaining initial molecular configurations of a condensed protein-rich phase from equilibrium coarse-grained simulations, and back mapping to an all-atom representation. Using the specialized Anton 2 supercomputer, we resolve microscopic structural and dynamical details of protein condensates through microsecond-scale all-atom explicit-solvent simulations. We have studied two IDPs which phase separate in vitro: the low complexity domain of FUS and the N-terminal disordered domain of LAF-1. Using this approach, we explain the partitioning of ions between phases with low and high protein density, demonstrate that the proteins are remarkably dynamic within the condensed phase, identify the key residue-residue interaction modes stabilizing the dense phase, all while showing good agreement with experimental observations. Our approach is generally applicable to all-atom studies of other single and multi-component systems of proteins and nucleic acids involved in the formation of membraneless organelles.

biophysics↗

bioRxiv2020

A predictive coarse-grained model for position-specific effects of post-translational modifications on disordered protein phase separation

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 forcefields, 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 proteins 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 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. Statement of SignificancePost-translational modifications are important regulators of liquid-liquid phase separation (LLPS) which drives the formation of biomolecular condensates. Theoretical methods can be used to characterize the biophysical properties of intrinsically disordered proteins (IDPs). Our recent framework for molecular simulations using a C-centered coarse-grained model can predict the effect of various perturbations such as mutations (Dignon et al. PloS Comput. Biol, 2018) and temperature (Dignon et al, ACS Cent. Sci., 2019) on LLPS. Here, we expand this framework to incorporate modified residues like phosphothreonine, phosphoserine and acetylysine. This model will prove useful for simulating the phase separation of post-translationally modified IDPs and predicting how position-specific modifications can control phase behavior across the large family of proteins known to be phosphorylated and acetylated.

biophysics↗

bioRxiv2020

Tyrosine phosphorylation regulates hnRNPA2 granule protein partitioning & reduces neurodegeneration

mRNA transport in neurons is a ubiquitous process but has been often overlooked as a contributor to disease. Mutations of transport granule protein hnRNPA2 cause hereditary proteinopathy of neurons, myocytes, and bone. Here, we examine transport granule component specificity, assembly/disassembly, and the link to neurodegeneration. hnRNPA2 transport granule components hnRNPF and ch-TOG interact weakly with hnRNPA2 yet they each partition specifically into hnRNPA2 liquid phases. hnRNPA2 tyrosine phosphorylation dissociates granule interactions by reducing hnRNPA2 phase separation and preventing partitioning of hnRNPF and ch-TOG; tyrosine phosphorylation also decreases aggregation of hnRNPA2 disease mutants. A C. elegans model of hnRNPA2 D290V-associated neurodegeneration exhibits TDP-43 ortholog-dependent glutamatergic neurodegeneration. Expression of the tyrosine kinase that phosphorylates hnRNPA2 reduces glutamatergic neurodegeneration. The evidence for specific partitioning of granule components as well as disruption of these interactions and reduction of neurodegeneration by tyrosine phosphorylation suggest transport granule biology has a role in the pathogenesis of neurodegeneration.

biochemistry↗

bioRxiv2020

Hydropathy patterning complements charge patterning to describe conformational preferences of disordered proteins

Understanding the conformational ensemble of an intrinsically disordered protein (IDP) is of great interest due to its relevance to critical intracellular functions and diseases. It is now well established that the polymer scaling behavior can provide a great deal of information about the conformational properties as well as liquid-liquid phase separation of an IDP. It is, therefore, extremely desirable to be able to predict an IDPs scaling behavior from the protein sequence itself. The work in this direction so far has focused on highly charged proteins and how charge patterning can perturb their structural properties. As naturally occurring IDPs are composed of a significant fraction of uncharged amino acids, the rules based on charge content and patterning are only partially helpful in solving the problem. Here, we propose a new order parameter, sequence hydropathy decoration (SHD), which can provide a near quantitative understanding of scaling and structural properties of IDPs devoid of charged residues. We combine this with a charge patterning parameter, sequence charge decoration (SCD), to obtain a general equation, parameterized from extensive coarse-grained simulation data, for predicting protein dimensions from the sequence. We finally test this equation against available experimental data and find a semi-quantitative match in predicting the scaling behavior. We also provide guidance on how to extend this approach to experimental data, which should be feasible in the near future. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/919498v1_ufig1.gif" ALT="Figure 1"> View larger version (10K): org.highwire.dtl.DTLVardef@1042309org.highwire.dtl.DTLVardef@984e73org.highwire.dtl.DTLVardef@1467afeorg.highwire.dtl.DTLVardef@34f8ec_HPS_FORMAT_FIGEXP M_FIG Graphical TOC Entry C_FIG

biophysics↗

bioRxiv2020

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 bio-inspired 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. By altering the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation, we designed sequences with significantly increased phase separation propensity. 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 in spite of these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance a predictive framework and identify key biophysical principles of sequence features important to phase separation. Significance StatementMembraneless organelles are assemblies of highly concentrated biomolecules that form through a liquid-liquid phase separation process. These assemblies are often enriched in intrinsically disordered proteins, which play an important role in driving phase separation. Understanding the sequence-to-phase behavior relationship of these disordered proteins is important for understanding the biochemistry of membraneless organelles, as well as for designing synthetic organelles and biomaterials. In this work, we explore a model protein, the disordered N-terminal domain of LAF-1, and highlight how three key features of the sequence control the proteins propensity to phase separate. Combining predictive simulations with experiments, we find that phase behavior of this model IDP is dictated by the presence of a short conserved domain, charge patterning, and arginine-tyrosine interactions.

biophysics↗

bioRxiv2019

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. Present in several MLOs, TDP-43 undergoes LLPS and is linked to the pathogenesis of amyotrophic lateral sclerosis (ALS). While some disease variants of 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 in a dimeric 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 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 mini-gene assay. Therefore, TDP-43 helical region serves as a short but uniquely tunable module that shows promise as for controlling assembly and function in cellular and synthetic biology applications of LLPS.

biochemistry↗

bioRxiv2017

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.\n\nAuthor summaryLiquid liquid phase separation (LLPS) of low-complexity protein sequences has emerged as an important research topic due to its relevance to membraneless organelles and intracellular compartmentalization. However a molecular level understanding of LLPS cannot be easily obtained by experimental methods due to difficulty of determining structural properties of phase separated protein assemblies, and of choosing appropriate mutations. Here we advance a coarse-grained computational framework for accessing the long time scale phase separation process and for obtaining molecular details of LLPS, in conjunction with state of the art enhanced sampling methods. We are able to capture qualitatively the changes of phase diagram due to specific mutations, inclusion of a folded domain, and to variation of chain length. The model is flexible and can be used with different knowledge-based potential energy functions, as we demonstrate. We expect a wide application of the presented framework for advancing our understanding of the formation of liquid-like protein assemblies.

biophysics↗