About

Dr. Gregory Dignon is an Assistant Professor in the Department of Chemical and Biochemical Engineering at Rutgers University. He joined Rutgers after completing his postdoctoral fellowship at Stony Brook University in the group of Ken Dill, where he worked on physical and quantitative biology. His research focuses on understanding biomolecular interactions with the goal of designing new and novel biotechnology. His lab utilizes computational techniques such as molecular dynamics simulations and statistical mechanics to investigate molecular engineering, biomolecular folding and interactions, biomolecular phase separation, intrinsically disordered proteins, drug design, and drug delivery. Dr. Dignon received his Ph.D. from Lehigh University under the supervision of Prof. Jeetain Mittal and his bachelor's degree from Rensselaer Polytechnic Institute, where he conducted research supervised by Prof. Peter Tessier. His professional experience includes a postdoctoral position at Stony Brook University and academic appointments at Rutgers. His honors include peer review contributions to prominent scientific journals, participation in the CASP14 Blind Prediction Challenge, and awards such as the PLoS Computational Biology Research Prize for Exemplary Methods/Software. He is a member of several professional societies, including AIChE, ACS, BPS, and APS.

Research topics

• Computer Science
• Machine Learning
• Chemistry
• Materials science
• Biochemistry
• Chemical physics
• Biology
• Biophysics
• Physics
• Nanotechnology
• Crystallography
• Operating system
• Library science
• Quantum mechanics
• Mathematics
• Computational science
• Cell biology
• Computational chemistry
• Statistical physics

Selected publications

• Analysis and design of disordered polypeptides with optimized sequence patterning properties

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-20

  articleOpen accessSenior authorCorresponding

  Abstract 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 ΔG 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 summary Intrinsically 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 it’s “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.

  PublisherOA PDFDOI

• BPS2025 - The approach of multi-scale modeling for understanding and addressing the pathogenesis of multiple sclerosis

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

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

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-07

  preprintOpen accessSenior authorCorresponding

  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 Statement: Understanding 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.

  PublisherOA PDFDOI

• Controlled and orthogonal partitioning of large particles into biomolecular condensates

  Nature Communications · 2025-04-14 · 21 citations

  articleOpen access

  Partitioning of client molecules into biomolecular condensates is critical for regulating the composition and function of condensates. Previous studies suggest that client size limits partitioning. Here, we ask whether large clients, such as macromolecular complexes and nanoparticles, can partition into condensates based on particle-condensate interactions. We seek to discover the fundamental biophysical principles that govern particle inclusion in or exclusion from condensates, using polymer nanoparticles surface-functionalized with biotin or oligonucleotides. Based on our experiments, coarse-grained molecular dynamics simulations, and theory, we conclude that arbitrarily large particles can controllably partition into condensates given sufficiently strong condensate-particle interactions. Remarkably, we also observe that beads with distinct surface chemistries partition orthogonally into immiscible condensates. These findings may provide insights into how various cellular processes are achieved based on partitioning of large clients into biomolecular condensates, and they offer design principles for drug delivery systems that selectively target disease-related condensates.

  PublisherOA PDFDOI

• Current practices in the study of biomolecular condensates: a community comment

  Nature Communications · 2025-08-19 · 54 citations

  articleOpen access

  The realization that the cell is abundantly compartmentalized into biomolecular condensates has opened new opportunities for understanding the physics and chemistry underlying many cellular processes1, fundamentally changing the study of biology2. The term biomolecular condensate refers to non-stoichiometric assemblies that are composed of multiple types of macromolecules in cells, occur through phase transitions, and can be investigated by using concepts from soft matter physics3. As such, they are intimately related to aqueous two-phase systems4 and water-in-water emulsions5. Condensates possess tunable emergent properties such as interfaces, interfacial tension, viscoelasticity, network structure, dielectric permittivity, and sometimes interphase pH gradients and electric potentials6–14. They can form spontaneously in response to specific cellular conditions or to active processes, and cells appear to have mechanisms to control their size and location15–17. Importantly, in contrast to membrane-enclosed organelles such as mitochondria or peroxisomes, condensates do not require the presence of a surrounding membrane.

  PublisherOA PDFDOI

• Hydration free energy is an incomplete predictor of globular protein incorporation into condensates

  Biophysical Journal · 2025-12-01 · 1 citations

  articleOpen accessSenior author

  Membraneless organelles (MLOs) are assemblies of biomolecules that 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 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 liquid-liquid phase separation in search of predicting equilibrium concentrations and materials properties of condensates. In this work, we provide a simple computational approach that may 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 green fluorescent protein variants and find that hydration free energy is relatively well-correlated with the partition coefficient of these proteins into FG nucleoporin 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.

  PublisherDOI

• Phosphorylation toggles the SARS-CoV-2 nucleocapsid protein between two membrane-associated condensate states

  Nature Communications · 2025-08-26 · 8 citations

  articleOpen access

  The Nucleocapsid protein (N) of SARS-CoV-2 plays a critical role in the viral lifecycle by regulating RNA replication and by packaging the viral genome. N and RNA phase separate to form condensates that may be important for these functions. Both functions occur at membrane surfaces, but how N toggles between these two membrane-associated functional states is unclear. Here, we reveal that phosphorylation switches how N condensates interact with membranes, in part by modulating condensate material properties. Our studies also show that phosphorylation alters N's interaction with viral membrane proteins. We gain mechanistic insight through structural analysis and molecular simulations, which suggest phosphorylation induces a conformational change in N that softens condensate material properties. Together, our findings identify membrane association as a key feature of N condensates and provide mechanistic insights into the regulatory role of phosphorylation. Understanding this mechanism suggests potential therapeutic targets for COVID infection.

  PublisherOA PDFDOI

• Uncovering the driving forces behind partitioning of proteins into phase-separated granules

  Biophysical Journal · 2024-02-01

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Proteins in phase: Unveiling the role of IDPs and structured proteins in biomolecular condensates

  Biophysical Journal · 2024-02-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Controlled and orthogonal partitioning of large particles into biomolecular condensates

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-07-16 · 7 citations

  preprintOpen accessCorresponding

  Abstract 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 Statement Biomolecular 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.

  PublisherDOI

Frequent coauthors

• Jeetain Mittal

  Texas A&M University

  39 shared

• Nicolas L. Fawzi

  Providence College

  29 shared

• Young C. Kim

  Naval Research Laboratory Materials Science and Technology Division

  27 shared

• Wenwei Zheng

  22 shared

• Robert B. Best

  National Institute of Diabetes and Digestive and Kidney Diseases

  18 shared

• Alexander E. Conicella

  John Brown University

  14 shared

• Gül H. Zerze

  University of Houston

  11 shared

• Roshan Mammen Regy

  Lehigh University

  9 shared

Awards & honors

• John C. Chen Endowed Fellowship from the Chemical Engineerin…
• PLoS Computational Biology Research Prize for Exemplary Meth…
• CASP14 Blind Prediction Challenge: participated with Dill la…
• 1st Place Best Student Talk: Midwest Thermodynamics and Stat…
• Awarded travel grant for EMBL Symposium: Cellular Mechanisms…