About

Carol Hall is the H. Clark Worley Distinguished University Professor in Engineering at NC State University, affiliated with the Department of Chemical and Biomolecular Engineering. Her research is centered on understanding how macroscopic properties reflect molecular behavior, with a particular focus on molecules of interesting architectures and energetics. She employs statistical thermodynamics and computer simulation as primary tools to model the self-assembly of soft materials such as colloids, gels, lipids, and surfactants, as well as proteins involved in neurodegenerative diseases like Alzheimer’s. Her work includes developing models of polysaccharides like chitosan for applications in oil spill remediation and drug delivery, and designing short peptides to bind specific biomolecular targets including RNA and proteins. Hall’s research extends to investigating protein aggregation processes associated with neurodegenerative diseases, utilizing coarse-grained models and molecular dynamics simulations to study fibril formation and oligomerization. She collaborates with experimental biophysicists and chemists to simulate early stages of peptide aggregation and evaluate inhibitors. Additionally, she develops computational algorithms for peptide design aimed at creating biosensors, diagnostics, and therapeutics, such as peptides that recognize specific tRNA species or serve as high-sensitivity detectors for cardiac biomarkers. Her work is informed by interactions with experimental colleagues at NC State and internationally, contributing to advancements in soft material science, biomolecular interactions, and peptide engineering.

Research topics

• Chemistry
• Biochemistry
• Biology
• Medicine
• Biophysics
• Nanotechnology
• Chromatography
• Crystallography
• Materials science
• Neuroscience
• Pathology

Selected publications

• Validation of <i>De Novo</i> Designs of Solid-Binding Peptides

  ACS Central Science · 2026-05-22

  articleOpen accessCorresponding

  Solid-binding peptides (SBPs) are versatile molecules that can control a range of atomic-scale interfacial processes, but they remain challenging to discover. Current approaches for discovery rely on directed evolution, which samples only a small fraction of possible sequences. Data-driven methods for therapeutic peptides are also not applicable as they rely on crystal structures whereas peptides adopt varied conformations at solid surfaces. To address this challenge, we recently combined biophysical modeling and machine learning to design plastic-binding peptides that were predicted to have strong adsorption enthalpies. Here, we evaluate these designs using steered molecular dynamics and single-molecule force measurements and identify de novo designed peptides that bind strongly to polyethylene. Experimental adhesion forces exceed those previously reported for SBPs, and adsorption free energies from metadynamics simulations support strong binding. Analysis of the designed peptides reveals blocks of non-polar and charged residues, which enables them to adopt conformations that segregate non-polar amino acids to the plastic surfaces (generating hydrophobic interactions) and charged amino acids away from the surfaces. The contact patterns within the non-polar blocks depend on sequence context and polyolefin type. Overall, we validate a general approach for de novo SBP discovery that has broad scientific and engineering applications.

  PublisherOA PDFDOI

• Development of Peptide Glucosyltransferase Inhibitors With Comprehensive Coverage Across Clostridioides difficile Toxin B Sub‐Types

  Open MIND · 2026-01-01

  articleSenior author

  Clostridioides difficile infection presents an escalating clinical challenge due to the proliferation of hypervirulent and antibiotic-resistant strains. The primary symptoms of disease, namely colitis and diarrhea, are induced by the release of two toxins: TcdA and TcdB. Targeting these toxins with peptide inhibitors provides an attractive therapeutic strategy that can be used alone or synergistically with standard antibiotic treatments to alleviate severe symptoms and reduce the risk of resistance development. In this study, we present the rational discovery and optimization of potent TcdB peptide inhibitors. The lead sequences effectively inhibit TcdB glucosyltransferase activity, the crucial enzymatic process leading to disease symptoms, by directly competing with the toxin's molecular targets, Rho proteins. Detailed enzymatic studies also elucidate distinct Michaelis constants, K_M, for each substrate, UDP-glucose and Rho-proteins, for multiple TcdB GTD subtypes. The selected peptides demonstrated broad efficacy against the three most common TcdB subtypes, which are used in over 90% of clinical isolates. Additionally, the peptides delayed TcdB-induced loss of barrier integrity and decreased apoptosis in a primary human colon epithelial monolayer model. This study highlights a novel therapeutic avenue with significant potential to enhance the treatment and management of C. difficile infections.

• Development of Peptide Glucosyltransferase Inhibitors With Comprehensive Coverage Across Clostridioides difficile Toxin B Sub‐Types

  UNC Libraries · 2026-02-06

  articleOpen accessSenior author

  Clostridioides difficile infection presents an escalating clinical challenge due to the proliferation of hypervirulent and antibiotic-resistant strains. The primary symptoms of disease, namely colitis and diarrhea, are induced by the release of two toxins: TcdA and TcdB. Targeting these toxins with peptide inhibitors provides an attractive therapeutic strategy that can be used alone or synergistically with standard antibiotic treatments to alleviate severe symptoms and reduce the risk of resistance development. In this study, we present the rational discovery and optimization of potent TcdB peptide inhibitors. The lead sequences effectively inhibit TcdB glucosyltransferase activity, the crucial enzymatic process leading to disease symptoms, by directly competing with the toxin's molecular targets, Rho proteins. Detailed enzymatic studies also elucidate distinct Michaelis constants, K_M, for each substrate, UDP-glucose and Rho-proteins, for multiple TcdB GTD subtypes. The selected peptides demonstrated broad efficacy against the three most common TcdB subtypes, which are used in over 90% of clinical isolates. Additionally, the peptides delayed TcdB-induced loss of barrier integrity and decreased apoptosis in a primary human colon epithelial monolayer model. This study highlights a novel therapeutic avenue with significant potential to enhance the treatment and management of C. difficile infections.

  PublisherDOI

• Designed peptides as affinity ligands for extracellular-vesicle-based cancer diagnosis

  Biosensors and Bioelectronics · 2026-03-03

  articleSenior authorCorresponding
  PublisherDOI

• In silico peptide self‐assembly reveals the importance of N‐terminal motifs and the inhibition mechanism of the mutation <scp>L38M</scp> in <scp> <i>α</i> </scp> ‐synuclein fibrillation

  Protein Science · 2026-03-12

  articleOpen accessSenior authorCorresponding

  Alpha-synuclein (αSyn) is a presynaptic protein associated with several neurodegenerative diseases. While the non-amyloid component (NAC) region of the αSyn sequence (residues 65-90) forms the core of all αSyn fibrils, recent findings suggest that the flanking regions play a key role in initiating or preventing amyloid formation. Two motifs in the N-terminal region, named P1 (αSyn [36-42]) and P2 (αSyn [45-57]), have been shown to be key modulators of fibril formation, with deletion of these regions or single-point mutations in the P1 region inhibiting amyloid formation of full-length αSyn. In this study, we use the coarse-grained molecular dynamics package DMD/PRIME20 to simulate the self-assembly of the P1 and P2 regions, alongside longer segments P3 (αSyn [36-57]) and P3Next (αSyn [27-57]), and single-point mutations: focusing primarily on L38M, L38A, and V40A, and additionally examining Y39A and S42A as secondary variants, all of which have different effects on fibril formation of the full-length protein in vitro. The results show that P1, P2 and P3 have a high propensity to form parallel β-sheets while P3Next tends to form β-hairpins within fibrillar structures. The L38M substitution reduces the formation of both parallel β-sheets and β-hairpins, consistent with the inability of full-length αSyn containing L38M to form amyloid fibrils in vitro at neutral pH and to aggregate in vivo in Caenorhabditis elegans. In contrast, simulations of L38A and V40A show no such effect, consistent with their minimal impact on full-length αSyn fibrillation. The simulation results suggest that the presence of P1/P2 hairpins are required to unleash the amyloid potential of αSyn and offer a structural explanation of how L38M mutation in this region protects the protein from amyloid formation.

  PublisherDOI

• Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-01 · 1 citations

  articleOpen access

  Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

  PublisherDOI

• Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution

  PNAS Nexus · 2025-01-27 · 6 citations

  articleOpen access

  Methods are needed to mitigate microplastic (MP) pollution to minimize their harm to the environment and human health. Given the ability of polypeptides to adsorb strongly to materials of micro- or nanometer size, plastic-binding peptides (PBPs) could help create bio-based tools for detecting, filtering, or degrading MNP pollution. However, the development of such tools is prevented by the lack of PBPs. In this work, we discover and evaluate PBPs for several common plastics by combining biophysical modeling, molecular dynamics (MD), quantum computing, and reinforcement learning. We frame peptide affinity for a given plastic through a Potts model that is a function of the amino acid sequence and then search for the amino acid sequences with the greatest predicted affinity using quantum annealing. We also use proximal policy optimization to find PBPs with a broader range of physicochemical properties, such as isoelectric point or solubility. Evaluation of the discovered PBPs in MD simulations demonstrates that the peptides have high affinity for two of the plastics: polyethylene and polypropylene. We conclude by describing how our computational approach could be paired with experimental approaches to create a nexus for designing and optimizing peptide-based tools that aid the detection, capture, or biodegradation of MPs. We thus hope that this study will aid in the fight against MP pollution.

  PublisherDOI

• Author response for "AI-driven rational design of promiscuous and selective plastic-binding peptides"

  2025-09-15

  peer-review
  PublisherDOI

• Discontinuous molecular dynamics simulations in an external field: Application to two-dimensional ferrofluids

  Physical review. E · 2025-01-07 · 3 citations

  articleSenior author

  We introduce a stochastic method for simulating the effect of an external magnetic field on coarse-grained models of magnetic colloids for use in discontinuous molecular dynamics (DMD) simulations. Our method for simulating an external field is illustrated with a coarse-grained model for magnetic squares in two dimensions. Square-shaped particles are represented as four disks bonded together in a 2×2 lattice configuration to create a hard colloidal geometry. Two opposite charges are embedded within the square to mimic the magnetic interactions between particles. The method for simulating an external field stochastically during DMD simulations operates by applying impulses randomly to the charges embedded within each square particle. When one square experiences an interaction with the field, each embedded charge within the square is assigned a new momentum with a specific magnitude and orientation. The magnitude of this momentum is equal to the average of a Maxwell-Boltzmann distribution at the simulation temperature. The orientation of the momentum depends on the charge, either positive or negative, and points either in the same or opposite direction as the field, respectively. The strength of the external field is determined by the average frequency at which the particles experience interactions with the field. The relationship between the stochastic frequency of the field and the field strength is derived from Newton's equation of motion. DMD simulations are performed for large systems of magnetic square particles at various temperatures and external field strengths. The simulation temperature is maintained constant with an Andersen thermostat, while the external field is simulated stochastically, as described above. We find that our simulation techniques reproduce a net system magnetization in close agreement with the two-dimensional equivalent of the Langevin function, while maintaining the simulation temperature constant.

  PublisherDOI

• Modulating peptide co-assembly <i>via</i> macromolecular crowding: Recipes for co-assembled structures

  Nanoscale · 2025-01-01 · 2 citations

  articleOpen accessSenior author

  Peptide-based biomaterials are commonly found in applications such as tissue engineering, wound healing, and drug delivery. Control over the size and morphology of the peptide supramolecular structure remains a challenge. One way to influence peptide assembly is through macromolecular crowding. Here we use discontinuous molecular dynamics simulation combined with the PRIME20 force field to investigate the effect of hydrophobic crowders on the architecture of co-assembled peptide aggregates. The peptide system used in this work is a mixture of oppositely-charged synthetic peptides: "CATCH(6K+)" (KQKFKFKFKQK) and "CATCH(6E-)" (EQEFEFEFEQE). The systems explored contained a mixture of 50 CATCH(6K+) and 50 CATCH (6E-) peptides at peptide concentrations of 5 mM and 20 mM, and crowders with diameters of 10, 20, 40 and 80 Å. Crowders were modeled as spheres with either hard-sphere or square-well/square-shoulder interactions. At low concentrations where CATCH co-assembly typically does not occur, the crowders were effective chaperones to trigger co-assembly. Small hard-sphere crowders promoted formation of multilayer fibrils. Large highly hydrophobic crowders promoted the formation of monolayer β-sheet structures and suppressed the formation of fibril structures. Overall, the simulations demonstrate that the crowder size and crowder-sidechain interaction strength govern the supramolecular architecture of peptide co-assemblies.

  PublisherOA PDFDOI

Recent grants

• EAGER: Computational Design of Peptide Ligands for the Bioseparation of "Fab" Antibody Fragments

  NSF · $120k · 2018–2020

• Element: Computational Toolkit to Discover Peptides that Self-assemble into User-selected Structures

  NSF · $600k · 2019–2025

• CDI Type II Computational Discovery of Unusual Nucleic-Acid-Based Nanostructures

  NSF · $1.5M · 2008–2014

• Molecular Recognition in Microarrays: A Computer Simulation Study

  NSF · $200k · 2006–2008

• NIH Grant R01GM040023

  NIH · $294k · 1991

Frequent coauthors

• Jan Genzer

  North Carolina State University

  27 shared

• Richard J. Spontak

  North Carolina State University

  25 shared

• Xingqing Xiao

  North Carolina State University

  23 shared

• Elizabeth A. Wilder

  Procter & Gamble (United States)

  21 shared

• Yiming Wang

  California University of Pennsylvania

  20 shared

• G. Stell

  Stony Brook University

  18 shared

• David A. Agard

  University of California, San Francisco

  16 shared

• Hiroshi Jinnai

  Tohoku University

  16 shared

Labs

• Hall GroupPI

  N/A

Education

• Ph. D. , Physics

  Stony Brook University

  1972

• BA, Physics

  Cornell University

  1967

Awards & honors

• Worley H. Clark, Jr. Distinguished University Professor in E…