Biology subjects

Hall, C. K.

Publications and source records attributed to Hall, C. K..

4 recordsLinked to original sources

bioRxiv2026

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

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.

biophysics↗

bioRxiv2025

Designed Peptides as Affinity Ligands for Extracellular-Vesicle-based Cancer Biomarker Detection

Assays for cancer diagnosis via the analysis of tumor biomarkers on circulating extracellular vesicles (EVs) have shown great potential. Single EV imaging that can measure the abundance of protein biomarkers in EVs can help in detecting the presence, stage, and progression of disease. Antibodies are typically used to detect EV proteins. Controlling the quality of antibody-based immunoassays can, however, be challenging, as they may exhibit unintended cross-reactivity with non-target proteins or variability in binding affinity across different batches, even for monoclonal antibodies. Here, we report short peptides that are a promising alternative to antibodies for detecting protein biomarkers in EVs. We describe an effort that combines a Peptide Binding Design (PepBD) algorithm and molecular-level simulations to identify peptides that can recognize (1) the extracellular domain of EpCAM (a known cancer biomarker) and (2) the extracellular domain of tetraspanin CD81(a protein commonly expressed on the surface of EVs). The peptides designed for EpCAM and CD81 were labeled with a fluorescent dye and their binding to the target protein was evaluated using fluorescence ELISA. The results demonstrated that one of the computationally designed peptides EP-2.1 exhibited an affinity for the target EpCAM protein comparable to that of an antibody. Further testing involved single EV imaging to gauge the peptides affinity towards EVs. Designed peptides for both the target proteins showed similar affinity as antibodies to EVs.

cancer biology↗

bioRxiv2024

Development of MHC Class I Blocking Peptides to Target Metabolic Dysfunction-Associated Steatohepatitis CD8+ T Cell Activation

MHC class I molecules play a crucial role in the immune system by presenting peptides derived from intracellular proteins to cytotoxic T lymphocytes (CTLs). This process is essential for immune surveillance and eliminating infected or malignant cells. In some diseases, the immune system fails to recognize and eliminate abnormal cells, leading to disease progression. Under conditions of metabolic dysfunction-associated steatohepatitis (MASH), subsets of CD8+ T cells have been identified as pathogenic, leading to inflammation and fibrosis. Therefore, explicitly targeting factors responsible for T cell activation may be necessary to prevent the onset of MASH and future complications such as cirrhosis or hepatocellular carcinoma. We have identified a specific MHC class I antigen that activates hepatic and splenic CD8+ T cells isolated from MASH mice. To specifically target the antigen, we developed two MHC H2-Kb blocking peptides, MHCP3 and MHCP5, that competitively inhibit the Ncf2 peptide from binding to H2-Kb and reduce activation and proliferation of CD8+ T cells. By inhibiting the recognition of specific antigens, these blocking peptides may prevent the activation of CD8+ T cells and progression of MASH.

immunology↗

bioRxiv2023

Design of parallel ????-sheet nanofibrils using Monte-Carlo search, coarse-grained simulations, and experimental testing

Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously-established computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into "parallel {beta}-sheets", i.e., {beta}-sheets with N-terminus-to-C-terminus {beta}-strand vectors oriented in parallel. To accomplish the desired {beta}-strand organization, we applied the PepAD amino acid sequence design software to the Class-1 cross-{beta} spine defined by Sawaya et al. This molecular configuration includes two layers of parallel {beta}-sheets stacked such that N-terminus-to-C-terminus vectors are oriented antiparallel for molecules on adjacent {beta}-sheets. The first cohort of PepAD identified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7-mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril-like structures with a predominantly parallel {beta}-sheet arrangement, two formed fibril-like structure with <50% in parallel {beta}-sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallel {beta}-sheets based on Fourier Transform Infrared Spectroscopy, Circular Dichroism, Electron Microscopy, and Thioflavin-T fluorescence spectroscopy measurements.

bioengineering↗