Biology subjects

Hurst, M.

Publications and source records attributed to Hurst, M..

3 recordsLinked to original sources

bioRxiv2025

The pKa values of buried ionizable amino acids can be determined by the thermodynamic stability of the protein

Ionizable groups in hydrophobic environments in proteins usually titrate with anomalous pKa values. The ionization of these buried residues is often coupled to transitions between conformational states. Here we test the hypothesis that because the thermodynamic stability of a protein ({Delta}G{degrees}H2O) determines the probability of conformational transitions, the apparent pKa values of buried residues can be governed by {Delta}G{degrees}H2O. Variants of staphylococcal nuclease (SNase) with either Lys-66 or Lys-92 buried in its hydrophobic interior were engineered along with surface mutations that alter {Delta}G{degrees}H2O without affecting the electrostatic properties of the internal microenvironments of the buried Lys residues. The measured pKa values of these Lys residues largely correlates with {Delta}G{degrees}H2O. NMR spectroscopy was used to demonstrate that the structural changes of the protein backbone coupled to the ionization of Lys-66 or Lys-92 are comparable regardless of the {Delta}G{degrees}H2O of the protein. NMR spectroscopy confirmed that global unfolding of the Lys-92 variant coincides with the apparent pKa of the Lys side chain. The data presented show that the anomalous pKa values measured for internal residues in proteins do not necessarily report on local dielectric or electrostatic properties of the microenvironments around the ionizable group; rather, they can report on the energetics of pH-driven conformational transitions. These data suggest that accurate structure-based calculation of pKa values will require de novo prediction of partially unfolded conformations, and accurate calculation of free energy differences between conformational states, both of which remain formidable challenges.

biophysics↗

bioRxiv2025

Adaptive Machine Learning Framework enables Unprecedented Yield and Purity of Adeno-Associated Viral Vectors for Gene Therapy

Adeno-associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, optimizing viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients. Traditional optimization methods are resource-intensive and often fail to adjust the purification process parameters to maximize the resulting product yield and quality. To address this challenge, we developed a machine learning framework that leverages Bayesian optimization to systematically refine affinity chromatography parameters (sample load, flow rate, and the formulation of chromatographic media) to improve AAV purification. The efficiency of this closed-loop workflow in iteratively optimizing the vectors yield, purity, and transduction efficiency was demonstrated by purifying clinically-relevant serotypes AAV2, AAV5, and AAV9 from HEK293 cell lysates using the affinity adsorbent AAVidity. We show that three cycles of Bayesian optimization elevated yields from a baseline of 70% to 99%, while reducing host-cell impurities by 230-to-400-fold across all serotypes. The optimized parameters consistently produced vectors with high purity and preserved high transduction activity, essential for therapeutic efficacy and safety, demonstrating serotype versatility - a key challenge in AAV manufacturing. By streamlining parameter optimization and enhancing productivity, our adaptive machine learning framework accelerates process development and reduces costs, advancing the accessibility and clinical translation of AAV-based gene therapies.

molecular biology↗

bioRxiv2023

Variability in the phytoplankton response to upwelling across an iron limitation mosaic within the California Current System

Coastal upwelling currents such as the California Current System (CCS) comprise some of the most productive biological systems on the planet. Diatoms, a distinct taxon of phytoplankton, dominate these upwelling events in part due to their rapid response to nutrient entrainment. In this region, they may also be limited by the micronutrient iron (Fe), an important trace element primarily involved in photosynthesis and nitrogen assimilation. The mechanisms behind how diatoms physiologically acclimate to the different stages of the upwelling conveyor belt cycle with respect to Fe limitation remains largely uncharacterized. Here, we explore their physiological and metatranscriptomic response to the upwelling cycle with respect to the Fe limitation mosaic that exists in the CCS. Subsurface, natural plankton assemblages that would potentially seed surface blooms were examined over wide and narrow shelf regions. The initial biomass and physiological state of the phytoplankton community had a large impact on the overall response to simulated upwelling. Following on-deck incubation under varying Fe physiological states, our results suggest that diatoms quickly dominated the blooms by "frontloading" nitrogen assimilation genes prior to upwelling. However, diatoms subjected to induced Fe limitation exhibited reductions in carbon and nitrogen uptake and decreasing biomass accumulation. Simultaneously, they exhibited a distinct gene expression response which included increased expression of Fe-starvation induced proteins and decreased expression of nitrogen assimilation and photosynthesis genes. These findings may have significant implications for upwelling events in future oceans, where changes in ocean conditions are projected to amplify the gradient of Fe limitation in coastal upwelling regions.

plant biology↗