Enhancing Protein Crystal Nucleation Using In Situ Templating on Bioconjugate-Functionalized Nanoparticles and Machine Learning

ACS Appl Mater Interfaces. 2023 Mar 15;15(10):12622-12630. doi: 10.1021/acsami.2c17208. Epub 2023 Feb 28.

Abstract

Although protein crystallization offers a promising alternative to chromatography for lower-cost protein purification, slow nucleation kinetics and high protein concentration requirements are major barriers for using crystallization as a viable strategy in downstream protein purification. Here, we demonstrate that nanoparticles functionalized with bioconjugates can result in an in situ template for inducing rapid crystallization of proteins at low protein concentration conditions. We use a microbatch crystallization setup to show that the range of successful crystallization conditions is expanded by the presence of functionalized nanoparticles. Furthermore, we use a custom machine learning-enabled emulsion crystallization setup to rigorously quantify nucleation parameters. We show that bioconjugate-functionalized nanoparticles can result in up to a 7-fold decrease in the induction time and a 3-fold increase in the nucleation rate of model proteins compared to those in control environments. We thus provide foundational insight that could enable crystallization to be used in protein manufacturing by reducing both the protein concentration and the time required to nucleate protein crystals.

Keywords: bioconjugates; downstream processing; machine learning; nanoparticles; nucleation; protein crystallization.

MeSH terms

  • Crystallization / methods
  • Kinetics
  • Nanoparticles*
  • Proteins* / chemistry

Substances

  • Proteins