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Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes.

Shadi FerdosiAlexey StukalovMoaraj HasanBehzad TangeyshTristan R BrownTianyu WangEltaher M ElgierariXiaoyan ZhaoYingxiang HuangAmir AlaviBrittany Lee-McMullenJessica ChuMike FigaWei TaoJian WangMartin GoldbergEvan S O'BrienHongwei XiaCraig StolarczykRalph WeisslederVivek FariasSerafim BatzoglouAsim SiddiquiOmid C FarokhzadDaniel Hornburg
Published in: Advanced materials (Deerfield Beach, Fla.) (2022)
Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.
Keyphrases
  • protein protein
  • binding protein
  • amino acid
  • small molecule
  • high throughput
  • microbial community
  • transcription factor
  • optical coherence tomography
  • single cell
  • deep learning
  • big data
  • data analysis