Full-length protein classification via cysteine fingerprinting in solid-state nanopores

Area of Science:

• Biophysics
• Nanotechnology
• Molecular Biology

Background:

• Single-molecule technologies are revolutionizing protein analysis.
• Solid-state nanopores enable protein threading but face challenges in slowing translocation for accurate fingerprinting.

Purpose of the Study:

• To develop a method for slowing protein translocation through nanopores for enhanced single-molecule analysis.
• To create a robust protein identification and classification technique using nanopore technology.

Main Methods:

• Covalent attachment of short oligonucleotides to denatured proteins via click chemistry.
• Utilizing negatively charged oligonucleotide tags to increase capture rate and induce 'stick-slip' motion for slower translocation.
• Employing all-atom molecular dynamics and finite element simulations to model translocation dynamics.
• Applying a supervised machine learning classifier for protein identification based on ion current pulses.

Main Results:

• Oligonucleotide tags increased protein capture rate tenfold and slowed translocation more than 20-fold.
• Characteristic ion current pulses generated by tagged proteins served as unique, time-resolved signatures.
• Near-perfect classification accuracy achieved with a small number of translocation events.
• Successfully distinguished between VEGF-A isoforms (VEGF-165 and VEGF-121) in a mixed sample.

Conclusions:

• The developed nanopore fingerprinting technique offers a rapid, direct, and cost-effective method for single-molecule protein identification.
• This approach eliminates the need for affinity reagents or motor proteins, simplifying protein analysis.
• The method shows promise for applications like cancer diagnostics through precise protein isoform discrimination.