Chop-n-drop: in silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. However whether such fragmentation patterns are sufficiently characteristic of proteins to identify them in complex samples remained unclear. In the simulation study presented here, we show that 97.9% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 78.8% can still be identified if the resolution is twice as low as currently attainable and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof-of-concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.