@article {Sampath024158, author = {G. Sampath}, title = {A digital approach to protein identification and quantity estimation using tandem nanopores, peptidases, and database search}, elocation-id = {024158}, year = {2015}, doi = {10.1101/024158}, publisher = {Cold Spring Harbor Laboratory}, abstract = {A digital approach to protein identification and quantity estimation using electrical measurements and database search is proposed. It is based on an electrolytic cell with two (three) nanopores and one (two) peptidase(s) covalently attached to the trans side of a pore. An unknown protein is digested by a reagent or peptidase into peptides ending in a known amino acid; the peptides enter the cell, pass through the first pore, and are fragmented by a high-specificity endopeptidase. The second enzyme, if present, is an exopeptidase that cleaves the fragments into single residues after the second pore. Level transitions in an ionic blockade or transverse current pulse due to residues in a fragment or individual pulses due to single residues are counted. This yields the positions of the endopeptidase{\textquoteright}s target in the peptide, and, together with the peptide{\textquoteright}s terminal residue, a partial sequence. Search through the Uniprot database for such sequences identifies over 90\% of the proteins in the human proteome. The percentage can be increased by repeating the procedure with other reagents and cells specific to other residues, close to 100\% may be possible. Sample purification to homogeneity is not required as the method applies to an arbitrary mixture of proteins; the quantity of a protein in the sample is estimated from the number of identifying peptides sensed over a long run. A Fokker-Planck model gives minimum enzyme turnover intervals required for ordered sensing of peptide fragments. With thick (80-100 nm) pores, required pulse resolution times are within the capability of CMOS detectors. The method can be implemented with existing technology; several related issues are discussed.}, URL = {https://www.biorxiv.org/content/early/2015/09/08/024158}, eprint = {https://www.biorxiv.org/content/early/2015/09/08/024158.full.pdf}, journal = {bioRxiv} }