Single-molecule protein identification by sub-nanopore sensors

• Published in: PLoS computational biology Vol. 13; no. 5; p. e1005356
• Main Authors: Kolmogorov, Mikhail, Kennedy, Eamonn, Dong, Zhuxin, Timp, Gregory, Pevzner, Pavel A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Bacteria; Biology and Life Sciences; Colleges & universities; Computer engineering; Computer science; Databases, Protein; Deoxyribonucleic acid; DNA; DNA sequencing; Electric currents; Electrical engineering; Fragmentation; Gene sequencing; Identification; Ions; Mass spectrometry; Mass spectroscopy; Matching; Methods; Nanopores; Nanostructure; Nanotechnology - methods; Observations; Physical Sciences; Porosity; Protein-protein interactions; Proteins; Proteins - chemistry; Proteins - classification; Proteomics; Research and Analysis Methods; Scientific imaging; Sensitivity; Sensors; Silicon nitride; Software; Technology; Translocation; La Jolla California; California; Indiana; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005356

Recent advances in top-down mass spectrometry enabled identification of intact proteins, but this technology still faces challenges. For example, top-down mass spectrometry suffers from a lack of sensitivity since the ion counts for a single fragmentation event are often low. In contrast, nanopore technology is exquisitely sensitive to single intact molecules, but it has only been successfully applied to DNA sequencing, so far. Here, we explore the potential of sub-nanopores for single-molecule protein identification (SMPI) and describe an algorithm for identification of the electrical current blockade signal (nanospectrum) resulting from the translocation of a denaturated, linearly charged protein through a sub-nanopore. The analysis of identification p-values suggests that the current technology is already sufficient for matching nanospectra against small protein databases, e.g., protein identification in bacterial proteomes.