Single-molecule protein identification by sub-nanopore sensors

2017

Preprint

Abstract. Protein sequences are recoded with a binary alphabet obtained by dividing the 20 amino acids into two subsets based on volume. A protein is identified from subsequences by database search. Computations on the Helicobacter pylori proteome show that over 93% of binary subsequences of length 20 are correct at a confidence level exceeding 90%. Over 98% of the proteins can be identified, most have multiple identifiers so the false detection rate is low. Binary sequences of unbroken protein molecules can be obtained with a nanopore from current blockade levels proportional to residue volume; only two levels, rather than 20, need be measured to determine a residue's subset. This procedure can be translated into practice with a sub-nanopore that can measure residue volumes with ~0.07 nm 3 resolution as shown in a recent publication. The high detector bandwidth required by the high speed of a translocating molecule can be reduced more than tenfold with an averaging technique, the resulting decrease in the identification rate is only 10%. Averaging also mitigates the homopolymer problem due to identical successive blockade levels. The proposed method is a proteolysis-free single-molecule method that can identify arbitrary proteins in a proteome rather than specific ones.

Section: The Homopolymer Problemmentioning

confidence: 84%

“…Such resolution is unattainable in practice, especially with noise present. This is mitigated somewhat if a four-way division of the 20 amino acids based on volume is used [13].…”

mentioning

confidence: 99%

Protein identification with a nanopore and a binary alphabet

2017

Preprint

“…Assume that the size of the current blockade IAN due to an analyte molecule AN is roughly proportional to the volume excluded when passing through the pore [7,21]. Then…”

Section: Current Blockade As a Proxy For Analyte Volumementioning

confidence: 99%

Label-free amino acid identification for de novo protein sequencing via tRNA charging and current blockade in a nanopore

2020

Preprint

Label-free identification of amino acids (AAs) based on superspecific transfer RNAs (tRNAs) and amino-acyl tRNA synthetase (AARS) is explored. Molecules of a specific AA, a tRNA and its cognate AARS, and adenosine triphosphate (ATP) from a reservoir are confined to a micro-sized cavity by hydraulic pressure to counter the effects of diffusion. In this confined space a cognate tRNA in the cavity gets charged with AA and adenosine monophosphate (AMP) is released. The products are transferred to an electrolytic cell with a nanopore where AA, AMP, and ATP cause current blockades of different sizes. If an AMP-sized blockade occurs it means that a tRNA molecule has been charged with AA, this identifies AA; otherwise the procedure is repeated with a different tRNA and AARS until AA is identified. Unlike in most other nanopore-based methods, there is no need for precise measurement of current blockade levels. The efficacy of the procedure is assessed by simulating the movement of particles in a reservoir-cavity structure and analyzing the blockades that occur in the nanopore. In the former the probability of reactants escaping the cavity is found to be near 0. In the latter the probability of an error in identifying an AMP blockade is found to be less than 10% in the worst case (which occurs with the largest volume AA, namely Tryptophan). Detailed information and data are provided in a Supplementary File.

“…Amino acids vary in volume [1] but in most cases the differences are small. In [2] they are coarsely partitioned into four subsets labeled Minuscule, Small, Intermediate, and Large. These subsets are encoded here with the reduced alphabet {B, U, X, Z}.…”

Section: A Partition Based On a Reduced Amino Acid Alphabetmentioning

confidence: 99%

“…In [2] peptide sequencing is based on using a reduced amino acid alphabet with four characters and mapping current blockades caused by residues to it. Post-processing of signal data with four measurable levels of discrimination uses a hidden Markov model (HMM) to construct the reverse map from reduced alphabet to full alphabet and the peptide sequence therefrom.…”

Section: A-2 Peptide Sequencing In Practicementioning

confidence: 99%

Peptide partitions and protein identification: a computational analysis

2016

Preprint

Peptide sequences from a proteome can be partitioned into N mutually exclusive sets and used to identify their parent proteins in a sequence database. This is illustrated with the human proteome (http://www.uniprot.org; id UP000005640), which is partitioned into eight subsets KZ and Z * ≡ 0 or more occurrences of Z. If the full peptide sequence is known then over 98% of the proteins in the proteome can be identified from such sequences. The rate exceeds 78% if the positions of four internal residue types are known. When the standard set of 20 amino acids is replaced with an alphabet of size four based on residue volume the identification rate exceeds 96%. In an information-theoretic sense this last result suggests that protein sequences effectively carry nearly the same amount of information as the exon sequences in the genome that code for them using an alphabet of size four. An appendix discusses possible in vitro methods to create peptide partitions and potential ways to sequence partitioned peptides.