scholarly journals Peptide sequencing in an electrolytic cell with two nanopores in tandem and exopeptidase

2015 ◽  
Author(s):  
G Sampath
Keyword(s):  
Sample Size ◽  
Gel Electrophoresis ◽  
Peptide Sequencing ◽  
Electrolytic Cell ◽  
Amino Acid Type ◽  
Tandem Cell ◽  
Confidence Levels ◽  
Size Number ◽  
Acid Type ◽  
Blockade Level

A nanopore-based approach to peptide sequencing without labels or immobilization is considered. It is based on a tandem cell (RSC Adv., 2015, 5, 167-171) with the structure [cis1, upstream pore (UNP), trans1/cis2, downstream pore (DNP), trans2]. An amino or carboxyl exopeptidase attached to the downstream side of UNP cleaves successive leading residues in a peptide threading from cis1 through UNP. A cleaved residue translocates to and through DNP where it is identified. A Fokker-Planck model is used to compute translocation statistics for each amino acid type. Multiple discriminators, including a variant of the current blockade level and translocation times through trans1/cis2 and DNP, identify a residue. Calculations show the 20 amino acids to be grouped by charge (+, -, neutral) and ordered within each group (which makes error correction easier). The minimum cleaving interval required of the exopeptidase, the sample size (number of copies of the peptide to sequence or runs with one copy) to identify a residue with a given confidence level, and confidence levels for a given sample size are calculated. The results suggest that if the exopeptidase cleaves each and every residue and does so in a reasonable time, peptide sequencing with acceptable (and correctable) errors may be feasible. If validated experimentally the proposed device could be an alternative to mass spectrometry and gel electrophoresis. Implementation-related issues are discussed.

scholarly journals A quasi-digital approach to peptide sequencing using tandem nanopores with endo- and exo-peptidases

2015 ◽  
Author(s):  
G Sampath
Keyword(s):  
Amino Acid ◽  
Simple Algorithm ◽  
Peptide Sequencing ◽  
Amino Acid Type ◽  
Cmos Circuits ◽  
Tandem Cell ◽  
The Third ◽  
Acid Type ◽  
In Series ◽  
Pass Through

A method of sequencing peptides using tandem cells (RSC Adv., 2015, 5, 167-171; RSC Adv., 2015, 5, 30694-30700) and peptidases is considered. A double tandem cell (two tandem cells in tandem) has three nanopores in series, an amino-acid-specific endopeptidase attached downstream of the first pore, and an exopeptidase attached downstream of the second pore. The endopeptidase cleaves a peptide threaded through the first pore into fragments that are well separated in time. Fragments pass through the second pore and are each cleaved by the exopeptidase into a series of single residues; the latter pass through the third pore and cause separate current blockades that can be counted. This leads to an ordered list of integers corresponding to the number of residues in each fragment. With 20 cells, one per amino acid type, and 20 peptide copies, the resulting 20 lists of integers are used by a simple algorithm to assemble the sequence. This is a quasi-digital process that uses the lengths of subsequences to sequence the peptide, it differs from conventional analog methods which seek to identify monomers in a polymer via differences in blockade levels, residence times, or transverse currents. Several implementation issues are discussed. In particular the problem of fast analyte translocation, widely considered intransigent, may be resolved through the use of a sufficiently long (40-60 nm) third pore. This translates to a required bandwidth of 1-2 MHz, which is within the range of currently available CMOS circuits.

Amino acid discrimination in a nanopore and the feasibility of sequencing peptides with a tandem cell and exopeptidase

RSC Advances ◽  
2015 ◽  
Vol 5 (39) ◽  
pp. 30694-30700 ◽  
Author(s):  
G. Sampath
Keyword(s):  
Amino Acid ◽  
Peptide Sequencing ◽  
Electrolytic Cell ◽  
Tandem Cell

Peptide sequencing in an electrolytic cell with two nanopores in tandem and exopeptidase.

scholarly journals Emulsifying Potency of New Amino Acid-Type Surfactant. (1). O/W Emulsions.

2001 ◽  
Vol 50 (11) ◽  
pp. 847-855 ◽  
Author(s):  
Junichi KOUCHI ◽  
Tatsuru TABOHASHI ◽  
Shoko YOKOYAMA ◽  
Fuminori HARUSAWA ◽  
Aritomo YAMAGUCHI ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Type ◽  
Acid Type

Solution properties of amino acid-type new surfactant

2001 ◽  
Vol 20 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Tatsuru Tabohashi ◽  
Kazuhiko Tobita ◽  
Kazutami Sakamoto ◽  
Junichi Kouchi ◽  
Shoko Yokoyama ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Type ◽  
Solution Properties ◽  
Acid Type

scholarly journals Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

2019 ◽  
Vol 116 (33) ◽  
pp. 16367-16377 ◽  
Author(s):  
Alex Nisthal ◽  
Connie Y. Wang ◽  
Marie L. Ary ◽  
Stephen L. Mayo
Keyword(s):  
Protein Stability ◽  
Amino Acid Type ◽  
Stability Prediction ◽  
Single Mutant ◽  
Prediction Tools ◽  
Prediction Algorithms ◽  
Automated Method ◽  
Acid Type ◽  
Stability Data ◽  
Neutral Effect

The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools.

Synthesis of new amino acid-type amphoteric surfactants from tall oil fatty acid

2001 ◽  
Vol 47 (6) ◽  
pp. 470-475 ◽  
Author(s):  
Shi-Fa Wang ◽  
Takeshi Furuno ◽  
Zhi Cheng
Keyword(s):  
Fatty Acid ◽  
Amino Acid ◽  
Amino Acid Type ◽  
Tall Oil ◽  
Acid Type ◽  
Tall Oil Fatty Acid ◽  
Amphoteric Surfactants

Influences on the Mantel-Haenszel Chi-Square in Detection of Differential Item Functioning under Rasch Conditions

1995 ◽  
Vol 80 (3_suppl) ◽  
pp. 1071-1074 ◽  
Author(s):  
Thomas Uttaro
Keyword(s):  
Sample Size ◽  
Differential Item Functioning ◽  
Item Bias ◽  
Reference Groups ◽  
Chi Square ◽  
Item Functioning ◽  
Size Number ◽  
Gender Based ◽  
And Gender ◽  
Present Simulation

The Mantel-Haenszel chi-square (χ2MH) is widely used to detect differential item functioning (item bias) between ethnic and gender-based subgroups on educational and psychological tests. The empirical behavior of χ2MH has been incompletely understood; previous research is inconclusive. The present simulation study explored the effects of sample size, number of items, and trait distributions on the power of χ2MH to detect modeled differential item functioning. A significant effect was obtained for sample size with unacceptably low power for 250 subjects each in the focal and reference groups. The discussion supports the 1990 recommendations of Swaminathan and Rogers, opposes the 1993 view of Zieky that a sample size of 250 for each group is adequate.

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