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Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry.
- Source :
-
Analytical Chemistry . 9/5/2017, Vol. 89 Issue 17, p9039-9047. 9p. - Publication Year :
- 2017
-
Abstract
- Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry. Our study demonstrates that S/N varies significantly across peptides within peptide arrays, and variation in S/N is attributable to differences of single amino acids. We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric. We find low peptide-S/N concordance between arrays, demonstrating that different arrays require individual characterization and that global peptide-S/N relationships are difficult to identify. However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection of high S/N peptides. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*PEPTIDES
*MASS spectrometry
*NOISE
*AMINO acids
Subjects
Details
- Language :
- English
- ISSN :
- 00032700
- Volume :
- 89
- Issue :
- 17
- Database :
- Academic Search Index
- Journal :
- Analytical Chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 125043895
- Full Text :
- https://doi.org/10.1021/acs.analchem.7b01728