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.
Highlights
We recently introduced the SAMDI mass spectrometry method, which uses MALDI mass spectrometry to analyze peptides that are immobilized to a self-assembled monolayer of alkanethiolates on gold (Figure 1), and we have demonstrated the use of this method for profiling enzyme specificities,[13] for discovering new enzymes,[14] and for profiling activities in a lysate.[15]
We trained a machine learning model with randomly chosen groups of peptides consisting of 5 to 350 peptides to discover patterns and make predictions of peptide signal-to-noise ratio (S/N) based on amino acid sequences
From analysis of both arrays, we found that the machine learning models need to be trained on only 1/3 of peptides in each array to make accurate predictions of peptide S/N
Summary
Peptide arrays have emerged as an enabling tool for identifying biologically relevant peptide substrates and molecular recognition sites, and hold great promise as a new analytical method for basic and translational research in the biomedical sciences.[1,2] Uses of peptide arrays include measuring changes in enzymatic activity enzymes that add or remove post-translational modifications to gain insight into different cellular pathways and processes.[3−5] Other applications include diagnostic or detection-focused arrays such as differential peptide arrays to detect specific analytes in complex mixtures[6,7] or diagnose diseases.[8,9] Many existing methods are based on either radioisotopic or fluorescent labels to detect reaction products.[10,11] These methods introduce additional protocol steps, and for the latter, can alter natural biological activity leading to false interpretations, as when resveratrol was erroneously found to enhance deacetylation on a peptide with an attached fluorophore.[12]. We recently introduced the SAMDI mass spectrometry method, which uses MALDI mass spectrometry to analyze peptides that are immobilized to a self-assembled monolayer of alkanethiolates on gold (Figure 1), and we have demonstrated the use of this method for profiling enzyme specificities,[13] for discovering new enzymes,[14] and for profiling activities in a lysate.[15] This method provides many benefits, including the use of surface chemistries that are intrinsically inert to the nonspecific adsorption of protein, the availability of a broad range of chemistries for immobilization of peptides, and, most significantly, the compatibility with matrix assisted laser desorption ionization mass spectrometry to analyze the masses of the peptide-alkanethiolate conjugates This ability to directly measure peptide masses[16] allows a straightforward analysis of peptide modifications by identifying the corresponding mass shifts. We select peptide libraries that are unbiased in their composition to evaluate differences in S/N due to differing amino acid sequences, and we offer a complete empirical analysis relating amino acid composition and S/N of the peptides
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
