Advance of Peptide Detectability Prediction on Mass Spectrometry Platform in Proteomics

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.

Interfaces with Structure Dynamics of the Workhorses from Cells Revealed through Cross-Linking Mass Spectrometry (CLMS)

The fundamentals of how protein-protein/RNA/DNA interactions influence the structures and functions of the workhorses from the cells have been well documented in the 20th century. A diverse set of methods exist to determine such interactions between different components, particularly, the mass spectrometry (MS) methods, with its advanced instrumentation, has become a significant approach to analyze a diverse range of biomolecules, as well as bring insights to their biomolecular processes. This review highlights the principal role of chemistry in MS-based structural proteomics approaches, with a particular focus on the chemical cross-linking of protein-protein/DNA/RNA complexes. In addition, we discuss different methods to prepare the cross-linked samples for MS analysis and tools to identify cross-linked peptides. Cross-linking mass spectrometry (CLMS) holds promise to identify interaction sites in larger and more complex biological systems. The typical CLMS workflow allows for the measurement of the proximity in three-dimensional space of amino acids, identifying proteins in direct contact with DNA or RNA, and it provides information on the folds of proteins as well as their topology in the complexes. Principal CLMS applications, its notable successes, as well as common pipelines that bridge proteomics, molecular biology, structural systems biology, and interactomics are outlined.

Transepithelial Transport of YWDHNNPQIR and Its Metabolic Fate with Cytoprotection against Oxidative Stress in Human Intestinal Caco-2 Cells

Studies on antioxidant peptides extracted from foodstuff sources have included not only experiments to elucidate their chemical characteristics but also to investigate their bioavailability and intracellular mechanisms. This study was designed to clarify the absorption and antioxidative activity of YWDHNNPQIR (named RAP), which is derived from rapeseed protein using a Caco-2 cell transwell model. Results showed that 0.8% RAP (C₀ = 0.2 mM, t = 90 min) could maintain the original structure across the Caco-2 cell monolayers via the intracellular transcytosis pathway, and the apparent drug absorption rate (P_app) was (6.6 ± 1.24) × 10^-7 cm/s. Three main fragments (WDHNNPQIR, DHNNPQIR, and YWDHNNPQ) and five modified peptides derived from RAP were found in both the apical and basolateral side of the Caco-2 cell transwell model. Among these new metabolites, WDHNNPQIR had the greatest antioxidative activity in Caco-2 cells apart from the DPPH assay. With a RAP concentration of 200 μM, there were significant differences in four antioxidative indicators (T-AOC, GSH-Px, SOD, and MDA) compared to the oxidative stress control (P < 0.05). In addition, RAP may also influence apoptosis of the Caco-2 cells, which was caused by AAPH-induced oxidative damage.

Quantifying the effect of competition for detection between coeluting peptides on detection probabilities in mass-spectrometry-based proteomics

There are many factors that contribute to the variation in detection probabilities of proteins in LC-MS/MS experiments, and currently little is known about their relative importance. In this study, we analyze the effect of competition for detection between coeluting peptides on peptide detection probability. Using a novel method for estimating peptide detection probabilities, we show that these probabilities can vary by an order of magnitude between peptides that elute from the liquid chromatograph at the same time as many other peptides and those that elute with fewer other peptides. To explore these results, we use a mathematical model to show that competition for detection between peptides is expected to be a major source of missed detections in complex mixtures because there will be many MS/MS scanning intervals that contain more coeluting peptides than can be subjected to MS/MS analysis. Our data and simulation results show that the number of coeluting peptides is a primary determinant of whether a peptide will be detected. In our data, this had a several-fold larger effect on peptide detection probability than did peptide abundance. Furthermore, the distribution of elution times for the most frequently detected peptides was strongly shifted toward values where there were few coeluting peptides, indicating that the number of coeluting peptides is a major determinant of whether a peptide is proteotypic.