15N metabolic labeling quantification workflow in Arabidopsis using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector (PP). Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms

Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

Workflow for Protein N-terminal Acetylation and C-terminal Amidation Application using CcpNmr Analysis v2.4 Platform and Aria2.3 Structure Calculation Software

Structural biology is a field that enables a better understanding of proteins from scratch. From the available techniques, solution NMR is one well established that provides structure, dynamics and protein-molecules interaction. In a NMR lab routine, from data acquisition until protein/mechanisms elucidation comes a process that can undergo months. During the past decades, different tools were developed for NMR data processing, peaks assignment, structure elucidation and data submission. Since many of these programs demand great computational skills, a few groups have tried to combine those programs and make them more friendly and useful, what can possibilite a faster process. Here we highlight CCPNMR2.4 analysis and ARIA2.3, responsible for peak assignment and structure calculation, respectively, and can work associated. Although being academic free and the possibility of working with a GUI interface, the common N-terminal acetylation and C-terminal amidation modifications are not implemented in a way that possibilities to work with them in combination, what results in a dilemma. This work brings visual data that evidences the low usability of CCPN and ARIA with N-terminal acetylated and C-terminal amidated proteins and propose a workflow to overcome this problem, which may improve the usage of both software in the mentioned versions and facilitate the lab users already used to these programs. As a proof of concept, we have chosen a N-terminal amidated peptide, L-Phenylseptin, whose structure has already been solved with other programs. Statistical data shows that no significant difference was found with the structure obtained with the new protocol. In conclusion, we exhibit a new protocol that can be used in combination with CCPNMR2.4 and ARIA2.3 for protein with the mentioned modifications and it successfully works and manipulates these molecules.

Classification of Coffee and Wine with a Microwave Resonator and Deep Learning Machine Technique

In this study, coffee and wine were measured using an microwave resonator, and a deep learning system was trained using the acquired data, and then tested to see if the deep leaning system could distinguish these samples. We tested 6 kinds of wine, 6 kinds of cold brew coffee and 6 kinds of bottled coffee. The microwave resonance spectra of all samples were graphically displayed. The graphical images were processed by an artificial intelligence (AI) technique. By applying deep learning machine technique instead of the peak assignment for complex compounds in general, it was possible to facilitate the classification of coffee or wine with high accuracy.

Classification of Coffee and Wine with a Microwave Resonator and Deep Learning Machine Technique

In this study, coffee and wine were measured using an microwave resonator, and a deep learning system was trained using the acquired data, and then tested to see if the deep leaning system could distinguish these samples. We tested 6 kinds of wine, 6 kinds of cold brew coffee and 6 kinds of bottled coffee. The microwave resonance spectra of all samples were graphically displayed. The graphical images were processed by an artificial intelligence (AI) technique. By applying deep learning machine technique instead of the peak assignment for complex compounds in general, it was possible to facilitate the classification of coffee or wine with high accuracy.

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage

Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated into many common proteomics pipelines and provides rapid conversion from multiple data source types. Finally, we show that our monoisotopic peak assignment results in up to a two-fold increase in total peptide identifications compared to analyses lacking monoisotopic correction and a 44% improvement over previous monoisotopic peak correction algorithms.

Comment on “Local, solvation pressures and conformational changes in ethylenediamine aqueous solutions probed using Raman spectroscopy” by M. Cáceres, A. Lobato, N. J. Mendoza, L. J. Bonales and V. G. Baonza, Phys. Chem. Chem. Phys., 2016, 18, 26192

Misconclusions are corrected on Raman peak assignment and gauche-vs.-trans conformer ratio of ethylenediamine in liquid and aqueous phases. Peaks lost upon aqueous dilution are due to lost NH⋯N interactions. Both conformers exist in both phases.

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

ABSTRACTCross-linking/mass spectrometry (CLMS) has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A seldom-used search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by ‘in-search assignment of the monoisotopic peak’ in Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available datasets. Xi-MPA always delivered the highest number of identifications with ~2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

Decreasing the uncertainty of peak assignments for the analysis of synthetic cathinones using multi-dimensional ultra-high performance liquid chromatography

Peak assignment uncertainty using retention time is significantly reduced by the use of multi-dimensional ultra-high performance liquid chromatography.