BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution

A framework for automated multimodal HDX-MS analysis

We present pyHXExpress, a customizable codebase for automated high-throughput multimodal analysis of all spectra generated from HDX-MS experiments. The workflow was validated against a synthetic test dataset to test the fitting algorithms and to confirm the statistical outputs. We further establish a framework for the determination of multimodality throughout a protein system by rigorous evaluation of multimodal fits across all peptide spectra. We demonstrate this approach using entire protein datasets to detect multimodality, conformational heterogeneity, and characterize dynamics of small heat shock protein HSPB5 and two disease mutants.

How to Deal With Internal Fragment Ions?

Tandem mass spectrometry of peptides and proteins generates 3mass spectra of their gas-phase fragmentation product ions, including N-terminal, C-terminal, and internal fragment ions. While N- and C-terminal ions are routinely assigned and identified using computational methods, internal fragment ions are often difficult to annotate correctly. They become particularly relevant for long peptides and full proteoforms where the peptide backbone is more likely to be fragmented multiple times. Internal fragment ions potentially offer tremendous information regarding amino acid sequences and positions of post-translational modifications of peptides and intact proteins. However, their practical application is challenged by the vast number of theoretical internal fragments that exist for long amino acid sequences, leading to a high risk of false-positive annotations. We analyze the mass spectral contributions of internal fragment ions in spectra from middle-down and top-down experiments and introduce a novel graph-based annotation approach designed to manage the complexity of internal fragments. Our graph-based representation allows us to compare multiple candidate proteoforms in a single graph, and to assess different candidate annotations in a fragment ion spectrum. We demonstrate cases from middle-down and top-down data where internal ions enhance amino acid sequence coverage of polypeptides and proteins and accurate localization of post-translational modifications. We conclude that our graph-based method provides a general approach to process complex tandem mass spectra, enhance annotation of internal fragment ions, and improve proteoform sequencing and characterization by mass spectrometry.

LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence

In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use of the available data to assess the quality of lipid identifications. Here, we present a machine-learning-based model to provide coherent, holistic quality scores based on multiple lines of evidence. Underutilized metrics such as isotope ratios and chromatographic behavior allow for much higher accuracy of identification confidence. We find that approximately 50% of tandem mass spectrometry-based automated lipid identifications are incorrect but that multidimensional rescoring reduces false discoveries to only 7% while retaining 80% of true positives. Our method works with most chromatography methods and generalizes across a family of MS instruments. LipoCLEAN is available at https://github.com/stavis1/LipoCLEAN.

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides

Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson's χ2 goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally, as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated with the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the "true" (i.e., theoretically) most-abundant isotope peak from a spectrum, even if the observed isotope distribution is distorted by noise or poor ion statistics. The method is available online as an R shiny app: https://valkenborg-lab.shinyapps.io/mind/.

An integrated high resolution mass spectrometric data acquisition method for rapid screening of saponins in Panax notoginseng (Sanqi)

The aim of this study was to develop a convenient method without pretreatments for nontarget discovery of interested compounds. The segment and exposure strategy, coupled with two mass spectrometer data acquisition methods was firstly proposed for screening the saponins in extract of Panax notoginseng (Sanqi) via high-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (HPLC-QTOF/MS). By gradually removing certain major or moderate interference compounds, the developed segment and exposure strategy could significantly improve the detection efficiency for trace compounds. Moreover, the newly developed five-point screening approach based on a modified mass defect filter strategy and the visual isotopic ion technique was verified to be efficient and reliable in picking out the interested precursor ions. In total, 234 ginsenosides including 67 potential new ones were characterized or tentatively identified from the extract of Sanqi. Particularly, some unusual compounds containing the branched glycosyl group or new substituted acyl groups were firstly reported. The proposed integrated strategy held a strong promise for analyses of the complex mixtures.