Dataset Information

Approximating Isotope Distributions of Biomolecule Fragments.

ABSTRACT: In mass spectrometry (MS)-based proteomics, protein and peptide sequences are determined by the isolation and subsequent fragmentation of precursor ions. When an isolation window captures only part of a precursor's isotopic distribution, the isotope distributions of the fragments depend on the subset of isolated precursor isotopes. Approximation of the expected isotope distributions of these fragments prior to sequence determination enables MS2 deisotoping, monoisotopic mass calculation, charge assignment of fragment peaks, and deconvolution of chimeric spectra. However, currently such methods do not exist, and precursor isotope distributions are often used as a proxy. Here, we present methods that approximate the isotope distribution of a biomolecule's fragment given its monoisotopic mass, the monoisotopic mass of its precursor, the set of isolated precursor isotopes, and optionally sulfur atom content. Our methods use either the Averagine model or splines, the latter of which have similar accuracy to the Averagine approach, but are 20 times faster to compute. Theoretical and approximated isotope distributions are consistent for fragments of in silico digested peptides. Furthermore, mass spectrometry experiments with the angiotensin I peptide and HeLa cell lysate demonstrate that the fragment methods match isotope peaks in MS2 spectra more accurately than the precursor Averagine approach. The algorithms for the approximation of fragment isotope distributions have been added to the OpenMS software library. By providing the means for analyzing fragment isotopic distributions, these methods will improve MS2 spectra interpretation.

SUBMITTER: Goldfarb D

PROVIDER: S-EPMC6166224 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Approximating Isotope Distributions of Biomolecule Fragments.

Goldfarb Dennis D Lafferty Michael J MJ Herring Laura E LE Wang Wei W Major Michael B MB

ACS omega 20180919 9

In mass spectrometry (MS)-based proteomics, protein and peptide sequences are determined by the isolation and subsequent fragmentation of precursor ions. When an isolation window captures only part of a precursor's isotopic distribution, the isotope distributions of the fragments depend on the subset of isolated precursor isotopes. Approximation of the expected isotope distributions of these fragments prior to sequence determination enables MS2 deisotoping, monoisotopic mass calculation, charge ...[more]

PMID: 30288463

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Project description:BackgroundAn important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with in vivo metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species.ResultsEnvelope has been developed to be user-friendly while still being as flexible and powerful as possible. Envelope can simultaneously calculate the isotope distributions for any number of different labeling patterns for a given peptide or oligonucleotide, while automatically summing these into a single overall isotope distribution. Envelope can handle fractional or complete atom or residue-based labeling, and the contribution from each different user-defined labeling pattern is clearly illustrated in the interactive display and is individually adjustable. At present, Envelope supports labeling with 2H, 13C, and 15N, and supports adjustments for baseline correction, an instrument accuracy offset in the m/z domain, and peak width. Furthermore, Envelope can display experimental data superimposed on calculated isotope distributions, and calculate a least-squares goodness of fit between the two. All of this information is displayed on the screen in a single graphical user interface. Envelope supports high-quality output of experimental and calculated distributions in PNG or PDF format. Beyond simply comparing calculated distributions to experimental data, Envelope is useful for planning or designing metabolic labeling experiments, by visualizing hypothetical isotope distributions in order to evaluate the feasibility of a labeling strategy. Envelope is also useful as a teaching tool, with its real-time display capabilities providing a straightforward way to illustrate the key variable factors that contribute to an observed isotope distribution.ConclusionEnvelope is a powerful tool for the interactive calculation and visualization of complex isotope distributions for comparison to experimental data. It is available under the GNU General Public License from http://williamson.scripps.edu/envelope/.

Project description:Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

Dataset Information

Approximating Isotope Distributions of Biomolecule Fragments.

Publications

Approximating Isotope Distributions of Biomolecule Fragments.

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