Band composition analysis: a new procedure for deconvolution of the mass spectra of organometallic compounds

Deconvolution and database search of complex tandem mass spectra of intact proteins: a combinatorial approach

Top-down proteomics studies intact proteins, enabling new opportunities for analyzing post-translational modifications. Because tandem mass spectra of intact proteins are very complex, spectral deconvolution (grouping peaks into isotopomer envelopes) is a key initial stage for their interpretation. In such spectra, isotopomer envelopes of different protein fragments span overlapping regions on the m/z axis and even share spectral peaks. This raises both pattern recognition and combinatorial challenges for spectral deconvolution. We present MS-Deconv, a combinatorial algorithm for spectral deconvolution. The algorithm first generates a large set of candidate isotopomer envelopes for a spectrum, then represents the spectrum as a graph, and finally selects its highest scoring subset of envelopes as a heaviest path in the graph. In contrast with other approaches, the algorithm scores sets of envelopes rather than individual envelopes. We demonstrate that MS-Deconv improves on Thrash and Xtract in the number of correctly recovered monoisotopic masses and speed. We applied MS-Deconv to a large set of top-down spectra from Yersinia rohdei (with a still unsequenced genome) and further matched them against the protein database of related and sequenced bacterium Yersinia enterocolitica. MS-Deconv is available at http://proteomics.ucsd.edu/Software.html.

NITPICK: peak identification for mass spectrometry data

Background

The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.

Results

This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averagine, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra.

Conclusion

Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from .

Modeling of isotopomeric cluster of the molecular ion

The occurrence of poly-isotopic elements in a molecule or ion can result in complex isotopomeric cluster of an ion. The "isotopomer" better and correctly indicates different isotopic compositions of a molecule (compound) or ion and not a single atom. The ions of organic compounds show in accurate mass spectra single, isolated peaks or narrow sub-clusters regardless of their molecular masses. The occurrence of a PIE makes the molecular ion cluster more complex and significantly influences the location of the most abundant peak and the form of the cluster. The present study is an attempt at answering the following question: what is the mechanism of the molecular ion's isotopomeric cluster formation and is it step-by-step predictable? The accurate mass-resolved isotopomer cluster can be predicted from accurate masses and abundances of the stable isotopes. The cluster consists of several sub-patterns, each of which is composed of near signals (at the same nominal m/z). The range of the sub-cluster usually does not exceed 0.005 u. The low-resolution cluster can be predicted from the high-resolution pattern by addition of all peaks occurring over a given narrow mass range (m/z - 0.5; m/z+0.49). Surprisingly, predicting the accurate mass cluster is simpler than predicting the low-resolution one. A compliance of the model results with the experimental ones suggests a correct prediction.

Mathematical tools in analytical mass spectrometry

Over the last few decades, mass spectrometry has become a powerful tool for exploring various aspects of molecular processes occurring in biological systems. Such exploration is leading to a greater understanding of various complex life processes; unraveling these processes poses the greatest challenge to contemporary bioscience. With due respect to sample preparation, data analysis is rapidly becoming a major obstacle to the conversion of experimental knowledge into valid conclusions. It is interesting to note that many problems related to mass spectrometry can be solved using techniques from computer science, graph theory and discrete mathematics. The aim of this manuscript is to recollect several essays that demonstrate the power and the need to apply such skills to mass spectrometry data interpretation. Special attention is paid to situations where traditional chemical analysis reaches its limits but mathematical reasoning can still allow us to reach valid conclusions.

Isotopic pattern and accurate mass determination in urine drug screening by liquid chromatography/time-of-flight mass spectrometry

An efficient method was developed for toxicological drug screening in urine by liquid chromatography coupled with electrospray ionization time-of-flight mass spectrometry. The method relies on a large target database of exact monoisotopic masses representing the elemental formulae of reference drugs and their metabolites. Mass spectral identification is based on matching measured accurate mass and isotopic pattern (SigmaFit) of a sample component with those in the database. Data post-processing software was developed for automated reporting of findings in an easily interpretable form. The mean and median of SigmaFit for true-positive findings were 0.0066 and 0.0051, respectively. The mean and median of mass error absolute values for true-positive findings were 2.51 and 2.17 ppm, respectively, corresponding to 0.65 and 0.60 mTh. For routine screening practice, a SigmaFit tolerance of 0.03 and a mass tolerance of 10 ppm were chosen. Ion abundance differences from urine extracts did not affect the accuracy of the automatically acquired SigmaFit or mass values. The results show that isotopic pattern matching by SigmaFit is a powerful means of identification in addition to accurate mass measurement.