A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry.

Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.

High speed capillary zone electrophoresis-mass spectrometry via an electrokinetically pumped sheath flow interface for rapid analysis of amino acids and a protein digest.

While capillary zone electrophoresis (CZE) has been used to produce very rapid and efficient separations, coupling these high-speed separations with mass spectrometry (MS) has been challenging. Now, with much faster and sensitive mass spectrometers, it is possible to take full advantage of the CZE speed and reconstruct the fast migrating peaks. Here are three high-speed CZE-MS analyses via an electrokinetically pumped sheath-flow interface. The first separation demonstrates CZE-ESI-MS of an amino acid mixture with a 2-min separation, >50,000 theoretical plates, low micromolar concentration detection limits, and subfemtomole mass detection limits (LTQ XL mass spectrometer). The second separation with our recently improved third-generation CE-MS interface illustrates a 20 amino acid separation in ∼7min with an average over 200,000 plate counts, and results in almost-baseline resolution of structural isomers, leucine and isoleucine. The third separation is of a BSA digest with a reproducible CZE separation and mass spectrometry detection in 2min. CZE-MS/MS analysis of the BSA digest identified 31 peptides, produced 52% sequence coverage, and generated a peak capacity of ∼40 across the 1-min separation window (Q-Exactive mass spectrometer).

Integrated device for online sample buffer exchange, protein enrichment, and digestion.

An integrated sample treatment device, composed of a membrane interface and a monolithic hybrid silica based immobilized enzymatic reactor (IMER), was developed for the simultaneous sample buffer exchange, protein enrichment, and online digestion, by which for the sample buffer, the acetonitrile content was reduced to approximately 1/10 of the initial one, and the pH value was adjusted from approximately 3.0 to approximately 8.0, compatible for online trypsin digestion. Furthermore, the signal intensity of myoglobin digests was improved by over 10 times. Such an integrated device was successfully applied to the online treatment of three protein eluates obtained by reverse-phase liquid chromatography (RPLC) separation, followed by further protein digest analysis with microreverse-phase liquid chromatography-electrospray ionization-tandem mass spectrometry (microRPLC-ESI-MS/MS). The experimental results showed that the performance of such an integrated sample treatment device was comparable to that of the traditional offline sample treatment method, including lyophilization and in-solution digestion. However, the consumed time was reduced to 1/192. All these results demonstrate that such an integrated sample treatment device could be further online coupled with protein separation, peptide separation, and identification, to achieve high-throughput proteome analysis.