Mikhail V. Gorshkov

Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics

AbstractPyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source code repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Sequence Scrambling in Shotgun Proteomics is Negligible

Analysis of 15,897 low-energy (CAD) and 10,878 higher-energy (HCD) collisional dissociation mass spectra of doubly protonated tryptic peptides taken with high resolution revealed that the rate of sequence scrambling due to b-ion cyclization is negligible (<1%) and can be safely ignored as a possible source of erroneous sequence assignment in shotgun proteomics. On the other hand, there is significant presence of normal (non-scrambled) internal fragments in HCD, which should be taken into account by MS/MS search engines.

Observation of ion coalescence in Orbitrap Fourier transform mass spectrometry

RATIONALE Similar to other mass spectrometric technologies based on ion trapping in a spatially restricted area, the performance of Orbitrap Fourier transform mass spectrometry (FTMS) is affected by the interaction between the trapped ion clouds. One of the effects associated with Coulombic interaction inside the ion trap is the ion cloud coupling, known in ion cyclotron resonance (ICR) FTMS as coalescence, or a phase-locking phenomenon. Nevertheless, the direct observation of ion coalescence has not been reported for Orbitrap FTMS yet. METHODS We have performed experiments on ion coalescence with a pair of isobaric peptides in the state-of-the-art hybrid linear ion trap high-field compact Orbitrap Elite FT mass spectrometer using both standard and advanced signal processing modes. RESULTS For the instrument configuration employed in this work we found that ion coalescence occurs when two singly charged peptides with the mass difference of 22 mDa and molecular weight of about 1060 Da have the total abundance of at least 7.5*10(4) charges. CONCLUSIONS We experimentally demonstrate the existence of the ion coalescence phenomenon in Orbitrap FTMS for peptides for a wide range of total trapped ion population. Using the applicable modeling of the phase-locking threshold we estimate the effect of ion coalescence on the performance of Orbitrap FTMS.

Exome-driven characterization of the cancer cell lines at the proteome level: the NCI-60 case study.

Cancer genome deviates significantly from the reference human genome, and thus a search against standard genome databases in cancer cell proteomics fails to identify cancer-specific protein variants. The goal of this Article is to combine high-throughput exome data [Abaan et al. Cancer Res. 2013] and shotgun proteomics analysis [Modhaddas Gholami et al. Cell Rep. 2013] for cancer cell lines from NCI-60 panel to demonstrate further that the cell lines can be effectively recognized using identified variant peptides. To achieve this goal, we generated a database containing mutant protein sequences of NCI-60 panel of cell lines. The proteome data were searched using Mascot and X!Tandem search engines against databases of both reference and mutant protein sequences. The identification quality was further controlled by calculating a fraction of variant peptides encoded by the own exome sequence for each cell line. We found that up to 92.2% peptides identified by both search engines are encoded by the own exome. Further, we used the identified variant peptides for cell line recognition. The results of the study demonstrate that proteome data supported by exome sequence information can be effectively used for distinguishing between different types of cancer cell lines.

Calibration function for the orbitrap FTMS accounting for the space charge effect

Ion storage in an electrostatic trap has been implemented with the introduction of the Orbitrap Fourier transform mass spectrometer (FTMS), which demonstrates performance similar to high-field ion cyclotron resonance MS. High mass spectral characteristics resulted in rapid acceptance of the Orbitrap FTMS for Life Sciences applications. The basics of Orbitrap operation are well documented; however, like in any ion trap MS technology, its performance is limited by interactions between the ion clouds. These interactions result in ion cloud couplings, systematic errors in measured masses, interference between ion clouds of different size yet with close m/z ratios, etc. In this work, we have characterized the space-charge effect on the measured frequency for the Orbitrap FTMS, looking for the possibility to achieve sub-ppm levels of mass measurement accuracy (MMA) for peptides in a wide range of total ion population. As a result of this characterization, we proposed an m/z calibration law for the Orbitrap FTMS that accounts for the total ion population present in the trap during a data acquisition event. Using this law, we were able to achieve a zero-space charge MMA limit of 80 ppb for the commercial Orbitrap FTMS system and sub-ppm level of MMA over a wide range of total ion populations with the automatic gain control values varying from 10 to 107.

Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC-MS phosphopeptide analysis

LC combined with MS/MS analysis of complex mixtures of protein digests is a reliable and sensitive method for characterization of protein phosphorylation. Peptide retention times (RTs) measured during an LC‐MS/MS run depend on both the peptide sequence and the location of modified amino acids. These RTs can be predicted using the LC of biomacromolecules at critical conditions model (BioLCCC). Comparing the observed RTs to those obtained from the BioLCCC model can provide additional validation of MS/MS‐based peptide identifications to reduce the false discovery rate and to improve the reliability of phosphoproteome profiling. In this study, energies of interaction between phosphorylated residues and the surface of RP separation media for both “classic” alkyl C18 and polar‐embedded C18 stationary phases were experimentally determined and included in the BioLCCC model extended for phosphopeptide analysis. The RTs for phosphorylated peptides and their nonphosphorylated analogs were predicted using the extended BioLCCC model and compared with their experimental RTs. The extended model was evaluated using literary data and a complex phosphoproteome data set distributed through the Association of Biomolecular Resource Facilities Proteome Informatics Research Group 2010 study. The reported results demonstrate the capability of the extended BioLCCC model to predict RTs which may lead to improved sensitivity and reliability of LC‐MS/MS‐based phosphoproteome profiling.

Proteome digestion specificity analysis for rational design of extended bottom-up and middle-down proteomics experiments.

Mass spectrometry (MS)-based bottom-up proteomics (BUP) is currently the method of choice for large-scale identification and characterization of proteins present in complex samples, such as cell lysates, body fluids, or tissues. Technically, BUP relies on MS analysis of complex mixtures of small, <3 kDa, peptides resulting from whole proteome digestion. Because of the extremely high sample complexity, further developments of detection methods and sample preparation techniques are necessary. In recent years, a number of alternative approaches such as middle-down proteomics (MDP, addressing up to 15 kDa peptides) and top-down proteomics (TDP, addressing proteins exceeding 15 kDa) have been gaining particular interest. Here we report on the bioinformatics study of both common and less frequently employed digestion procedures for complex protein mixtures specifically targeting the MDP approach. The aim of this study was to maximize the yield of protein structure information from MS data by optimizing peptide size distribution and sequence specificity. We classified peptides into four categories based on molecular weight: 0.6-3 (classical BUP), 3-7 (extended BUP), 7-15 kDa (MDP), and >15 kDa (TDP). Because of instrumentation-related considerations, we first advocate for the extended BUP approach as the potential near-future improvement of BUP. Therefore, we chose to optimize the number of unique peptides in the 3-7 kDa range while maximizing the number of represented proteins. The present study considers human, yeast, and bacterial proteomes. Results of the study can be further used for designing extended BUP or MDP experimental workflows.

Applicability of the critical chromatography concept to proteomics problems: Dependence of retention time on the sequence of amino acids

A peptide separation model based on the technique of liquid chromatography of macromolecules at the critical condition was proposed. In terms of this model, the array of experimental data on the separation of peptides is considered. The main phenomenological parameters of the model—effective adsorption energies of amino acid residues—were determined, thus allowing the influence of character of their alternation in the chain on retention times to be predicted. The model is applicable to investigation into the feasibility of separation in different chromatographic modes of not only peptides with the same amino acid composition and different sequences of units in the chain but also peptides containing amino acid isomers and mirror sequences with different terminal groups.

Applicability of the Critical Chromatography Concept to Proteomics Problems : Experimental Study of the Dependence of Peptide Retention Time on the Sequence of Amino Acids in the Chain

Experimental data on the separation of synthetic and natural peptides are presented as treated in terms of the separation model proposed by the authors, which allows for the chain connectivity of amino acid residues and the cooperative character of their interaction with the surface. It was shown that the model accurately predicts the separation of peptides with identical amino acid contents and different sequences of units in the chain. The differences in the sequence may be permutation of amino acid residues and the presence of terminal groups, amino acid isomers, or mirror sequences in the chain. The separation model was used to predict the retention times of peptides prepared via the enzymatic hydrolysis of E. coli proteins and bovine serum albumin with trypsin. It was shown that in general the model accurately explains the array of experimental data on the separation of such peptides, thus being the first successful attempt to relate the chain sequence to the retention volume.

On the Utility of Isotopic Fine Structure Mass Spectrometry in Protein Identification

Modern mass spectrometry (MS)-based protein identification and characterization relies upon accurate mass measurements of the (13)C isotopic distributions of the enzymatically produced peptides. Interestingly, obtaining peptide elemental composition information from its isotopic fine structure mass spectrum to increase the confidence in peptide and protein identification has not yet been developed into a bottom-up proteomics-grade analytical approach. Here, we discuss the possible utility and limitations of the isotopic fine structure MS for peptide and protein identification. First, we in silico identify the peptides from the E. coli tryptic digest and show the increased confidence in peptide identification by consideration of the isotopic fine structures of these peptides as a function of mass and abundance accuracies. In the following, we demonstrate that the state-of-the-art high magnetic field Fourier transform ion cyclotron resonance (FT-ICR) MS allows a routine acquisition of the isotopic fine structure information of a number of isobaric peptide pairs, including a pair of peptides originating from E. coli. Finally, we address the practical limitation of the isotopic fine structure MS implementation in the time-constraint experiments by applying an advanced signal processing technique, filter diagonalization method, to the experimental transients to overcome the resolution barrier set by the typically applied Fourier transformation. We thus demonstrate that the isotopic fine structures of peptides may indeed improve the peptide and possibly protein identification, can be produced in a routine experiment by the state-of-the-art high resolution mass spectrometers, and can be potentially obtained on a chromatographic time-scale of a typical bottom-up proteomics experiment. The latter one requires at least an order of magnitude increase in sensitivity of ion detection, which presumably can be realized using high-field Orbitrap FTMS and/or future generation of ultrahigh magnetic field FT-ICR MS equipped with harmonized ICR cells.