Prediction of peptide ion mobilities via a priori calculations from intrinsic size parameters of amino acid residues

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide's ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (http://jpostdb.org) with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

Deep learning the collisional cross sections of the peptide universe from a million experimental values

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

QSSR Modeling of Bacillus Subtilis Lipase A Peptide Collision Cross-Sections in Ion Mobility Spectrometry: Local Descriptor Versus Global Descriptor

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor.

Structurally Informative Isotopic Shifts in Ion Mobility Spectra for Heavier Species

The isotopic molecular envelopes due to stable isotopes for most elements were a staple of mass spectrometry since its origins, often leveraged to identify and quantify compounds. However, all isomers share one MS envelope. As the molecular motion in media also depends on the isotopic composition, separations such as liquid chromatography (LC) and ion mobility spectrometry (IMS) must also feature isotopic envelopes. These were largely not observed because of limited resolution, except for the (structurally uninformative) shifts in LC upon H/D exchange. We recently found the isotopic shifts in FAIMS for small haloanilines (∼130-170 Da) to hinge on the halogen position, opening a novel route to isomer characterization. Here, we extend the capability to heavier species: dibromoanilines (DBAs, ∼250 Da) and tribromoanilines (TBAs, ∼330 Da). The ¹³C shifts for DBAs and TBAs vary across isomers, some changing sign. While ⁸¹Br shifts are less specific, the 2-D ¹³C/⁸¹Br shifts unequivocally differentiate all isomers. The trends for DBAs track those for dichloroanilines, with the ¹³C shift order preserved for most isomers. The peak broadening due to merged isotopomers is also isomer-specific. The absolute shifts for TBAs are smaller than those for lighter haloanilines, but differentiate isomers as well because of compressed uncertainties. These results showcase the feasibility of broadly distinguishing isomers in the more topical ∼200-300 Da range using the isotopic shifts in IMS spectra.

Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification

Rapid, solution-phase hydrogen/deuterium exchange (HDX) coupled with mass spectrometry (MS) is demonstrated as a means for distinguishing small-molecule metabolites. HDX is achieved using capillary vibrating sharp-edge spray ionization (cVSSI) to allow sufficient time for reagent mixing and exchange in micrometer-sized droplets. Different compounds are observed to incorporate deuterium with varying efficiencies resulting in unique isotopic patterns as revealed in the MS spectra. Using linear regression techniques, parameters representing contribution to exchange by different hydrogen types can be computed. In this proof-of-concept study, the exchange parameters are shown to be useful in the retrodiction of the amount of deuterium incorporated within different compounds. On average, the exchange parameters retrodict the exchange level with ~ 2.2-fold greater accuracy than treating all exchangeable hydrogens equally. The parameters can be used to produce hypothetical isotopic distributions that agree (± 16% RMSD) with experimental measurements. These initial studies are discussed in light of their potential value for identifying challenging metabolite species.

New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells

Mass-spectrometry based omics technologies - namely proteomics, metabolomics and lipidomics - have enabled the molecular level systems biology investigation of organisms in unprecedented detail. There has been increasing interest for gaining a thorough, functional understanding of the biological consequences associated with cellular heterogeneity in a wide variety of research areas such as developmental biology, precision medicine, cancer research and microbiome science. Recent advances in mass spectrometry (MS) instrumentation and sample handling strategies are quickly making comprehensive omics analyses of single cells feasible, but key breakthroughs are still required to push through remaining bottlenecks. In this review, we discuss the challenges faced by single cell MS-based omics analyses and highlight recent technological advances that collectively can contribute to comprehensive and high throughput omics analyses in single cells. We provide a vision of the potential of integrating pioneering technologies such as Structures for Lossless Ion Manipulations (SLIM) for improved sensitivity and resolution, novel peptide identification tactics and standards free metabolomics approaches for future applications in single cell analysis.

Advanced analytical strategies for measuring free bioactive milk sugars: from composition and concentrations to human metabolic response

Our daily food intake provides the nutrients to maintain health. However, in addition to the nutritional values, food can promote health and be beneficial in preventing diseases. Human milk is a unique food source that contains essential nutrients in the right balance and other bioactive factors that make it the ideal food for all healthy term infants. Human milk oligosaccharides (HMOs) play an important role in health, at several levels: acting as prebiotics promoting the growth of beneficial bacterial strains, preventing the growth of pathogenic bacteria in the intestine, and modulating the immune response against bacterial infections. However, despite their biological relevance and the advances made in the analytical field, very few studies have been carried out to better understand HMOs bioactivity mechanisms or to examine human metabolic response to dietary supplementation. This review describes the state-of-the-art of glycomics strategies, recent analytical methods, and future trends for the identification and discovery of bioactive sugars, the known mechanisms of action, and discusses findings of some recent human intervention trials.

Ion Mobility Spectrometry-Mass Spectrometry Coupled with Gas-Phase Hydrogen/Deuterium Exchange for Metabolomics Analyses

Ion mobility spectrometry-mass spectrometry (IMS-MS) in combination with gas-phase hydrogen/deuterium exchange (HDX) and collision-induced dissociation (CID) is evaluated as an analytical method for small-molecule standard and mixture characterization. Experiments show that compound ions exhibit unique HDX reactivities that can be used to distinguish different species. Additionally, it is shown that gas-phase HDX kinetics can be exploited to provide even further distinguishing capabilities by using different partial pressures of reagent gas. The relative HDX reactivity of a wide variety of molecules is discussed in light of the various molecular structures. Additionally, hydrogen accessibility scoring (HAS) and HDX kinetics modeling of candidate (in silico) ion structures is utilized to estimate the relative ion conformer populations giving rise to specific HDX behavior. These data interpretation methods are discussed with a focus on developing predictive tools for HDX behavior. Finally, an example is provided in which ion mobility information is supplemented with HDX reactivity data to aid identification efforts of compounds in a metabolite extract. Graphical Abstract ᅟ.

LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics

The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

A Database of Transition-Metal-Coordinated Peptide Cross-Sections: Selective Interaction with Specific Amino Acid Residues

Ion mobility mass spectrometry (IMS-MS) techniques were used to generate a database of 2288 collision cross sections of transition-metal-coordinated tryptic peptide ions. This database consists of cross sections for 1253 [Pep + X]²⁺ and 1035 [Pep + X + H]³⁺, where X²⁺ corresponds to Mn²⁺, Co²⁺, Ni²⁺, Cu²⁺, or Zn²⁺. This number of measurements enables the extraction of structural trends for transition-metal-coordinated peptide ions. The range of structures and changes in collision cross sections for X²⁺-coordinated species (compared with protonated species of the same charge state) is similar to Mg²⁺-coordinated species. This suggests that the structures are largely determined by similarities in cation size with differences among the cross section distributions presumably caused by X²⁺ interactions with specific functional groups offered by the residue R-groups or the peptide backbone. Cross section contributions for individual residues upon X²⁺ solvation are assessed with the derivation of intrinsic size parameters (ISPs). The comparison of the [Pep + X]²⁺ ISPs with those previously reported for [Pep + Mg]²⁺ ions displays a lower contribution to the cross section for His, carboxyamidomethylated Cys, and Met, and is consistent with specific metal-residue interactions identified within protein X-ray crystallography databases. Graphical Abstract ᅟ.

A Priori Intrinsic PTM Size Parameters for Predicting the Ion Mobilities of Modified Peptides

The rising profile of ion mobility spectrometry (IMS) in proteomics has driven the efforts to predict peptide cross-sections. In the simplest approach, these are derived by adding the contributions of all amino acid residues and post-translational modifications (PTMs) defined by their intrinsic size parameters (ISPs). We show that the ISPs for PTMs can be calculated from properties of constituent atoms, and introduce the "impact scores" that govern the shift of cross-sections from the central mass-dependent trend for unmodified peptides. The ISPs and scores tabulated for 100 more common PTMs enable predicting the domains for modified peptides in the IMS/MS space that would guide subproteome investigations. Graphical Abstract ᅟ.

Conformational ordering of biomolecules in the gas phase: nitrogen collision cross sections measured on a prototype high resolution drift tube ion mobility-mass spectrometer

Ion mobility-mass spectrometry measurements which describe the gas-phase scaling of molecular size and mass are of both fundamental and pragmatic utility. Fundamentally, such measurements expand our understanding of intrinsic intramolecular folding forces in the absence of solvent. Practically, reproducible transport properties, such as gas-phase collision cross-section (CCS), are analytically useful metrics for identification and characterization purposes. Here, we report 594 CCS values obtained in nitrogen drift gas on an electrostatic drift tube ion mobility-mass spectrometry (IM-MS) instrument. The instrument platform is a newly developed prototype incorporating a uniform-field drift tube bracketed by electrodynamic ion funnels and coupled to a high resolution quadrupole time-of-flight mass spectrometer. The CCS values reported here are of high experimental precision (±0.5% or better) and represent four chemically distinct classes of molecules (quaternary ammonium salts, lipids, peptides, and carbohydrates), which enables structural comparisons to be made between molecules of different chemical compositions for the rapid “omni-omic” characterization of complex biological samples. Comparisons made between helium and nitrogen-derived CCS measurements demonstrate that nitrogen CCS values are systematically larger than helium values; however, general separation trends between chemical classes are retained regardless of the drift gas. These results underscore that, for the highest CCS accuracy, care must be exercised when utilizing helium-derived CCS values to calibrate measurements obtained in nitrogen, as is the common practice in the field.

IMSPeptider: A computational peptide collision cross-section area calculator based on a novel molecular dynamics simulation protocol

Introduction of ion mobility mass spectrometry (IMS/MS) into the proteomic workflow provides an orthogonal separation to the widely used LC-MS platforms. IMS also provides structural information that could facilitate peptide identification. However, the lack of tools capable of predictive power in a high-throughput fashion makes peptide global profiling quite challenging. To target this issue, a computational workflow was developed based on biophysical principles to predict the collision cross-section area (CCS) of peptides as measured from IMS/MS experiments. Hosted on a web server, it allows the user to input a primary sequence (query) and retrieve information on peptide structure, sequence, and corresponding CCS. The current version is designed to identify peptide sequences up to 23 residues in length, in its higher charge state, based on a match of the molecule m/z and CCS. The protocol was validated against a 128-sequences-dataset and CCS predicted within 2.8% average error.

Separation of a set of peptide sequence isomers using differential ion mobility spectrometry

Protein identification in bottom-up proteomics requires disentangling isomers of proteolytic peptides, a major class of which are sequence inversions. Their separation using ion mobility spectrometry (IMS) has been limited to isomeric pairs. Here we demonstrate baseline separation of all seven 8-mer tryptic peptide isomers using differential IMS. Evaluation of peak capacity implies that even larger libraries should be resolved for heavier peptides with higher charge states.

Separation of peptide isomers with variant modified sites by high-resolution differential ion mobility spectrometry

Many proteins and proteolytic peptides incorporate the same post-translational modification (PTM) at different sites, creating multiple localization variants with different functions or activities that may coexist in cells. Current analytical methods based on liquid chromatography (LC) followed by tandem mass spectrometry (MS/MS) are challenged by such isomers that often coelute in LC and/or produce nonunique fragment ions. The application of ion mobility spectrometry (IMS) was explored, but success has been limited by insufficient resolution. We show that high-resolution differential ion mobility spectrometry (FAIMS) employing helium-rich gases can readily separate phosphopeptides with variant modification sites. Use of He/N(2) mixtures containing up to 74% He has allowed separating to >95% three monophosphorylated peptides of identical sequence. Similar separation was achieved at 50% He, using an elevated electric field. Bisphosphorylated isomers that differ in only one modification site were separated to the same extent. We anticipate FAIMS capabilities for such separations to extend to other PTMs.

Machine learning based prediction for peptide drift times in ion mobility spectrometry

MOTIVATION

Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting.

RESULTS

When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4).

AVAILABILITY

The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php

CONTACT

rds@pnl.gov

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Determination of sequence-specific intrinsic size parameters from cross sections for 162 tripeptides

Ion mobility and mass spectrometry techniques have been used to measure cross sections for 162 tripeptide sequences (27 different sets of six sequence isomers). The isomers have the general forms ABC, ACB, BAC, BCA, CAB, and CBA, where A corresponds to the amino acids Asp, Glu, or Gly, B corresponds to Lys, Arg, or Leu, and C corresponds to Phe, Tyr, or Ser. From these data, we derive a set of size parameters for individual amino acids that reflect the position of the amino acid in the sequence. These sequence-specific intrinsic size parameters (SSISPs) are used to retrodict cross-section values for the 162 measured sequences and to predict cross sections for all remaining tripeptide sequences (567 different sequences) that are comprised of these residues. In several types of peptide compositions, the position of the amino acid in the sequence has a significant impact on the parameter that is derived. For example, the sequence-specific intrinsic size parameter for leucine in the third position of a peptide (SSISP(Leu3)) is approximately 10% larger than SSISP(Leu1). On average, cross sections that are derived using SSISPs provide a better representation of the experimental value than those derived from composition only intrinsic size parameters, derived as described previously (Valentine et al. J. Phys. Chem. 1999, 103, 1203). Finally, molecular modeling techniques are used to derive some insight into the origin of cross-section differences that arise from sequence variation.

Quantitative structure–property relationship studies for collision cross sections of 579 singly protonated peptides based on a novel descriptor as molecular graph fingerprint (MoGF)

Aiming at ion mobility spectrometry (IMS), computer-assisted ion mobility prediction (CAIMP) has been recently developed to simulate and predict diverse IMS behaviors in assistance of mathematics and computer science. Of that, quantitative structure-property relationship (QSPR) plays a vital role, dedicating to predict properties of unknown samples by creating statistical model based on known samples. In QSPR, the key lies in how to transform structural characteristics of target compounds into a group of numerical codes. In consideration that future IMS applications may mainly focus on intricate drug/biological systems, a novel molecular structural characterization method referring to molecular graphic fingerprint (MoGF) is proposed in this paper. In MoGF approach, radical distribution function is employed to map intrinsic interatomic correlations into a coordinate system according to a reasonable sampling interval, thus forming the characteristic graph curve which is rich in information on molecular structural characteristics, possessing of great merits in easy calculation, independent of experiments, large information contents, explicit structural meanings and intuitive expressions, etc. Consequently, MoGF is utilized to QSPR studies on 579 singly protonated peptide collision cross sections, and the constructed partial least square (PLS) regression model is confirmed to be robust and predictable by rigorous both internal and external validations, with statistics as r2 = 0.991, q2 = 0.990, RMSEE = 5.526, RMSCV = 5.572, q2(ext) = 0.990, r2(ext) = 0.990, r2(0,ext) = 0.990, r'2(0,ext) = 0.990, k = 1.003, k' = 0.996 and RMSEP = 5.561, respectively.

Mapping the human plasma proteome by SCX-LC-IMS-MS

The advent of on-line multidimensional liquid chromatography-mass spectrometry has significantly impacted proteomic analyses of complex biological fluids such as plasma. However, there is general agreement that additional advances to enhance the peak capacity of such platforms are required to enhance the accuracy and coverage of proteome maps of such fluids. Here, we describe the combination of strong-cation-exchange and reversed-phase liquid chromatographies with ion mobility and mass spectrometry as a means of characterizing the complex mixture of proteins associated with the human plasma proteome. The increase in separation capacity associated with inclusion of the ion mobility separation leads to generation of one of the most extensive proteome maps to date. The map is generated by analyzing plasma samples of five healthy humans; we report a preliminary identification of 9087 proteins from 37,842 unique peptide assignments. An analysis of expected false-positive rates leads to a high-confidence identification of 2928 proteins. The results are catalogued in a fashion that includes positions and intensities of assigned features observed in the datasets as well as pertinent identification information such as protein accession number, mass, and homology score/confidence indicators. Comparisons of the assigned features reported here with other datasets shows substantial agreement with respect to the first several hundred entries; there is far less agreement associated with detection of lower abundance components.

Split-Field Drift Tube/Mass Spectrometry and Isotopic Labeling Techniques for Determination of Single Amino Acid Polymorphisms

A combination of split-field drift tube/mass spectrometry and isotopic labeling techniques is evaluated as a means of identifying single amino acid polymorphisms (SAAPs) in proteins. The method is demonstrated using cytochromec (equine and bovine) and hemoglobin (bovine and sheep). For these studies, proteins from different species are digested with trypsin, and the peptides are labeled at primary amine groups [using either a light (H(3))- or heavy (D(3))-isotopic reagent]. SAAP analysis is carried out by mixing the light-labeled peptides of one species with the heavy-labeled peptides of the other and electrospraying the resulting mixture into a split-field drift tube/mass spectrometer. Peptides having the same sequence in both species appear as doublets in the mass spectrum [shifted in mass-to-charge (m/z) according to the number of incorporated labels]; additionally, these species have identical mobility distributions. Peptides having sequences that differ by one amino acid appear as peaks in the mass spectrum that are shifted in m/z according to the mass difference associated with the SAAP and the number of incorporated labels. The ion mobility distributions for these peptides (differing by only a single amino acid) can often be rationalized by their expected similarities or differences providing additional evidence that they are related. In all, 12 and 26 peptide variants (between species) corresponding to 5 and 11 amino acid polymorphisms have been identified for the cytochrome c and hemoglobin protein samples, respectively.

Characterizing the Structures and Folding of Free Proteins Using 2-D Gas-Phase Separations: Observation of Multiple Unfolded Conformers

Understanding the 3-D structure and dynamics of proteins and other biological macromolecules in various environments is among the central challenges of chemistry. Electrospray ionization can often transfer ions from solution to gas phase with only limited structural distortion, allowing their profiling using mass spectrometry and other gas-phase approaches. Ion mobility spectrometry (IMS) can separate and characterize macroion conformations with high sensitivity and speed. However, IMS separation power is generally insufficient for full resolution of major structural variants of protein ions and elucidation of their interconversion dynamics. Here we report characterization of macromolecular conformations using field asymmetric waveform IMS (FAIMS) coupled to conventional IMS in conjunction with mass spectrometry. The collisional heating of ions in the electrodynamic funnel trap between FAIMS and IMS stages enables investigating the structural evolution of particular isomeric precursors as a function of the intensity and duration of activation that can be varied over large ranges. These new capabilities are demonstrated for ubiquitin and cytochrome c, two common model proteins for structure and folding studies. For nearly all charge states, two-dimensional FAIMS/IMS separations distinguish many more conformations than either FAIMS or IMS alone, including some with very low abundance. For cytochrome c in high charge states, we find several abundant "unfolded" isomer series not distinguishable by IMS, possibly corresponding to different "string of beads" geometries. The unfolding of specific ubiquitin conformers selected by FAIMS has been studied by employing their heating in the FAIMS/IMS interface.

Feasibility of higher-order differential ion mobility separations using new asymmetric waveforms

Technologies for separating and characterizing ions based on their transport properties in gases have been around for three decades. The early method of ion mobility spectrometry (IMS) distinguished ions by absolute mobility that depends on the collision cross section with buffer gas atoms. The more recent technique of field asymmetric waveform IMS (FAIMS) measures the difference between mobilities at high and low electric fields. Coupling IMS and FAIMS to soft ionization sources and mass spectrometry (MS) has greatly expanded their utility, enabling new applications in biomedical and nanomaterials research. Here, we show that time-dependent electric fields comprising more than two intensity levels could, in principle, effect an infinite number of distinct differential separations based on the higher-order terms of expression for ion mobility. These analyses could employ the hardware and operational procedures similar to those utilized in FAIMS. Methods up to the 4th or 5th order (where conventional IMS is 1st order and FAIMS is 2nd order) should be practical at field intensities accessible in ambient air, with still higher orders potentially achievable in insulating gases. Available experimental data suggest that higher-order separations should be largely orthogonal to each other and to FAIMS, IMS, and MS.

Mapping the proteome of Drosophila melanogaster: analysis of embryos and adult heads by LC-IMS-MS methods

Multidimensional separations combined with mass spectrometry are used to study the proteins that are present in two states of Drosophila melanogaster: the whole embryo and the adult head. The approach includes the incorporation of a gas-phase separation dimension in which ions are dispersed according to differences in their mobilities and is described as a means of providing a detailed analytical map of the proteins that are present. Overall, we find evidence for 1133 unique proteins. In total, 780 are identified in the head, and 660 are identified in the embryo. Only 307 proteins are in common to both developmental stages, indicating that there are significant differences in these proteomes. A comparison of the proteome to a database of mRNAs that are found from analysis by cDNA approaches (i.e., transcriptome) also shows little overlap. All of this information is discussed in terms of the relationship between the predicted genome, and measured transcriptomes and proteomes. Additionally, the merits and weaknesses of current technologies are assessed in some detail.

Two-dimensional gas-phase separations coupled to mass spectrometry for analysis of complex mixtures

Ion mobility spectrometry (IMS) has been explored for decades, and its versatility in separation and identification of gas-phase ions is well established. Recently, field asymmetric waveform IMS (FAIMS) has been gaining acceptance in similar applications. Coupled to mass spectrometry (MS), both IMS and FAIMS have shown the potential for broad utility in proteomics and other biological analyses. A major attraction of these separations is extremely high speed, exceeding that of condensed-phase alternatives by orders of magnitude. However, modest separation peak capacities have limited the utility of FAIMS and IMS for analyses of complex mixtures. We report 2-D gas-phase separations that join FAIMS to IMS, in conjunction with high-resolution and accuracy time-of-flight (TOF) MS. Implementation of FAIMS/IMS and IMS/MS interfaces using electrodynamic ion funnels greatly improves sensitivity. Evaluation of FAIMS/IMS/TOF performance for a protein mixture tryptic digest reveals high orthogonality between FAIMS and IMS dimensions and, hence, the benefit of FAIMS filtering prior to IMS/MS. The effective peak capacities in analyses of tryptic peptides are approximately 500 for FAIMS/IMS separations and approximately 10(6) for 3-D FAIMS/IMS/MS, providing a potential platform for ultrahigh-throughput analyses of complex mixtures.

Observation of conserved solution-phase secondary structure in gas-phase tryptic peptides

Results from ion mobility studies of tryptic peptides suggest that, in some cases, the gas-phase structures can be related to the solution-phase structure of the parent protein. The interpretation of ion mobility measurements is supported by results from molecular modeling and H/D exchange experiments on the same peptides. This study clearly illustrates the utility of IM-MS for screening complex mixtures for peptides having intrinsically stable secondary/tertiary structures, and/or posttranslational modification.

Prediction of peptide ion collision cross sections from topological molecular structure and amino acid parameters

Quantitative structure-property relationships (QSPRs) have been developed to predict the ion mobility spectrometry (IMS) collision cross sections of singly protonated lysine-terminated peptides using information derived from topological molecular structure and various amino acid parameters. The primary amino acid sequence alone is sufficient to accurately predict the collision cross section. The models were built using multiple linear regression (MLR) and computational neural networks (CNNs). The best MLR model found contains six descriptors and predicts 94 of 113 peptides (83%) to within 2% of their experimentally determined values. The best CNN model using the same six descriptors predicts 105 of the 113 peptides (93%) to within 2% of their experimentally determined values. The best overall CNN model, using a different set of six descriptors, predicts 109 of the 113 peptides (96%) to within 2% of their experimentally determined values. In addition, this model can discriminate among peptides having identical amino acid composition, but differing in primary amino acid sequence. This represents a capability not found in previously described models. The descriptors used in the models presented may provide some insight into the nature of peptide ion folding in the gas phase.