Abstract:Achieving (bio)macromolecular structural assignment from the interpretation of ion mobility spectrometry (IMS) experiments requires successful comparison with computer modeling. Replica-exchange molecular dynamics simulations with suitable force fields not only offer a convenient framework to locate relevant conformations, especially in the case of multiple-funnel energy landscapes, but they are also well suited to statistical analyses. In the present paper, we discuss two extensions of the method used to impr… Show more
“…Several enhanced sampling techniques allow the species to overcome energetic barriers and explore other conformational 5À on the Synapt G1 and (C) Reconstructed collision cross section distributions for the ion [(dTG 4 T) basins, for example simulated annealing molecular dynamics, [57][58][59][60] replica-exchange molecular dynamics [61][62][63] and adaptively biased molecular dynamics. [64,65] On the other hand, when the molecular system taken into account is too big, as for instance large multiprotein complexes (>500 kDa), all the aforementioned atomistic methods are still too computationally expensive, but some qualitative models can be built instead, such as (bead-type) coarse grained models. [37,[66][67][68][69] Force fields A possible pitfall intrinsic in classical MD simulations in gas phase is related to the use of force fields (FFs) that are presently parameterized based on high-level quantum calculations, but eventually tested and tuned in aqueous phase (with either explicit or implicit water models).…”
Ion mobility spectrometry experiments allow the mass spectrometrist to determine an ion's rotationally averaged collision cross section Ω EXP . Molecular modelling is used to visualize what ion three-dimensional structure(s) is(are) compatible with the experiment. The collision cross sections of candidate molecular models have to be calculated, and the resulting Ω CALC are compared with the experimental data. Researchers who want to apply this strategy to a new type of molecule face many questions: (1) What experimental error is associated with Ω EXP determination, and how to estimate it (in particular when using a calibration for traveling wave ion guides)? (2) How to generate plausible 3D models in the gas phase? (3) Different collision cross section calculation models exist, which have been developed for other analytes than mine. Which one(s) can I apply to my systems? To apply ion mobility spectrometry to nucleic acid structural characterization, we explored each of these questions using a rigid structure which we know is preserved in the gas phase: the tetramolecular G-quadruplex [dTGGGGT] 4 , and we will present these detailed investigation in this tutorial. Additional supporting information may be found in the online version of this article at the publisher's web site.Keywords: ion mobility spectrometry; collision cross section; structure; nucleic acids; simulations; molecular modeling; gas-phase ion structure
IntroductionElectrospray mass spectrometry (ESI-MS) in native conditions can preserve the structures of biomolecules and separate them according to their mass-to-charge ratios. [1,2] Ion mobility spectrometry (IMS) separates ions according to their size-to-charge ratios in the gas-phase. [3][4][5][6] Size is related to the mass (or number of atoms) and to the three-dimensional shape of a molecule. Therefore, by hyphenating IMS to mass spectrometry (IM-MS), one can sort ions according to both mass and shape. The challenge is to decipher structural information from ion mobility experiments. [7][8][9] The physical quantity characterizing the shape is the collision cross section (CCS), [10] which will be introduced in more detail in the Section on Ion mobility and collision cross sections. The present tutorial clarifies how to interpret CCS measurements in terms of three-dimensional structure for ions larger than 100 atoms extracted from the solution by electrospray ionization (ESI). The gold standard is to match CCSs experimentally obtained from IMS with CCSs calculated for modelling three-dimensional structures of the ion of interest. We will discuss the factors that can affect the precision (reproducibility) and accuracy in both the measurements and the calculations. Understanding each of these factors is crucial to interpret quantitative matches confidently and assess the meaningfulness of structural assignments.On the experimental side, we will describe the determination of CCS from traveling wave ion mobility spectrometers and from drift tube ion mobility spectrometers, with example data recorde...
“…Several enhanced sampling techniques allow the species to overcome energetic barriers and explore other conformational 5À on the Synapt G1 and (C) Reconstructed collision cross section distributions for the ion [(dTG 4 T) basins, for example simulated annealing molecular dynamics, [57][58][59][60] replica-exchange molecular dynamics [61][62][63] and adaptively biased molecular dynamics. [64,65] On the other hand, when the molecular system taken into account is too big, as for instance large multiprotein complexes (>500 kDa), all the aforementioned atomistic methods are still too computationally expensive, but some qualitative models can be built instead, such as (bead-type) coarse grained models. [37,[66][67][68][69] Force fields A possible pitfall intrinsic in classical MD simulations in gas phase is related to the use of force fields (FFs) that are presently parameterized based on high-level quantum calculations, but eventually tested and tuned in aqueous phase (with either explicit or implicit water models).…”
Ion mobility spectrometry experiments allow the mass spectrometrist to determine an ion's rotationally averaged collision cross section Ω EXP . Molecular modelling is used to visualize what ion three-dimensional structure(s) is(are) compatible with the experiment. The collision cross sections of candidate molecular models have to be calculated, and the resulting Ω CALC are compared with the experimental data. Researchers who want to apply this strategy to a new type of molecule face many questions: (1) What experimental error is associated with Ω EXP determination, and how to estimate it (in particular when using a calibration for traveling wave ion guides)? (2) How to generate plausible 3D models in the gas phase? (3) Different collision cross section calculation models exist, which have been developed for other analytes than mine. Which one(s) can I apply to my systems? To apply ion mobility spectrometry to nucleic acid structural characterization, we explored each of these questions using a rigid structure which we know is preserved in the gas phase: the tetramolecular G-quadruplex [dTGGGGT] 4 , and we will present these detailed investigation in this tutorial. Additional supporting information may be found in the online version of this article at the publisher's web site.Keywords: ion mobility spectrometry; collision cross section; structure; nucleic acids; simulations; molecular modeling; gas-phase ion structure
IntroductionElectrospray mass spectrometry (ESI-MS) in native conditions can preserve the structures of biomolecules and separate them according to their mass-to-charge ratios. [1,2] Ion mobility spectrometry (IMS) separates ions according to their size-to-charge ratios in the gas-phase. [3][4][5][6] Size is related to the mass (or number of atoms) and to the three-dimensional shape of a molecule. Therefore, by hyphenating IMS to mass spectrometry (IM-MS), one can sort ions according to both mass and shape. The challenge is to decipher structural information from ion mobility experiments. [7][8][9] The physical quantity characterizing the shape is the collision cross section (CCS), [10] which will be introduced in more detail in the Section on Ion mobility and collision cross sections. The present tutorial clarifies how to interpret CCS measurements in terms of three-dimensional structure for ions larger than 100 atoms extracted from the solution by electrospray ionization (ESI). The gold standard is to match CCSs experimentally obtained from IMS with CCSs calculated for modelling three-dimensional structures of the ion of interest. We will discuss the factors that can affect the precision (reproducibility) and accuracy in both the measurements and the calculations. Understanding each of these factors is crucial to interpret quantitative matches confidently and assess the meaningfulness of structural assignments.On the experimental side, we will describe the determination of CCS from traveling wave ion mobility spectrometers and from drift tube ion mobility spectrometers, with example data recorde...
“…Although increasingly successful for interpreting ion mobility measurements [4,5,8,24], conventional molecular dynamics simulations may also face difficulties in locating conformations compatible with experiments due to either intrinsic force field limitations, or insufficient sampling time [24]. One useful strategy to facilitate interpretation of IMS measurements consists in guiding the sampling toward the regions of experimental interest, using a dedicated order parameter that hopefully mimics the measured property.…”
mentioning
confidence: 99%
“…One useful strategy to facilitate interpretation of IMS measurements consists in guiding the sampling toward the regions of experimental interest, using a dedicated order parameter that hopefully mimics the measured property. The collision cross section itself cannot be used actively in such MD simulations, because it is not an explicit function of the coordinates (no gradient is available), but the gyration radius turns out to be a reasonable approximation [24]. It should be stressed here that the biased MD approach does not aim at approximating the CCS by a geometrical quantity, but to take advantage of the relation between the CCS and the molecular structure in order to save computational time in the production of realistic candidate structures.…”
mentioning
confidence: 99%
“…One straightforward biasing method uses a so-called umbrella potential, in which conformations with prescribed values of the order parameter are favored. However, locating conformations with prescribed values of the collision cross section was still a challenge with this approach, the parameters for the guiding potential having to be adjusted by trial and error [24].…”
mentioning
confidence: 99%
“…Following our previous work [24], we have chosen to test these ideas on selected peptides for which experimental IMS data are available. Triply protonated bradykinin was recently found by the Clemmer group [27,28] to exhibit three distinct signatures in ion mobility spectrometry upon specific activation.…”
Following a recent effort [J. Am. Soc. Mass Spectrom. 23, 386-396 (2012)], we continue to explore computational methodologies for generating molecular conformations to support collisional cross sections suggested by ion mobility measurements. Here, adaptively biased molecular dynamics (ABMD) simulations are used to sample the configuration space and to achieve flat-histogram sampling along the reaction coordinates of the first two moments of the gyration tensor. The method is tested and compared with replica-exchange simulations on triplyprotonated bradykinin and on a larger 25-residue peptide. It is found to have a much higher efficiency for producing large sets of conformations in a broad range of diffusion cross-sections, whereas it does not compete with conventional replica-exchange molecular dynamics in locating the lowest-energy structure. Nevertheless, the broad sampling obtained from the ABMD method allows to quantitatively correlate the diffusion cross-section Ω with other geometric order parameters that have simpler interpretation. The strong correlations found between the diffusion cross-section and the radius of gyration, the surface area and the volume of the convex hull suggest an optimal template for accurately mimicking the variations of Ω in a broad range of conformations, using only geometrical information and doing so at a very moderate computational cost. The existence of such a correlation is confirmed on the much larger protein α-lactalbumin.
An ultrafast shape-recognition technique was used to analyze the phase transition of finite-size clusters, which, according to our research, has not yet been accomplished. The shape of clusters is the unique property that distinguishes clusters from bulk systems and is comprehensive and natural for structural analysis. In this study, an isothermal molecular dynamics simulation was performed to generate a structural database for shape recognition of Ag-Cu metallic clusters using empirical many-body potential. The probability contour of the shape similarity exhibits the characteristics of both the specific heat and Lindemann index (bond-length fluctuation) of clusters. Moreover, our implementation of the substructure to the probability of shapes provides a detailed observation of the atom/shell-resolved analysis, and the behaviors of the clusters were reconstructed based on the statistical information. The method is efficient, flexible, and applicable in any type of finite-size system, including polymers and nanostructures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.