NaViA: a program for the visual analysis of complex mass spectra

Quetschlich, Daniel; Esser, Tim K.; Newport, Thomas D.; Fiorentino, Francesco; Shutin, Denis; Chen, Siyun; Davis, Rachel E.; Lovera, Silvia; Liko, Idlir; Stansfeld, Phillip J.; Robinson, Carol V.

doi:10.1093/bioinformatics/btab436

Cited by 11 publications

(9 citation statements)

References 12 publications

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“…Ease of interpretation is especially important for integration of these tools into industry settings, in addition to rigorous validation and automation of these methods . We also envision a future in which multiple different tools can be integrated onto the same platform to provide complementary and/or supporting information, including development of field-wide scoring metrics, which has been the focus of some recent efforts already. ,, …”

Section: Discussionmentioning

confidence: 99%

Approaches to Heterogeneity in Native Mass Spectrometry

Rolland

Prell

2021

Chem. Rev.

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Native mass spectrometry (MS) is aimed at preserving and determining the native structure, composition, and stoichiometry of biomolecules and their complexes from solution after they are transferred into the gas phase. Major improvements in native MS instrumentation and experimental methods over the past few decades have led to a concomitant increase in the complexity and heterogeneity of samples that can be analyzed, including protein–ligand complexes, protein complexes with multiple coexisting stoichiometries, and membrane protein–lipid assemblies. Heterogeneous features of these biomolecular samples can be important for understanding structure and function. However, sample heterogeneity can make assignment of ion mass, charge, composition, and structure very challenging due to the overlap of tens or even hundreds of peaks in the mass spectrum. In this review, we cover data analysis, experimental, and instrumental advances and strategies aimed at solving this problem, with an in-depth discussion of theoretical and practical aspects of the use of available deconvolution algorithms and tools. We also reflect upon current challenges and provide a view of the future of this exciting field.

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Section: Discussionmentioning

confidence: 99%

Approaches to Heterogeneity in Native Mass Spectrometry

Rolland

Prell

2021

Chem. Rev.

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“…This is can be done manually or via computational spectral deconvolution. A variety of softwares exist to enable this, including open source softwares such as UniDec, which deconvolutes spectra using Bayesian inference, , NaVia, a program to augment manual assignment, as well as vendor-provided software. Software for analyzing native IM-MS experiments presents several challenges, as reviewed in ref .…”

Section: Ms-based Techniques For Studying Endogenous Complexesmentioning

confidence: 99%

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Rogawski

Sharon

2021

Chem. Rev.

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Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

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“…The desalted solution was analyzed by LC-MS and the obtained mass spectra were deconvoluted with the NaViA program. 25 The expected mass spectrum of insulin with a single PLAA modification was observed (Fig. 4A and ESI, † Fig.…”

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confidence: 94%