Challenges and Opportunities for Bayesian Statistics in Proteomics

Crook, Oliver M.; Chung, Chun‐wa; Deane, Charlotte M.

doi:10.1021/acs.jproteome.1c00859

Cited by 7 publications

(11 citation statements)

References 109 publications

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“…In addition, the prior distribution acts as regularization for our model parameters and hence rules-out improbable parameters; this stabilizes the inference in our model. For a review of Bayesian methods applied to proteomics data, we refer to Crook, Chung, and Deane …”

Section: Resultsmentioning

confidence: 99%

A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry

Crook,

Gittens,

Chung

et al. 2023

J. Proteome Res.

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Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subjected to environmental changes. Hydrogen−deuterium exchange mass spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions that hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment, improved interpretation in an epitope mapping experiment, and increased sensitivity in a small molecule case-study. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformational changes on binding. Our findings are consistent with the cocrystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data. We also generate HDX-MS data of the bromodomain-containing protein BRD4 in complex with GSK1210151A to demonstrate the increased sensitivity of adopting a Bayesian approach.

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Section: Resultsmentioning

confidence: 99%

A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry

Crook,

Gittens,

Chung

et al. 2023

J. Proteome Res.

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show abstract

“…MDI is a Bayesian integrative or multi-modal clustering method. Bayesian statistics allows quantification of uncertainty through the use of probability distributions and offers a modular framework for data analysis by making dependencies between data and parameters explicit [60]. In MDI, signal sharing is defined by 8/38 the prior on the cluster label of the n th observation in the M modalities/datasets:…”

Section: Multiple Dataset Integrationmentioning

confidence: 99%

Semi-supervised Bayesian integration of multiple spatial proteomics datasets

Coleman,

Breckels,

Waller

et al. 2024

Preprint

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The subcellular localisation of proteins is a key determinant of their function. High-throughput analyses of these localisations can be performed using mass spectrometry-based spatial proteomics, which enables us to examine the localisation and relocalisation of proteins. Furthermore, complementary data sources can provide additional sources of functional or localisation information. Examples include protein annotations and other high-throughput `omic assays. Integrating these modalities can provide new insights as well as additional confidence in results, but existing approaches for integrative analyses of spatial proteomics datasets are limited in the types of data they can integrate and do not quantify uncertainty. Here we propose a semi-supervised Bayesian approach to integrate spatial proteomics datasets with other data sources, to improve the inference of protein sub-cellular localisation. We demonstrate our approach outperforms other transfer-learning methods and has greater flexibility in the data it can model. To demonstrate the flexibility of our approach, we apply our method to integrate spatial proteomics data generated for the parasite Toxoplasma gondii with time-course gene expression data generated over its cell cycle. Our findings suggest that proteins linked to invasion organelles are associated with expression programs that peak at the end of the first cell-cycle. Furthermore, this integrative analysis divides the dense granule proteins into heterogeneous populations suggestive of potentially different functions. Our method is disseminated via the mdir R package available on the lead author's Github.

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“…In addition, the prior distribution acts as regularisation for our model parameters and hence rules-out improbable parameters -this stabilises the inference in our model. For a review of Bayesian methods applied to proteomics data, we refer to Crook et al (2022a).…”

Section: Model Summarymentioning

confidence: 99%

A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry

Crook

Chung

Deane

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Proteins often undergo structural perturbations upon binding to other proteins or ligands or when they are subject to environmental changes. Hydrogen deuterium exchange mass-spectrometry (HDX-MS) can be used to explore conformational changes in proteins by examining differences in the rate of deuterium incorporation in different contexts. To determine deuterium incorporation rates, HDX-MS measurements are typically made over a time course. Recently introduced methods show that incorporating the temporal dimension into the statistical analysis improves power and interpretation. However, these approaches have technical assumptions which hinder their flexibility. Here, we propose a more flexible methodology by reframing these methods in a Bayesian framework. Our proposed framework has improved algorithmic stability, allows us to perform uncertainty quantification, and can calculate statistical quantities that are inaccessible to other approaches. We demonstrate the general applicability of the method by showing it can perform rigorous model selection on a spike-in HDX-MS experiment and improved interpretation in an epitope mapping experiment. Bayesian analysis of an HDX experiment with an antibody dimer bound to an E3 ubiquitin ligase identifies at least two interaction interfaces where previous methods obtained confounding results due to the complexities of conformation change on binding. Our findings are consistent with the co-crystal structure of these proteins, demonstrating a bayesian approach can identify important binding epitopes from HDX data.

show abstract

Challenges and Opportunities for Bayesian Statistics in Proteomics

Cited by 7 publications

References 109 publications

A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry

A Functional Bayesian Model for Hydrogen–Deuterium Exchange Mass Spectrometry

Semi-supervised Bayesian integration of multiple spatial proteomics datasets

A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry

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