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  1. Article ; Online: A Functional Bayesian Model for Hydrogen-Deuterium Exchange Mass Spectrometry.

    Crook, Oliver M / Gittens, Nathan / Chung, Chun-Wa / Deane, Charlotte M

    Journal of proteome research

    2023  Volume 22, Issue 9, Page(s) 2959–2972

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Hydrogen Deuterium Exchange-Mass Spectrometry ; Bayes Theorem ; Deuterium/chemistry ; Deuterium Exchange Measurement/methods ; Nuclear Proteins ; Mass Spectrometry/methods ; Transcription Factors
    Chemical Substances Deuterium (AR09D82C7G) ; Nuclear Proteins ; Transcription Factors
    Language English
    Publishing date 2023-08-15
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.3c00297
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Challenges and Opportunities for Bayesian Statistics in Proteomics.

    Crook, Oliver M / Chung, Chun-Wa / Deane, Charlotte M

    Journal of proteome research

    2022  Volume 21, Issue 4, Page(s) 849–864

    Abstract: Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of ... ...

    Abstract Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.
    MeSH term(s) Bayes Theorem ; Machine Learning ; Probability ; Proteomics ; Uncertainty
    Language English
    Publishing date 2022-03-08
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.1c00859
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry.

    Crook, Oliver M / Chung, Chun-Wa / Deane, Charlotte M

    Communications biology

    2022  Volume 5, Issue 1, Page(s) 588

    Abstract: Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting ... ...

    Abstract Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting significant changes to this rate requires a statistical test. To determine rates of incorporation, HDX-MS measurements are frequently made over a time course. However, current statistical testing procedures ignore the correlations in the temporal dimension of the data. Using tools from functional data analysis, we develop a testing procedure that explicitly incorporates a model of hydrogen deuterium exchange. To further improve statistical power, we develop an empirical Bayes version of our method, allowing us to borrow information across peptides and stabilise variance estimates for low sample sizes. Our approach has increased power, reduces false positives and improves interpretation over linear model-based approaches. Due to the improved flexibility of our method, we can apply it to a multi-antibody epitope-mapping experiment where current approaches are inapplicable due insufficient flexibility. Hence, our approach allows HDX-MS to be applied in more experimental scenarios and reduces the burden on experimentalists to produce excessive replicates. Our approach is implemented in the R-package "hdxstats": https://github.com/ococrook/hdxstats .
    MeSH term(s) Bayes Theorem ; Deuterium/chemistry ; Deuterium Exchange Measurement/methods ; Hydrogen Deuterium Exchange-Mass Spectrometry ; Mass Spectrometry/methods ; Peptides
    Chemical Substances Peptides ; Deuterium (AR09D82C7G)
    Language English
    Publishing date 2022-06-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-022-03517-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry

    Oliver M. Crook / Chun-wa Chung / Charlotte M. Deane

    Communications Biology, Vol 5, Iss 1, Pp 1-

    2022  Volume 10

    Abstract: A statistical analysis approach for HDX-MS time series data incorporates correlations in time, reducing false positives and improving statistical power and data interpretation. ...

    Abstract A statistical analysis approach for HDX-MS time series data incorporates correlations in time, reducing false positives and improving statistical power and data interpretation.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: The 17

    Ciulli, Alessio / O'Connor, Suzanne / Chung, Chun-Wa / Hartung, Ingo V / Testa, Andrea / Daniels, Danette L / Heitman, Laura H

    ChemMedChem

    2023  Volume 18, Issue 20, Page(s) e202300464

    Abstract: ... The ... ...

    Abstract The 17
    MeSH term(s) Chemistry, Pharmaceutical ; Drug Design ; Europe ; Proteolysis ; South Africa
    Language English
    Publishing date 2023-10-10
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2218496-X
    ISSN 1860-7187 ; 1860-7179
    ISSN (online) 1860-7187
    ISSN 1860-7179
    DOI 10.1002/cmdc.202300464
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Challenges and Opportunities for Bayesian Statistics in Proteomics

    Crook, Oliver M. / Chung, Chun-wa / Deane, Charlotte M.

    Journal of proteome research. 2022 Mar. 08, v. 21, no. 4

    2022  

    Abstract: Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of ... ...

    Abstract Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.
    Keywords Bayesian theory ; experimental design ; instrumentation ; proteome ; proteomics ; research ; separation ; uncertainty
    Language English
    Dates of publication 2022-0308
    Size p. 849-864.
    Publishing place American Chemical Society
    Document type Article
    ZDB-ID 2078618-9
    ISSN 1535-3907 ; 1535-3893
    ISSN (online) 1535-3907
    ISSN 1535-3893
    DOI 10.1021/acs.jproteome.1c00859
    Database NAL-Catalogue (AGRICOLA)

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  7. Article: Small molecule bromodomain inhibitors: extending the druggable genome.

    Chung, Chun-Wa

    Progress in medicinal chemistry

    2012  Volume 51, Page(s) 1–55

    MeSH term(s) Acetylation ; Amino Acid Sequence ; Animals ; Autoimmune Diseases/drug therapy ; Autoimmune Diseases/genetics ; Communicable Diseases/drug therapy ; Drug Design ; Epigenesis, Genetic ; Humans ; Metabolic Diseases/drug therapy ; Metabolic Diseases/genetics ; Metabolic Diseases/metabolism ; Molecular Sequence Data ; Neoplasms/drug therapy ; Neoplasms/genetics ; Protein Structure, Tertiary ; Proteins/antagonists & inhibitors ; Proteins/chemistry ; Proteins/genetics ; Small Molecule Libraries
    Chemical Substances Proteins ; Small Molecule Libraries
    Language English
    Publishing date 2012
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 209306-6
    ISSN 1875-7863 ; 0079-6468
    ISSN (online) 1875-7863
    ISSN 0079-6468
    DOI 10.1016/B978-0-12-396493-9.00001-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Design and Characterization of 1,3-Dihydro-2

    Bamborough, Paul / Chung, Chun-Wa / Goodwin, Nicole C / Mitchell, Darren J / Neipp, Christopher E / Phillipou, Alex / Preston, Alex / Prinjha, Rab K / Soden, Peter E / Watson, Robert J / Demont, Emmanuel H

    ACS medicinal chemistry letters

    2023  Volume 14, Issue 9, Page(s) 1231–1236

    Abstract: The 1,3-dihydro- ... ...

    Abstract The 1,3-dihydro-2
    Language English
    Publishing date 2023-08-14
    Publishing country United States
    Document type Journal Article
    ISSN 1948-5875
    ISSN 1948-5875
    DOI 10.1021/acsmedchemlett.3c00242
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Correction: Cryo-EM in drug discovery.

    Ceska, Tom / Chung, Chun-Wa / Cooke, Rob / Phillips, Chris / Williams, Pamela A

    Biochemical Society transactions

    2019  Volume 47, Issue 1, Page(s) 487

    Language English
    Publishing date 2019-02-28
    Publishing country England
    Document type Journal Article ; Published Erratum
    ZDB-ID 184237-7
    ISSN 1470-8752 ; 0300-5127
    ISSN (online) 1470-8752
    ISSN 0300-5127
    DOI 10.1042/BST-2018-0267C_COR
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Efficient Ligand Discovery Using Sulfur(VI) Fluoride Reactive Fragments.

    Aatkar, Arron / Vuorinen, Aini / Longfield, Oliver E / Gilbert, Katharine / Peltier-Heap, Rachel / Wagner, Craig D / Zappacosta, Francesca / Rittinger, Katrin / Chung, Chun-Wa / House, David / Tomkinson, Nicholas C O / Bush, Jacob T

    ACS chemical biology

    2023  Volume 18, Issue 9, Page(s) 1926–1937

    Abstract: Sulfur(VI) fluorides (SFs) have emerged as valuable electrophiles for the design of "beyond-cysteine" covalent inhibitors and offer potential for expansion of the liganded proteome. Since SFs target a broad range of nucleophilic amino acids, they deliver ...

    Abstract Sulfur(VI) fluorides (SFs) have emerged as valuable electrophiles for the design of "beyond-cysteine" covalent inhibitors and offer potential for expansion of the liganded proteome. Since SFs target a broad range of nucleophilic amino acids, they deliver an approach for the covalent modification of proteins without requirement for a proximal cysteine residue. Further to this, libraries of reactive fragments present an innovative approach for the discovery of ligands and tools for proteins of interest by leveraging a breadth of mass spectrometry analytical approaches. Herein, we report a screening approach that exploits the unique properties of SFs for this purpose. Libraries of SF-containing reactive fragments were synthesized, and a direct-to-biology workflow was taken to efficiently identify hit compounds for CAII and BCL6. The most promising hits were further characterized to establish the site(s) of covalent modification, modification kinetics, and target engagement in cells. Crystallography was used to gain a detailed molecular understanding of how these reactive fragments bind to their target. It is anticipated that this screening protocol can be used for the accelerated discovery of "beyond-cysteine" covalent inhibitors.
    MeSH term(s) Cysteine/chemistry ; Fluorides ; Ligands ; Amino Acids ; Sulfur
    Chemical Substances Cysteine (K848JZ4886) ; Fluorides (Q80VPU408O) ; Ligands ; Amino Acids ; Sulfur (70FD1KFU70)
    Language English
    Publishing date 2023-04-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1554-8937
    ISSN (online) 1554-8937
    DOI 10.1021/acschembio.3c00034
    Database MEDical Literature Analysis and Retrieval System OnLINE

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