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      Discovery of XL01126: A Potent, Fast, Cooperative, Selective, Orally Bioavailable, and Blood–Brain Barrier Penetrant PROTAC Degrader of Leucine-Rich Repeat Kinase 2

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          Abstract

          Leucine-rich repeat kinase 2 (LRRK2) is one of the most promising targets for Parkinson’s disease. LRRK2-targeting strategies have primarily focused on type 1 kinase inhibitors, which, however, have limitations as the inhibited protein can interfere with natural mechanisms, which could lead to undesirable side effects. Herein, we report the development of LRRK2 proteolysis targeting chimeras (PROTACs), culminating in the discovery of degrader XL01126, as an alternative LRRK2-targeting strategy. Initial designs and screens of PROTACs based on ligands for E3 ligases von Hippel–Lindau (VHL), Cereblon (CRBN), and cellular inhibitor of apoptosis (cIAP) identified the best degraders containing thioether-conjugated VHL ligand VH101. A second round of medicinal chemistry exploration led to qualifying XL01126 as a fast and potent degrader of LRRK2 in multiple cell lines, with DC 50 values within 15–72 nM, D max values ranging from 82 to 90%, and degradation half-lives spanning from 0.6 to 2.4 h. XL01126 exhibits high cell permeability and forms a positively cooperative ternary complex with VHL and LRRK2 (α = 5.7), which compensates for a substantial loss of binary binding affinities to VHL and LRRK2, underscoring its strong degradation performance in cells. Remarkably, XL01126 is orally bioavailable ( F = 15%) and can penetrate the blood–brain barrier after either oral or parenteral dosing in mice. Taken together, these experiments qualify XL01126 as a suitable degrader probe to study the noncatalytic and scaffolding functions of LRRK2 in vitro and in vivo and offer an attractive starting point for future drug development.

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

            The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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              The Perseus computational platform for comprehensive analysis of (prote)omics data.

              A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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                Author and article information

                Journal
                J Am Chem Soc
                J Am Chem Soc
                ja
                jacsat
                Journal of the American Chemical Society
                American Chemical Society
                0002-7863
                1520-5126
                25 August 2022
                21 September 2022
                : 144
                : 37
                : 16930-16952
                Affiliations
                []Centre for Targeted Protein Degradation, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee , Dow Street, Dundee DD1 5EH, United Kingdom
                []Medical Research Council (MRC) Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee , Dow Street, Dundee DD1 5EH, United Kingdom
                [§ ]Research Institute for Medicines (iMed.ULisboa), Faculty of Pharmacy, Universidade de Lisboa , Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
                Author notes
                [* ]Email: a.ciulli@ 123456dundee.ac.uk . Phone: +44(0)1382386230.
                Author information
                https://orcid.org/0000-0003-2659-8007
                https://orcid.org/0000-0001-5185-7694
                https://orcid.org/0000-0002-1696-9815
                https://orcid.org/0000-0002-2140-9185
                https://orcid.org/0000-0002-8654-1670
                Article
                10.1021/jacs.2c05499
                9501899
                36007011
                8c0a3245-6515-4c4f-8e18-02090dc2b227
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 24 May 2022
                Funding
                Funded by: Wellcome Trust, doi 10.13039/100004440;
                Award ID: 094090/Z/10/Z
                Funded by: Ono Pharmaceutical, doi 10.13039/501100013170;
                Award ID: NA
                Funded by: Fundação para a Ciência e a Tecnologia, doi 10.13039/501100001871;
                Award ID: PD/BD/114281/2016
                Funded by: Parkinson''s UK, doi 10.13039/501100000304;
                Award ID: H-1701
                Funded by: Medical Research Council, doi 10.13039/501100000265;
                Award ID: MC_UU_00018/1
                Funded by: FP7 Ideas: European Research Council, doi 10.13039/100011199;
                Award ID: 311460
                Funded by: H2020 Health, doi 10.13039/100010677;
                Award ID: 875510
                Funded by: Wellcome Trust, doi 10.13039/100004440;
                Award ID: 100476/Z/12/Z
                Categories
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                Custom metadata
                ja2c05499
                ja2c05499

                Chemistry
                Chemistry

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