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      Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

      research-article
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      Nature Communications
      Nature Publishing Group UK
      Computational biophysics, Protein structure predictions, Molecular modelling, Ubiquitin ligases

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          Abstract

          Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

          Abstract

          The formation of ternary degrader-protein complexes is a key step in the targeted degradation of proteins of interest. Here, the authors explore the structure and dynamics of such complexes applying high-performance computer simulations augmented with experimental data.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Comparison of simple potential functions for simulating liquid water

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                Author and article information

                Contributors
                alexrd@msu.edu
                huafeng.xu@roivant.com
                woody.sherman@roivant.com
                jesus.izaguirre@roivant.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 October 2022
                6 October 2022
                2022
                : 13
                : 5884
                Affiliations
                [1 ]Roivant Discovery, New York City, NY 10036 USA
                [2 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Computational Mathematics, Science, and Engineering, , Michigan State University, ; East Lansing, MI 48824 USA
                [3 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Biochemistry and Molecular Biology, , Michigan State University, ; East Lansing, MI 48824 USA
                Author information
                http://orcid.org/0000-0002-4520-3894
                http://orcid.org/0000-0003-2006-1620
                http://orcid.org/0000-0003-0568-9866
                http://orcid.org/0000-0003-0771-7074
                http://orcid.org/0000-0001-6159-615X
                http://orcid.org/0000-0001-6985-6657
                http://orcid.org/0000-0003-4278-8265
                http://orcid.org/0000-0002-0353-4126
                http://orcid.org/0000-0001-9901-1276
                http://orcid.org/0000-0002-6295-645X
                http://orcid.org/0000-0002-9640-1380
                http://orcid.org/0000-0001-5447-0452
                http://orcid.org/0000-0002-4687-4884
                Article
                33575
                10.1038/s41467-022-33575-4
                9537307
                b6cd217f-597d-4f8c-9835-aeceff80c0b8
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 February 2022
                : 20 September 2022
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                © The Author(s) 2022

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                computational biophysics,protein structure predictions,molecular modelling,ubiquitin ligases

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