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      Uncoupled evolution of the Polycomb system and deep origin of non-canonical PRC1

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          Abstract

          Polycomb group proteins, as part of the Polycomb repressive complexes, are essential in gene repression through chromatin compaction by canonical PRC1, mono-ubiquitylation of histone H2A by non-canonical PRC1 and tri-methylation of histone H3K27 by PRC2. Despite prevalent models emphasizing tight functional coupling between PRC1 and PRC2, it remains unclear whether this paradigm indeed reflects the evolution and functioning of these complexes. Here, we conduct a comprehensive analysis of the presence or absence of cPRC1, nPRC1 and PRC2 across the entire eukaryotic tree of life, and find that both complexes were present in the Last Eukaryotic Common Ancestor (LECA). Strikingly, ~42% of organisms contain only PRC1 or PRC2, showing that their evolution since LECA is largely uncoupled. The identification of ncPRC1-defining subunits in unicellular relatives of animals and fungi suggests ncPRC1 originated before cPRC1, and we propose a scenario for the evolution of cPRC1 from ncPRC1. Together, our results suggest that crosstalk between these complexes is a secondary development in evolution.

          Abstract

          A systematic characterization of the Polycomb system throughout the eukaryotic tree of life reveals that evolution of PRC1 and PRC2 has been largely uncoupled.

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          Most cited references105

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              Basic local alignment search tool.

              A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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                Author and article information

                Contributors
                b.snel@uu.nl
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                10 November 2023
                10 November 2023
                2023
                : 6
                : 1144
                Affiliations
                [1 ]Theoretical Biology and Bioinformatics, Department of Biology, Science Faculty, Utrecht University, ( https://ror.org/04pp8hn57) Utrecht, Netherlands
                [2 ]GRID grid.418101.d, ISNI 0000 0001 2153 6865, Hubrecht institute, , Royal Netherlands Academy of Arts and Sciences, ; Utrecht, Netherlands
                [3 ]Department of Biochemistry, Erasmus University Medical Center, ( https://ror.org/018906e22) Rotterdam, Netherlands
                [4 ]GRID grid.418101.d, ISNI 0000 0001 2153 6865, Present Address: Hubrecht institute, , Royal Netherlands Academy of Arts and Sciences, ; Utrecht, Netherlands
                Author information
                http://orcid.org/0000-0003-4479-2460
                http://orcid.org/0000-0002-5218-2083
                http://orcid.org/0000-0002-5804-8547
                Article
                5501
                10.1038/s42003-023-05501-x
                10638273
                37949928
                8bffd085-e268-42a0-90f5-c26897b64432
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 July 2023
                : 24 October 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research);
                Award ID: NWO-Vici 016.160.638
                Award Recipient :
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                © Springer Nature Limited 2023

                comparative genomics,epigenetics,post-translational modifications

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