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      Chromatin remodeling of histone H3 variants by DDM1 underlies epigenetic inheritance of DNA methylation

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          Summary

          Nucleosomes block access to DNA methyltransferase, unless they are remodeled by DECREASE in DNA METHYLATION 1 (DDM1 Lsh/HELLS), a Snf2-like master regulator of epigenetic inheritance. We show that DDM1 promotes replacement of histone variant H3.3 by H3.1. In ddm1 mutants, DNA methylation is partly restored by loss of the H3.3 chaperone HIRA, while the H3.1 chaperone CAF-1 becomes essential. The single-particle cryo-EM structure at 3.2 Å of DDM1 with a variant nucleosome reveals engagement with histone H3.3 near residues required for assembly, and with the unmodified H4 tail. An N-terminal autoinhibitory domain inhibits, while a disulfide bond in the helicase domain supports activity. DDM1 co-localizes with H3.1 and H3.3 during the cell cycle, and with the DNA methyltransferase MET1 Dnmt1, but is blocked by H4K16 acetylation. The male germline H3.3 variant MGH3/HTR10 is resistant to remodeling by DDM1 and acts as a placeholder nucleosome in sperm cells for epigenetic inheritance.

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          How DDM1 drives DNA methylation and epigenetic inheritance is unknown. Through high resolution structural analyses, it is shown how DDM1 is recruited by MET1 and provides MET1 access to naked replicated DNA, ensuring epigenetic inheritance of DNA methylation.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                8 August 2023
                14 September 2023
                28 August 2023
                28 November 2023
                : 186
                : 19
                : 4100-4116.e15
                Affiliations
                [1 ]Howard Hughes Medical Institute, Cold Spring Harbor Laboratory; 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
                [2 ]W. M. Keck Structural Biology Laboratory, Howard Hughes Medical Institute; Cold Spring Harbor, NY 11724, USA
                [3 ]Graduate Program in Genetics, Stony Brook University; Stony Brook, NY 11794, USA
                [4 ]Cold Spring Harbor Laboratory School of Biological Sciences; 1 Bungtown Rd, Cold Spring Harbor, NY 11724, USA
                [5 ]Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh; Edinburgh EH9 3BF, United Kingdom
                [6 ]Present address: Department of Molecular, Cellular and Developmental Biology, Faculty of Arts and Sciences, Yale University; 260 Whitney Ave, New Haven, CT, 06511, USA
                [7 ]Institut de Recherche pour le Développement; 911 Avenue Agropolis, 34394 Montpellier, France
                [8 ]Present address: Epigenetics Programme, Babraham Institute; Cambridge CB22 3AT, United Kingdom
                Author notes
                [*]

                These authors contributed equally to this work.

                Author Contributions

                SCL, LJ and RAM designed the study; SCL, JJI, DWA, JL, HK, UR, CL, UR, SB, and VM performed the experiments; JC, SCL, JJI, DWA, BB, DG, YJ, LJ, and RAM analyzed the data and/or its significance; SCL and RAM wrote the manuscript with contributions from JC, JJI and PV. YJ, PV, LJ, and RAM acquired funding.

                [** ]Corresponding authors. martiens@ 123456cshl.edu (lead contact), leemor@ 123456cshl.edu
                Article
                NIHMS1922946
                10.1016/j.cell.2023.08.001
                10529913
                37643610
                e10ccb0e-5e9d-4376-96c8-d79f4bda7641

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                Cell biology
                Cell biology

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