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      The molecular basis of tRNA selectivity by human pseudouridine synthase 3

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          Summary

          Pseudouridine (Ψ), the isomer of uridine, is ubiquitously found in RNA, including tRNA, rRNA, and mRNA. Human pseudouridine synthase 3 (PUS3) catalyzes pseudouridylation of position 38/39 in tRNAs. However, the molecular mechanisms by which it recognizes its RNA targets and achieves site specificity remain elusive. Here, we determine single-particle cryo-EM structures of PUS3 in its apo form and bound to three tRNAs, showing how the symmetric PUS3 homodimer recognizes tRNAs and positions the target uridine next to its active site. Structure-guided and patient-derived mutations validate our structural findings in complementary biochemical assays. Furthermore, we deleted PUS1 and PUS3 in HEK293 cells and mapped transcriptome-wide Ψ sites by Pseudo-seq. Although PUS1-dependent sites were detectable in tRNA and mRNA, we found no evidence that human PUS3 modifies mRNAs. Our work provides the molecular basis for PUS3-mediated tRNA modification in humans and explains how its tRNA modification activity is linked to intellectual disabilities.

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          Highlights

          • Single-particle cryo-EM structures reveal how human PUS3 recognizes tRNAs

          • Two distinct interfaces in PUS3 are key for tRNA binding and positioning

          • PUS1- but not PUS3-dependent Ψ sites are found in RNAs other than tRNAs

          • Patient-derived mutations link PUS3’s tRNA modification activity to human disease

          Abstract

          Lin, Kleemann et al. provide a comprehensive structure-function analysis of human PUS3, which catalyzes the conversion of uridine to pseudouridine (Ψ). PUS3 forms a homodimer to selectively bind and specifically modify tRNAs. No PUS3-dependent Ψs were detected in mRNA, linking the associated human diseases to tRNAs.

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          Most cited references88

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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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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Mol Cell
                Mol Cell
                Molecular Cell
                Cell Press
                1097-2765
                1097-4164
                11 July 2024
                11 July 2024
                : 84
                : 13
                : 2472-2489.e8
                Affiliations
                [1 ]Małopolska Centre of Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
                [2 ]Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, 3012 Bern, Switzerland
                [3 ]Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland
                [4 ]Department of Cell Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, 30-387 Kraków, Poland
                [5 ]SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, 30-392 Kraków, Poland
                [6 ]Doctoral School of Exact and Natural Sciences, Jagiellonian University, 30-348 Kraków, Poland
                [7 ]Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany
                Author notes
                []Corresponding author ting-yu.lin@ 123456durham.ac.uk
                [∗∗ ]Corresponding author sebastian.leidel@ 123456unibe.ch
                [∗∗∗ ]Corresponding author sebastian.glatt@ 123456uj.edu.pl
                [8]

                These authors contributed equally

                [9]

                Present address: Department of Biosciences, Durham University, DH1 3LE Durham, UK

                [10]

                Lead contact

                Article
                S1097-2765(24)00520-3
                10.1016/j.molcel.2024.06.013
                11258540
                38996458
                a6bcf05c-9053-4c19-9120-fb4832c2f146
                © 2024 The Authors

                This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

                History
                : 25 April 2023
                : 14 March 2024
                : 13 June 2024
                Categories
                Article

                Molecular biology
                trna modification,pseudouridine synthase,pus1,pus3,anticodon stem loop,cryo-em structure,pseudo-seq,transcriptome

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