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      A genomic database furnishes minimal functional glycyl-tRNA synthetases homologous to other, designed class II urzymes

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          Abstract

          The hypothesis that conserved core catalytic sites could represent ancestral aminoacyl-tRNA synthetases (AARS) drove the design of functional TrpRS, LeuRS, and HisRS ‘urzymes’. We describe here new urzymes detected in the genomic record of the arctic fox, Vulpes lagopus. They are homologous to the α-subunit of bacterial heterotetrameric Class II glycyl-tRNA synthetase (GlyRS-B) enzymes. AlphaFold2 predicted that the N-terminal 81 amino acids would adopt a 3D structure nearly identical to our designed HisRS urzyme (HisCA1). We expressed and purified that N-terminal segment and the spliced open reading frame GlyCA1–2. Both exhibit robust single-turnover burst sizes and ATP consumption rates higher than those previously published for HisCA urzymes and comparable to those for LeuAC and TrpAC. GlyCA is more than twice as active in glycine activation by adenosine triphosphate as the full-length GlyRS-B α 2 dimer. Michaelis–Menten rate constants for all three substrates reveal significant coupling between Exon2 and both substrates. GlyCA activation favors Class II amino acids that complement those favored by HisCA and LeuAC. Structural features help explain these results. These minimalist GlyRS catalysts are thus homologous to previously described urzymes. Their properties reinforce the notion that urzymes may have the requisite catalytic activities to implement a reduced, ancestral genetic coding alphabet.

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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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            MolProbity: More and better reference data for improved all-atom structure validation.

            This paper describes the current update on macromolecular model validation services that are provided at the MolProbity website, emphasizing changes and additions since the previous review in 2010. There have been many infrastructure improvements, including rewrite of previous Java utilities to now use existing or newly written Python utilities in the open-source CCTBX portion of the Phenix software system. This improves long-term maintainability and enhances the thorough integration of MolProbity-style validation within Phenix. There is now a complete MolProbity mirror site at http://molprobity.manchester.ac.uk. GitHub serves our open-source code, reference datasets, and the resulting multi-dimensional distributions that define most validation criteria. Coordinate output after Asn/Gln/His "flip" correction is now more idealized, since the post-refinement step has apparently often been skipped in the past. Two distinct sets of heavy-atom-to-hydrogen distances and accompanying van der Waals radii have been researched and improved in accuracy, one for the electron-cloud-center positions suitable for X-ray crystallography and one for nuclear positions. New validations include messages at input about problem-causing format irregularities, updates of Ramachandran and rotamer criteria from the million quality-filtered residues in a new reference dataset, the CaBLAM Cα-CO virtual-angle analysis of backbone and secondary structure for cryoEM or low-resolution X-ray, and flagging of the very rare cis-nonProline and twisted peptides which have recently been greatly overused. Due to wide application of MolProbity validation and corrections by the research community, in Phenix, and at the worldwide Protein Data Bank, newly deposited structures have continued to improve greatly as measured by MolProbity's unique all-atom clashscore.
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              Evolution and tinkering.

              F Jacob (1977)
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                27 November 2024
                04 November 2024
                04 November 2024
                : 52
                : 21
                : 13305-13324
                Affiliations
                Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, NC 27599-7260, USA
                Department of Physics, The University of Auckland , Auckland 1042, New Zealand
                Centre for Computational Evolution, University of Auckland , 1010, New Zealand
                Department of Physics, The University of Auckland , Auckland 1042, New Zealand
                Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, NC 27599-7260, USA
                Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, NC 27599-7260, USA
                Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, NC 27599-7260, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 919 259 2558; Fax: +1 919 843 3328; Email: carter@ 123456med.unc.edu
                Author information
                https://orcid.org/0000-0002-9314-5641
                https://orcid.org/0000-0003-0371-9961
                https://orcid.org/0000-0002-2670-7624
                https://orcid.org/0000-0002-2653-4452
                Article
                gkae992
                10.1093/nar/gkae992
                11602164
                39494520
                fe5bae72-0bea-4eec-a24d-36cb71cce14d
                © The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@ 123456oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals.permissions@ 123456oup.com

                History
                : 18 October 2024
                : 18 December 2023
                Page count
                Pages: 20
                Funding
                Funded by: Alfred P. Sloan Foundation Matter-to-Life Program;
                Award ID: G- 2021-16944
                Categories
                AcademicSubjects/SCI00010
                Nucleic Acid Enzymes

                Genetics
                Genetics

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