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      Enzymic recognition of amino acids drove the evolution of primordial genetic codes

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      , , ,
      Nucleic Acids Research
      Oxford University Press

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          Abstract

          How genetic information gained its exquisite control over chemical processes needed to build living cells remains an enigma. Today, the aminoacyl-tRNA synthetases (AARS) execute the genetic codes in all living systems. But how did the AARS that emerged over three billion years ago as low-specificity, protozymic forms then spawn the full range of highly-specific enzymes that distinguish between 22 diverse amino acids? A phylogenetic reconstruction of extant AARS genes, enhanced by analysing modular acquisitions, reveals six AARS with distinct bacterial, archaeal, eukaryotic, or organellar clades, resulting in a total of 36 families of AARS catalytic domains. Small structural modules that differentiate one AARS family from another played pivotal roles in discriminating between amino acid side chains, thereby expanding the genetic code and refining its precision. The resulting model shows a tendency for less elaborate enzymes, with simpler catalytic domains, to activate amino acids that were not synthesised until later in the evolution of the code. The most probable evolutionary route for an emergent amino acid type to establish a place in the code was by recruiting older, less specific AARS, rather than adapting contemporary lineages. This process, retrofunctionalisation, differs from previously described mechanisms through which amino acids would enter the code.

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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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            Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7

            Abstract Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.
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              BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis

              Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.
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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
                25 January 2024
                04 December 2023
                04 December 2023
                : 52
                : 2
                : 558-571
                Affiliations
                Department of Physics, The University of Auckland , New Zealand
                Centre for Computational Evolution, The University of Auckland , New Zealand
                Centre for Computational Evolution, The University of Auckland , New Zealand
                School of Computer Science, The University of Auckland , New Zealand
                Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill , USA
                Department of Physics, The University of Auckland , New Zealand
                Centre for Computational Evolution, The University of Auckland , New Zealand
                Author notes
                To whom correspondence should be addressed. Tel: +64 9 373 7513; Email: jordan.douglas@ 123456auckland.ac.nz
                Author information
                https://orcid.org/0000-0003-0371-9961
                https://orcid.org/0000-0001-6765-3813
                https://orcid.org/0000-0002-2653-4452
                https://orcid.org/0000-0002-2670-7624
                Article
                gkad1160
                10.1093/nar/gkad1160
                10810186
                38048305
                96daf996-310b-4e08-98f6-3af153cc5ba5
                © The Author(s) 2023. 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 License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 November 2023
                : 28 October 2023
                : 10 August 2023
                Page count
                Pages: 14
                Funding
                Funded by: Alfred P. Sloan Foundation, DOI 10.13039/100000879;
                Award ID: G-2021-16944
                Categories
                AcademicSubjects/SCI00010
                Computational Biology

                Genetics
                Genetics

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