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      The role of oligomerization in the optimization of cyclohexadienyl dehydratase conformational dynamics and catalytic activity

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          Abstract

          The emergence of oligomers is common during the evolution and diversification of protein families, yet the selective advantage of oligomerization is often cryptic or unclear. Oligomerization can involve the formation of isologous head‐to‐head interfaces (e.g., in symmetrical dimers) or heterologous head‐to‐tail interfaces (e.g., in cyclic complexes), the latter of which is less well studied and understood. In this work, we retrace the emergence of the trimeric form of cyclohexadienyl dehydratase from Pseudomonas aeruginosa (PaCDT) by introducing residues that form the PaCDT trimer‐interfaces into AncCDT‐5 (a monomeric reconstructed ancestor of PaCDT). We find that single interface mutations can switch the oligomeric state of the variants and that trimerization corresponds with a reduction in the K M value of the enzyme from a promiscuous level to the physiologically relevant range. In addition, we find that removal of a C‐terminal extension present in PaCDT leads to a variant with reduced catalytic activity, indicating that the C‐terminal region has a role in tuning enzymatic activity. We show that these observations can be rationalized at the structural and dynamic levels, with trimerization and C‐terminal extension leading to reduced sampling of non‐catalytic conformational substates in molecular dynamics simulations. Overall, this work provides insight into how neutral sampling of distinct oligomeric states along an evolutionary trajectory can facilitate the evolution and optimization of enzyme function.

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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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            GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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              PAML 4: phylogenetic analysis by maximum likelihood.

              PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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                Author and article information

                Contributors
                joe.kaczmarski@anu.edu.au
                Journal
                Protein Sci
                Protein Sci
                10.1002/(ISSN)1469-896X
                PRO
                Protein Science : A Publication of the Protein Society
                John Wiley & Sons, Inc. (Hoboken, USA )
                0961-8368
                1469-896X
                December 2022
                December 2022
                : 31
                : 12 ( doiID: 10.1002/pro.v31.12 )
                : e4510
                Affiliations
                [ 1 ] ARC Centre of Excellence in Synthetic Biology Australian National University Canberra Australia
                [ 2 ] Research School of Biology Australian National University Acton Australian Capital Territory Australia
                [ 3 ] Protein Engineering and Evolution Unit Okinawa Institute of Science and Technology Okinawa Japan
                [ 4 ] ARC Centre of Excellence for Innovations in Peptide and Protein Science, Research School of Chemistry Australian National University Acton Australian Capital Territory Australia
                Author notes
                [*] [* ] Correspondence

                Joe A. Kaczmarski, Research School of Biology, Australian National University. R.N Robertson Building, Building 46, Biology Place, Acton, Australian Capital Territory, Australia.

                Email: joe.kaczmarski@ 123456anu.edu.au

                Author information
                https://orcid.org/0000-0002-4471-449X
                https://orcid.org/0000-0002-7469-7424
                https://orcid.org/0000-0001-6150-3822
                https://orcid.org/0000-0001-8549-0122
                Article
                PRO4510
                10.1002/pro.4510
                9703590
                36382881
                a0dc0a8b-2563-45b7-a234-921a341db3b5
                © 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 07 November 2022
                : 13 September 2022
                : 11 November 2022
                Page count
                Figures: 4, Tables: 1, Pages: 15, Words: 9355
                Funding
                Funded by: Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science
                Award ID: CE200100012
                Funded by: Australian Research Council Centre of Excellence in Synthetic Biology
                Award ID: CE200100029
                Funded by: KAKENHI Grant‐in‐Aid for Scientific Research
                Award ID: 20F20705
                Categories
                Full‐length Paper
                Full‐length Papers
                Custom metadata
                2.0
                December 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.1 mode:remove_FC converted:28.11.2022

                Biochemistry
                cyclohexadienyl dehydratase,enzyme evolution,molecular dynamics simulations,oligomerization,protein evolution,size‐exclusion chromatography,trimerization

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