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      Evaluation of residue variability in a conformation‐specific context and during evolutionary sequence reconstruction narrows drug resistance selection in Abl1 tyrosine kinase

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          Abstract

          Diseases with readily available therapies may eventually prevail against the specific treatment by the acquisition of resistance. The constitutively active Abl1 tyrosine kinase known to cause chronic myeloid leukemia is an example, where patients may experience relapse after small inhibitor drug treatment. Mutations in the Abl1 tyrosine kinase domain (Abl1‐KD) are a critical source of resistance and their emergence depends on the conformational states that have been observed experimentally: the inactive state, the active state, and the intermediate inactive state that resembles Src kinase. Understanding how resistant positions and amino acid identities are determined by selection pressure during drug treatment is necessary to improve future drug development or treatment decisions. We carry out in silico site‐saturation mutagenesis over the Abl1‐KD structure in a conformational context to evaluate the in situ and conformational stability energy upon mutation. Out of the 11 studied resistant positions, we determined that 7 of the resistant mutations favored the active conformation of Abl1‐KD with respect to the inactive state. When, instead, the sequence optimization was modeled simultaneously at resistant positions, we recovered five known resistant mutations in the active conformation. These results suggested that the Abl1 resistance mechanism targeted substitutions that favored the active conformation. Further sequence variability, explored by ancestral reconstruction in Abl1‐KD, showed that neutral genetic drift, with respect to amino acid variability, was specifically diminished in the resistant positions. Since resistant mutations are susceptible to chance with a certain probability of fixation, combining methodologies outlined here may narrow and limit the available sequence space for resistance to emerge, resulting in more robust therapeutic treatments over time.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

            PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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              UniProt: the universal protein knowledgebase in 2021

              (2020)
              Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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                Author and article information

                Contributors
                sinisa.bjelic@lnu.se
                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
                16 June 2022
                July 2022
                16 June 2022
                : 31
                : 7 ( doiID: 10.1002/pro.v31.7 )
                : e4354
                Affiliations
                [ 1 ] Department of Chemistry and Biomedical Sciences Linnaeus University Kalmar Sweden
                [ 2 ] Departamento de Bioquímica, Instituto de Química Universidade de São Paulo São Paulo Brazil
                Author notes
                [*] [* ] Correspondence

                Sinisa Bjelic, Department of Chemistry and Biomedical Sciences, Linnaeus University, Kalmar, Sweden.

                Email: sinisa.bjelic@ 123456lnu.se

                Author information
                https://orcid.org/0000-0002-9300-614X
                Article
                PRO4354
                10.1002/pro.4354
                9202545
                82c76375-ec09-4fa7-98cc-7b5fb7372284
                © 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-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 31 March 2022
                : 12 January 2022
                : 10 May 2022
                Page count
                Figures: 5, Tables: 1, Pages: 14, Words: 9392
                Funding
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo , doi 10.13039/501100001807;
                Award ID: 2018/18537‐4
                Award ID: 2019/25955‐0
                Funded by: Vetenskapsrådet , doi 10.13039/501100004359;
                Award ID: SNIC 2021/5‐401
                Funded by: Conselho Nacional de Desenvolvimento Científico , doi 10.13039/501100003593;
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , doi 10.13039/501100002322;
                Funded by: Swedish Research Council , doi 10.13039/501100004359;
                Categories
                Full‐length Paper
                Full‐length Papers
                Custom metadata
                2.0
                July 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:16.06.2022

                Biochemistry
                abl1,ancestral reconstruction,conformational energy,kinase,resistant positions
                Biochemistry
                abl1, ancestral reconstruction, conformational energy, kinase, resistant positions

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