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      Changes in total charge on spike protein of SARS-CoV-2 in emerging lineages

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      Bioinformatics Advances
      Oxford University Press

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          Abstract

          Motivation

          Charged amino acid residues on the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been shown to influence its binding to different cell surface receptors, its non-specific electrostatic interactions with the environment, and its structural stability and conformation. It is therefore important to obtain a good understanding of amino acid mutations that affect the total charge on the spike protein which have arisen across different SARS-CoV-2 lineages during the course of the virus’ evolution.

          Results

          We analyse the change in the number of ionizable amino acids and the corresponding total charge on the spike proteins of almost 2200 SARS-CoV-2 lineages that have emerged over the span of the pandemic. Our results show that the previously observed trend toward an increase in the positive charge on the spike protein of SARS-CoV-2 variants of concern has essentially stopped with the emergence of the early omicron variants. Furthermore, recently emerged lineages show a greater diversity in terms of their composition of ionizable amino acids. We also demonstrate that the patterns of change in the number of ionizable amino acids on the spike protein are characteristic of related lineages within the broader clade division of the SARS-CoV-2 phylogenetic tree. Due to the ubiquity of electrostatic interactions in the biological environment, our findings are relevant for a broad range of studies dealing with the structural stability of SARS-CoV-2 and its interactions with the environment.

          Availability and implementation

          The data underlying the article are available in the Supplementary material.

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          Most cited references48

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          SARS-CoV-2 variants, spike mutations and immune escape

          Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of ‘variants of concern’, that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets. The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been characterized by the emergence of mutations and so-called variants of concern that impact virus characteristics, including transmissibility and antigenicity. In this Review, members of the COVID-19 Genomics UK (COG-UK) Consortium and colleagues summarize mutations of the SARS-CoV-2 spike protein, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.
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            Nextstrain: real-time tracking of pathogen evolution

            Abstract Summary Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.
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              Biopython: freely available Python tools for computational molecular biology and bioinformatics

              Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_lists peter.cock@scri.ac.uk.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Bioinform Adv
                Bioinform Adv
                bioadv
                Bioinformatics Advances
                Oxford University Press
                2635-0041
                2024
                08 April 2024
                08 April 2024
                : 4
                : 1
                : vbae053
                Affiliations
                Department of Theoretical Physics, Jožef Stefan Institute , Ljubljana 1000, Slovenia
                School of Physical Sciences, University of Chinese Academy of Sciences , Beijing 100049, China
                Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences , Beijing 100049, China
                Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou 325001, China
                Author notes
                Corresponding author. School of Physical Sciences, University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China. E-mail: podgornikrudolf@ 123456ucas.ac.cn
                Author information
                https://orcid.org/0000-0001-6304-6637
                Article
                vbae053
                10.1093/bioadv/vbae053
                11031363
                38645718
                a96aaf78-6269-4370-a624-79b0dfc8cb42
                © The Author(s) 2024. Published by Oxford University Press.

                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
                : 08 November 2023
                : 13 March 2024
                : 01 April 2024
                : 04 April 2024
                : 19 April 2024
                Page count
                Pages: 9
                Funding
                Funded by: Slovenian Research Agency, DOI 10.13039/501100004329;
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 12034019
                Categories
                Original Article
                Sequence Analysis
                AcademicSubjects/SCI01060

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