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    <responseDate>2026-10-14T01:05:05Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/t8fnb5vmtr.1</identifier>
                <datestamp>2024-02-22T12:14:59Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Singh, Mahender</dc:creator>
    <dc:title>Dataset on double mutation in PGIP of Glycine max improves defense to PG of Sclerotinia sclerotiorum</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>The cell wall of the Glycine max altered by the polygalacturonases (PGs) secreted by
the fungus Sclerotinia sclerotiorum, causes disease and quality losses. In soybeans, a
resistance protein called polygalacturonases-inhibiting proteins (PGIPs) binds to the
PG to block fungal infection. The active site residues of PGIP3, VAL170 and GLN242
are mutated naturally by various amino acids in different types of PGIPs. Therefore, the
mutation of VAL170 to GLY is ineffective but the GLN242 amino acid mutation by LYS
significantly alters the structure and is crucial for interacting with the PG protein.
Docking and Molecular Dynamics simulation provide a comprehensive evaluation of
the interactions between gmPGIP and ssPG. By elucidating the structural basis of the
interaction between gmPGIP and ssPG, this investigation lays a foundation for the
development of targeted strategies in-order to enhance soybean resistance against
Sclerotinia sclerotiorum. By leveraging this knowledge, researchers can potentially
engineer soybean varieties with improved resistance to the fungus, thereby reducing
disease incidence and improving crop yields.</dc:description>
    <dc:subject>Molecular Dynamics</dc:subject>
    <dc:contributor>Chaurasia, Anurag</dc:contributor>
    <dc:contributor>Rashmi, Mayank</dc:contributor>
    <dc:contributor>Murmu, Sneha</dc:contributor>
    <dc:contributor>Nagrale, Dipak T</dc:contributor>
    <dc:contributor>Behera, Santosh Kumar</dc:contributor>
    <dc:contributor>Shankar, Raja</dc:contributor>
    <dc:contributor>Ranjan, Rajiv</dc:contributor>
    <dc:contributor>Jha, Girish Kumar</dc:contributor>
    <dc:contributor>Kumar, Sunil</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/t8fnb5vmtr.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/t8fnb5vmtr.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/t8fnb5vmtr</dc:relation>
    <dc:date>2024-02-22T12:14:59Z</dc:date>
</oai_dc:dc></metadata>
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