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    <responseDate>2026-10-10T16:51:47Z</responseDate>
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    <identifier>10.57760/sciencedb.nbsdc.00131</identifier>
    <datestamp>2026-03-02T14:02:04Z</datestamp>
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<oai_dc:dc 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" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2026-03-02</dc:date>
  <dc:title>Deep Learning-Guided Redesign of a Miniaturized Myoglobin for Enhanced Catalysis</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.nbsdc.00131</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Data for the paper: Redesigning Myoglobin via Functional Site Scaffolding for Enhanced Catalytic FunctionsThis dataset supports the research paper &amp;quot;Redesigning Myoglobin via Functional Site Scaffolding for Enhanced Catalytic Functions&amp;quot; and contains data and code for molecular dynamics simulation for bitMb and swMb, deep-learning based protein design for bitMb. Data was generated from RFDiffusion, ProteinMPNN, OmegaFold for protein design and NAMD3 for molecular dynamics simulation, ultimately forming 2 Gb of MD trajectory records, with 50ns to 200ns time lapse, and 100000 designed sequences along with 48 predicted structures in pdb format. The dataset is stored in .dcd, .fasta and .pdb format and can be used for further analysis of protein structure.</dc:description>
  <dc:subject>Myoglobin; protein design; deep learning</dc:subject>
  <dc:creator>Yang Yu</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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