Journal article Open Access

Identification and In-silico Prediction of Hermetiaillucens Larval Protein that Inhibits the Progression of Cervical Cancer

Nisha Rajasekhar; Ramesh N; Prashantha


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  <identifier identifierType="URL">https://zenodo.org/record/5592303</identifier>
  <creators>
    <creator>
      <creatorName>Nisha Rajasekhar</creatorName>
      <affiliation>Research Scholar, Department of Biotechnology,  REVA University, Bangalore-560064</affiliation>
    </creator>
    <creator>
      <creatorName>Ramesh N</creatorName>
      <affiliation>Director, Dean-Training, Placement and planning, School  of Applied Sciences, REVA University, Bangalore-560064</affiliation>
    </creator>
    <creator>
      <creatorName>Prashantha</creatorName>
      <affiliation>Assistant Professor, Department of Biotechnology,  REVA University, Bangalore-560064.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Identification and In-silico Prediction of  Hermetiaillucens Larval Protein that Inhibits the  Progression of Cervical Cancer</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Pharmacokinetics Cell proliferation, LC-MS, quantification of protein, Bioinformatics assessment, identification and characterization, Bioactive prediction, Peptide Ranking , Target Protein selection, Molecular docking.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">C5715029320/2020©BEIESP</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-02-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5592303</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.C5715.029320</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The aim of the current research is to assess the anticancer activity of the proteins identified from the crude protein extract derived from Hermetiaillucens larvae by computational methods. Bioinformatics methods were used to identify the protein sequence and virtual screening for the prediction of protein structure, their physico-chemical characteristics and functional aspects, that aid in exploring the anti-cancer interaction that is inclined to inhibit the oncoprotein activity. Due to the pathological process transpired by HPV, an array of genetic alterations, including the overexpression of oncoproteins or the inactivation of tumor suppressor proteins like TP53, BCL2, MDM2, ARF and BAX occur. The altered protein signaling pathways convulse to cervical cancer. In this research, we identified proteins, from the crude extract derived from Hermetiaillucens by LC-MS method. Further Computational screening of the selected proteins were employed to assess the functional units which has anticancer activity. We predicted four proteins Metallothionine, Defensin like precursor 1 protein, Heat shock protein 90 and NADH dehydrogenase and eluted the protein sequence from NCBI GenBank database. These sequences were used to predict physicochemical properties and protein structure prediction. Pharmacophore analysis of the peptide sequences as potential targets for cancer treatment was evaluated. Molecular docking of peptide sequences with target protein structure was carried out. To screen the best active potential molecule for cancer treatment the Molecular dynamics of docked protein-peptide structures were administered. The molecular docking of the peptides with onco-proteins has been predicted and virtually screened based on RMSD values. The resultant protein sequence was evaluated further by amino acid sequencing, extensive scrutiny of the proteins, advanced computational imaging, molecular docking examination and assessed the results for clinical interpretation. To summate, bioinformatics assessment enabled us to identify four key proteins derived from Hermetiaillucens larvae that have strong inhibitory function against proteins that cause cervical cancer.&lt;/p&gt;</description>
  </descriptions>
</resource>
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