Dergi makalesi Açık Erişim
Azar, Lena Mahmoudi; Oncel, Muhammed Miran; Karaman, Elif; Soysal, Levent Faruk; Fatima, Ayesha; Choi, Sy Bing; Eyupoglu, Alp Ertunga; Erman, Batu; Khan, Asif M.; Uysal, Serdar
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Oncel, Muhammed Miran</subfield> <subfield code="u">Bezmialem Vakif Univ, Beykoz Inst Life Sci & Biotechnol, TR-34820 Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Karaman, Elif</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Soysal, Levent Faruk</subfield> <subfield code="u">Bezmialem Vakif Univ, Beykoz Inst Life Sci & Biotechnol, TR-34820 Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Fatima, Ayesha</subfield> <subfield code="u">Bezmialem Vakif Univ, Beykoz Inst Life Sci & Biotechnol, TR-34820 Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Choi, Sy Bing</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Eyupoglu, Alp Ertunga</subfield> <subfield code="u">Bogazici Univ, Fac Arts & Sci, Dept Mol Biol & Genet, TR-34450 Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Erman, Batu</subfield> <subfield code="u">Bogazici Univ, Fac Arts & Sci, Dept Mol Biol & Genet, TR-34450 Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Khan, Asif M.</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Uysal, Serdar</subfield> <subfield code="u">Bezmialem Vakif Univ, Beykoz Inst Life Sci & Biotechnol, TR-34820 Istanbul, Turkiye</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">14</subfield> <subfield code="p">COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL</subfield> <subfield code="v">21</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1016/j.csbj.2023.07.022</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Human ACE2 orthologous peptide sequences show better binding affinity to SARS-CoV-2 RBD domain: Implications for drug design</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Azar, Lena Mahmoudi</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:273164</subfield> <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2023-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/273164/files/bib-1ed06566-6395-46a3-9a16-a9c423fc7301.txt</subfield> <subfield code="z">md5:b4c5221bd06a7d0aa89891f761dc1e82</subfield> <subfield code="s">319</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20240608042831.0</controlfield> <controlfield tag="001">273164</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of human ACE2 helical peptide were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 peptide docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy;-14 kcal/mol) predicted from the docking results. The MD simulation confirmed the stability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.</p></subfield> </datafield> </record>
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