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• W4297788232 abstract "Abstract. The coronavirus outbreak that is sweeping the world is a serious challenge for the international health system. Overcoming the lack of information regarding experimental data, tools and even understanding of the body's immune response to SARS-CoV2, it is necessary to take effective steps to anticipate the spread and control the COVID-19 pandemic. Therefore, a COVID-19 epitope T-cell protein-peptida docking simulation against the TLR-2 (Toll-like Receptor-2) immune receptor target with the PDB code 2Z80 computationally was carried out in order to add dry laboratory data information, anticipate contact and crowds so as to minimize the spread of the SARS-CoV2 outbreak. There are several bioactive peptidas that are potential candidates for SARS-CoV2 vaccines such as bioactive peptidas FLAFVVFLL, FVLAAVYRI, FVVFLLVTL, VLLFLAFVV and YVYSRVKNL. This study aims to identify and evaluate the molecular interactions that occur between bioactive peptida molecules and TLR-2 enzyme macromolecules with PDB code 2Z80 using in-silico protein-peptida-based molecular docking method. Bioactive peptida sequence modeling using PEP-FOLD software. Furthermore, the best conformation was selected to be used in the study of interactions with TLR-2 macromolecules using the HPEPDock software. Further exploration was carried out on the results of molecular interactions formed using the BIOVIA Discovery Studio 2019 software. Based on the results from molecular anchoring, the bioactive peptida FLAFVVFLL had the best affinity for TLR-2 macromolecules (PDB code 2Z80) with a bond free energy value of -155,194 kJ/mol. So it can be concluded that the bioactive peptida is predicted to be used as a candidate for TLR-2 enzyme inhibitor.&#x0D; Abstrak. Wabah virus corona yang sedang melanda dunia ini merupakan suatu tantangan yang serius bagi sistem kesehatan internasional. Mengatasi kurangnya informasi mengenai data eksperimental, alat bahkan pemahaman mengenai respons imun tubuh terhadap SARS-CoV2 diperlukan langkah yang efektif agar mengantisipasi penyebaran sekaligus mengendalikan pandemic COVID-19. Maka dari itu dilakukan simulasi docking protein-peptida sel-T epitope COVID-19 terhadap target reseptor imun TLR-2 (Toll-like Receptor-2) dengan kode PDB 2Z80 secara komputasi agar menambahkan informasi data laboratorium kering, mengantisipasi kontak dan kerumunan sehingga meminimalisir penyebaran wabah SARS-CoV2. Terdapat beberapa peptida bioaktif yang berpotensi sebagai kandidat vaksin SARS-CoV2 seperti peptida bioaktif FLAFVVFLL, FVLAAVYRI, FVVFLLVTL, VLLFLAFVV dan YVYSRVKNL. Penelitian ini bertujuan mengidentifikasi dan mengevaluasi interaksi molekuler yang terjadi antara molekul peptida bioaktif dengan makromolekul enzim TLR-2 dengan kode PDB 2Z80 menggunakan metode penambatan molekuler berbasis protein-peptida secara in-silico. Pemodelan sekuensi peptida bioaktif dengan menggunakan perangkat lunak PEP-FOLD. Selanjutnya dipilih konformasi terbaik untuk digunakan pada studi interaksi terhadap makromolekul TLR-2 dengan menggunakan perangkat lunak HPEPDock. Dilakukan eksplorasi lebih lanjut terhadap hasil interaksi molekuler yang terbentuk dengan menggunakan perangkat lunak BIOVIA Discovery Studio 2019. Berdasarkan hadil dari penambatan molekuler, ppeptida bioaktif FLAFVVFLL memiliki afinitas yang paling baik terhadap makromolekul TLR-2 (kode PDB 2Z80) dengan nilai energi bebas ikatan -155,194 kJ/mol. Sehingga dapat disimpulkan, peptida bioaktif tersebut diprediksi dapat digunakan sebagai kandidat inhibitor enzim TLR-2." @default.
• W4297788232 created "2022-10-01" @default.
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• W4297788232 date "2022-07-29" @default.
• W4297788232 modified "2023-10-05" @default.
• W4297788232 title "Identifikasi Epitop Sel-T COVID-19 terhadap Reseptor Imun TLR-2 (Toll-like Receptor-2) untuk Pengembangan Vaksin Berbasis Peptida" @default.
• W4297788232 doi "https://doi.org/10.29313/bcsp.v2i2.3393" @default.
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