Analisis Dinamika Molekul Kompleks Protein Membran SARS_CoV-2 dengan Antibodi IgG Secara In Silico

Sukmamulia, Muhammad Aqsa Lentera (2026) Analisis Dinamika Molekul Kompleks Protein Membran SARS_CoV-2 dengan Antibodi IgG Secara In Silico. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 5004221110-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (2MB)

Abstract

Pandemi COVID-19 yang disebabkan oleh virus SARS-CoV-2 telah memberikan dampak signifikan terhadap kesehatan global. Protein membran (M protein) SARS-CoV-2 menunjukkan potensi besar sebagai target untuk pengembangan vaksin dan terapi antibodi karena aktivitasnya yang superior dibandingkan protein viral lainnya. Penelitian ini bertujuan untuk menganalisis karakteristik interaksi antara protein membran SARS-CoV-2 dengan antibodi IgG serta mengevaluasi kestabilan kompleks melalui pendekatan in silico. Penelitian ini menggunakan laptop Lenovo IdeaPad slim 3 sebagai alat uji. Protein antibodi IgG (kode PDB: 7CM4) dipreparasi menggunakan PDB-Tools untuk menghilangkan air, ion, dan ligan pengotor. Sekuens protein membran SARS-CoV-2 (YP_009724393.1) diambil dari NCBI dan dimodelkan secara tiga dimensi menggunakan I-TASSER, menghasilkan C-score 0,34 dan TM-score 0,76±0,10. Molecular docking dilakukan dengan ClusPro 2.0 dengan mengaktifkan mode antibodi untuk menutup wilayah CDR, menghasilkan nilai ∆G sebesar -17,1 kcal/mol dan Kd sebesar 3×10⁻¹³ M, menunjukkan afinitas ikatan yang sangat kuat. Analisis binding interface mengidentifikasi ikatan hidrogen dan interaksi hidrofobik antara residu antibodi dan antigen. Simulasi molecular dynamics menggunakan GROMACS dengan force field AMBER99SB-ILDN dan model air TIP3P selama 1 nanosekon. Hasil analisis molecular dynamic menunjukkan bahwa sistem masih berada dalam fase ekuilibrasi aktif, dengan RMSD meningkat dari 0,1 nm hingga 0,6 nm, RoG meningkat dari 3,8 nm menjadi 4 nm, dan H-Bond berkisar 460-520. Meskipun durasi simulasi lebih pendek dari standar konvensional, hasil penelitian ini memberikan informasi penting mengenai stabilitas dan dinamika kompleks protein membran-antibodi IgG, membuka peluang pengembangan strategi vaksinasi berbasis multi-antigen terhadap SARS-CoV-2.
====================================================================================================================================
The COVID-19 pandemic caused by SARS-CoV-2 has had a significant impact on global health. The SARS-CoV-2 membrane protein (M protein) shows great potential as a target for vaccine and antibody therapy development due to its superior activity compared to other viral proteins. This research aims to analyze the interaction characteristics between SARS-CoV-2 membrane protein and IgG antibody and evaluate complex stability through an in silico approach. This research using Lenovo IdeaPad slim 3 as instrument. IgG antibody protein (PDB code: 7CM4) was prepared using PDB-Tools to remove water, ions, and unwanted ligands. The SARS-CoV-2 membrane protein sequence (YP_009724393.1) was retrieved from NCBI and modeled in three dimensions using I-TASSER, resulting in a C-score of 0.34 and TM-score of 0.76±0.10. Molecular docking was performed using ClusPro 2.0 with antibody mode activated to mask CDR regions, yielding a ∆G value of -17.1 kcal/mol and Kd of 3×10⁻¹³ M, indicating exceptionally strong binding affinity. Binding interface analysis identified hydrogen bonds and hydrophobic interactions between antibody and antigen residues. Molecular dynamics simulation was conducted using GROMACS with AMBER99SB-ILDN force field and TIP3P water model for 1 nanosecond. Molecular dynamic analysis demonstrates that the system remains in an active equilibration phase, with RMSD increasing from 0.1 nm to 0.6 nm, RoG increasing from 3.8 nm to 4 nm, and H-Bond ranging from 460-520. Although simulation duration was shorter than conventional standards, these findings provide important insights into the stability and dynamics of the membrane protein-IgG antibody complex, opening opportunities for developing multi-antigen-based vaccination strategies against SARS-CoV-2.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Antigen, Molecular Docking, Molecular Dynamic, SARS-CoV-2, Vaksin, Antige, Molecular Docking, Molecular Dynamic, SARS-CoV-2, Vaccine
Subjects:	Q Science > QR Microbiology > QR180 Immunology Q Science > QR Microbiology > QR355 Virology
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Chemistry > 47201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Aqsa Lentera Sukmamulia
Date Deposited:	05 Aug 2026 02:37
Last Modified:	05 Aug 2026 02:37
URI:	http://repository.its.ac.id/id/eprint/143810

Actions (login required)

View Item