creators_name: Havier , Maheswara Putra
creators_id: 1917011088
type: other
datestamp: 2023-10-17 07:24:08
lastmod: 2023-10-17 07:24:08
metadata_visibility: show
title: PREDIKSI KANDIDAT SENYAWA HASIL ISOLASI DARI KULIT
BATANG Sesbania grandiflora (L.) SEBAGAI ANTI-INFLAMMASI
COVID-19 MENGGUNAKAN ANALISIS NETWORK PHARMACOLOGY,
MOLECULAR DOCKING DAN MOLECULAR DYNAMICS SIMULATIONS
ispublished: pub
subjects: 500
subjects: 540
full_text_status: restricted
abstract: Turi putih (S. grandiflora) merupakan salah satu tumbuhan asli Indonesia yang
biasa dimanfaatkan sebagai anti-inflammasi. Tujuan penelitian ini adalah untuk
mendapatkan senyawa yang potensial sebagai anti-inflammasi COVID-19
menggunakan analisis Network Pharmacology, Molecular Docking, dan Molecular
Dynamics Simulations. Senyawa yang berhasil diisolasi dari kulit batang turi putih
dan target proteinnya diperoleh melalui proses data mining, dan dibuat jaringan
senyawa-target protein. Selanjutnya, jaringan target protein untuk penyakit
inflammasi COVID-19 juga dibuat agar dapat digabungkan dengan jaringan
senyawa-target protein. Selanjutnya, jaringan Protein-Protein Interaction (PPI)
dibangun, berdasarkan target protein yang mengalami overlapping. Target protein
dengan nilai degree tertinggi secara berurutan adalah SRC, MAPK1, HSP90AA1,
PIK3R1, AKT1, RELA, JAK2, PTPN11, NFKB1, ERBB2 dengan nilai degree
masing-masing yakni 23, 19, 17, 17, 16, 14, 14, 13, 13, dan 11. Setelah itu,
dilakukan filtering berdasarkan topologi degree, betwenness centrality, dan
closeness centrality, untuk mendapat target protein paling signifikan dengan hasil
target protein SRC, MAPK1, PIK3R1, HSP90AA1, dan AKT1. Berdasarkan GO &
KEGG Enrichment Analysis, target protein signifikan kebanyakan berasosiasi
dengan anotasi Biological Processes response to hormone dan KEGG Pathway
yakni MAPK signalling pathway & PIK3-Akt signalling pathway dalam pathway
in cancer. Berdasarkan validasi Molecular Docking, senyawa sesbagrandiflorain A
memiliki energi ikatan terbaik sebesar -9 kcal/mol dengan membentuk 3 ikatan
hidrogen pada LYS 295 (4,65 Å), THR 338 (5,41 Å), PHE 405 (3,10 Å) dengan
target protein SRC serta stabil selama Molecular Dynamics Simulations
berdasarkan nilai rata-rata RMSD & RMSF masing-masing sebesar 1,1 Å dan 0,65
Å.
Kata kunci: S. grandiflora, anti-inflammasi, COVID-19, Network Pharmacology,
Molecular Docking, Molecular Dynamics Simulations

White turi (S. grandiflora) is a native Indonesian plant which is commonly used as
an anti-inflammatory. The aim of this study was to obtain potential compounds as
anti-inflammatory for COVID-19 using Network Pharmacology, Molecular
Docking, and Molecular Dynamics Simulations analysis. Compounds that were
successfully isolated from the stem bark of white turi and their protein targets were
obtained through a data mining process, and a compound-protein target network
was made. Furthermore, the protein target network for the inflammatory disease
COVID-19 was also made so that it could be combined with the protein target
compound network. A Protein-protein interaction (PPI) network is constructed
based on the overlapping protein targets. The protein targets with the highest degree
values sequentially are SRC, MAPK1, HSP90AA1, PIK3R1, AKT1, RELA, JAK2,
PTPN11, NFKB1, ERBB2 with respective degree values of 23, 19, 17, 17, 16, 14,
14, 13, 13, and 11. After that, filtering was carried out based on topology degree,
betweenness centrality, and closeness centrality, to get the most significant protein
targets with the results of protein targets SRC, MAPK1, PIK3R1, HSP90AA1, and
AKT1. Based on GO & KEGG Enrichment Analysis, significant protein targets are
mostly associated with the annotation of Biological Processes response to hormone
and KEGG Pathway namely MAPK signalling pathway & PIK3-Akt signalling
pathway in pathway in cancer. Based on Molecular Docking validation,
sesbagrandiflorin A has the best bond energy of -9 kcal/mol by forming 3 hydrogen
bonds at LYS 295 (4.65 Å), THR 338 (5.41 Å), PHE 405 (3.10 Å) with SRC protein
targets and stable during Molecular Dynamics Simulations based on RMSD &
RMSF mean values of 1.1 Å and 0.65 Å, respectively.
Keywords: S. grandiflora, anti-inflammatory, COVID-19, Network Pharmacology,
Molecular Docking, Molecular Dynamics Simulations
date: 2023-07-28
date_type: published
publisher: FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM
place_of_pub: UNIVERSITAS LAMPUNG
id_number: 1917011088
citation:   Havier , Maheswara Putra  (2023) PREDIKSI KANDIDAT SENYAWA HASIL ISOLASI DARI KULIT BATANG Sesbania grandiflora (L.) SEBAGAI ANTI-INFLAMMASI COVID-19 MENGGUNAKAN ANALISIS NETWORK PHARMACOLOGY, MOLECULAR DOCKING DAN MOLECULAR DYNAMICS SIMULATIONS.  FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM, UNIVERSITAS LAMPUNG.     
document_url: http://digilib.unila.ac.id/76525/1/ABSTRAK.pdf
document_url: http://digilib.unila.ac.id/76525/2/SKRIPSI%20FULL.pdf
document_url: http://digilib.unila.ac.id/76525/3/SKRIPSI%20TANPA%20BAB%20PEMBAHASAN.pdf