Prediksi Efektivitas Efavirenz terhadap Berbagai Human-Infecting Retrovirus secara In-Silico Menggunakan Tools Bioinformatika dan Kimia Medisinal

Authors

  • Billgerd Tjengal Sains dan Teknologi Farmasi, Sekolah Farmasi, Institut Teknologi Bandung, Bandung
  • Ade Fany Safitri Mikrobiologi, Sekolah Ilmu dan Teknologi Hayati, Institut Teknologi Bandung, Bandung
  • Julio Jonathan Gilbert Alexis Mikrobiologi, Sekolah Ilmu dan Teknologi Hayati, Institut Teknologi Bandung, Bandung
  • Nadira Rahmatunisa Mikrobiologi, Sekolah Ilmu dan Teknologi Hayati, Institut Teknologi Bandung, Bandung
  • Sigit Nur Pratama 3 Biologi, Sekolah Ilmu dan Teknologi Hayati, Institut Teknologi Bandung, Bandung

DOI:

https://doi.org/10.48177/bimfi.v6i1.4

Keywords:

HTLV, Retrovirus, Efavirenz, Bioinformatics, HIV

Abstract

Introduction: One strategy for HIV-1 (retroviridae) infection therapy is using NNRTI (non-nucleoside reverse transcriptase inhibitor) to suppress viral replication by binding to the reverse transcriptase (RT-ase) enzyme. One drug in this class is Efavirenz (EFV). Based on the good effectivity of EFV in HIV-1, it is suspected that EFV also has activity against HIV-2 and HTLV. This study aims to determine the activity of EFV in the human-infecting retroviruses such as HIV-2, HTLV-1, and HTLV-2.

Methods: This study uses Bioinformatics and Medicinal Chemistry tools such as, phylogenetic tree construction, pairwise sequence alignment, looking for RT-ase motifs and domains, predicting secondary structures, modelling 3D RT-ase structures, docking, and predicting hydrophobicity with Kyte-Doolittle Hydropathy Plot.

Result: There is a common ancestor of the four viruses according to the constructed phylogenetic tree. There are also RT_like Superfamily domains at HIV-2 and HTLV-1. The similarity and identity value, and results of HTLV-1 and HTLV-2 RT-ase structural modeling do not give good results. Binding energy value of EFV to HIV-2 RT-ase is higher than HIV-1 RT-ase.

Conclusion: EFV is not effective against HIV-2, HTLV-1 and HTLV-2

Additional Files

Published

2019-06-30

How to Cite

Tjengal, B., Safitri, A. F., Alexis, J. J. G., Rahmatunisa, N., & Pratama, S. N. (2019). Prediksi Efektivitas Efavirenz terhadap Berbagai Human-Infecting Retrovirus secara In-Silico Menggunakan Tools Bioinformatika dan Kimia Medisinal. Berkala Ilmiah Mahasiswa Farmasi Indonesia, 6(1). https://doi.org/10.48177/bimfi.v6i1.4