Atom-based 3D-QSAR and DFT analysis of 5‐substituted 2‐acylaminothiazole derivatives as HIV-1 latency-reversing agents

Abstract

HIV-1 latency consists of viral DNA; integrated inside the host genome; it remains transcriptional silent. Combined Antiretroviral Therapy (cART) and the host immune system fail to recognize the latency cells or reservoirs, representing a major barrier to eradicating the HIV-1 infection. The Shock and Kill emerged as a promising strategy to target these cells using Latency reversal agents (LRAs); partially succeeded in producing viral mRNA but failed to reduce the size of reservoirs. In this Context, 2‐acylaminothiazole class derivatives appeared as promising HIV-1 latency-reversing agents. In this study, we have developed an atom-based 3 D-QSAR model by utilizing the 49 active compounds of the 5‐substituted 2‐acylaminothiazoles derivatives. These compounds are further randomly divided into training (37) and test (12) datasets, yielding statistically significant R² (0.90) and Q² (0.85) results, respectively. The internal and external validation of the model shows highly robust and reliable results. Next, the model was visualized to check the favourable and unfavourable groups in terms of hydrogen bond donor, electron-withdrawing and hydrophobic group on the most active compound 96 and least active compound 30. The investigated model reveals the structural insights required for obtaining more leads that are potent. Finally, DFT calculations on the most and least active compounds were performed to support the atom-based 3 D-QSAR model. Overall, this study will aid in understanding the minimum structural requirement and functional group required for screening the novel potent leads as HIV-1 latency reversal agents.

Communicated by Ramaswamy H. Sarma

Keywords:

• 2‐Acylaminothiazoles
• 3D-QSAR
• DFT
• HIV-1 latency
• latency reversal agents
• shock and kill

Disclosure statement

The author does not show any conflict of interest.

Additional information

Funding

The authors MAK and SKS thank Alagappa University for providing the necessary infrastructure and facilities. The Authors also thankfully acknowledge the Alagappa University Research Fund [1913/AURF Fellowship/2018, Dated 25.10.2018], MHRD-RUSA Phase 2.0 under [grant sanctioned wide letter no. F.24-51/2014-U, Policy (TN Multi-Gen), Department of Education, Government of India, Dated 09.10.2018], Tamil Nadu State Council for Higher Education (TANSCHE) under [grant sanctioned wide letter no. AU: S.O. (P&D): TANSCHE Projects: 117/2021, Dated 31.03.2021, File No. RGP/2019-20/ALU/HECP-0048 dated 27.04.2021], and Department of Biotechnology, Ministry of Science and Technology, New Delhi, under Grant/Award [No. BT/PR40154/BTIS/137/34/2021, dated 07.03.2022] for providing the infrastructure facilities and financial support.