Correlation between the predicted and the observed biological activity of the symmetric and nonsymmetric cyclic urea derivatives used as HIV-1 protease inhibitors. A 3D-QSAR-CoMFA method for new antiviral drug design

Background: Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections which avoids resistance and has less toxicities. Objectives: In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study Methods: Total 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules were predicted using generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity. Results: In CoMFA, leave one out cross validated coefficient (q 2 ), conventional coefficient (r 2 ) and predicted correlation coefficient (r 2 Predicted) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also had significant cross validated coefficient (q 2 ), conventional coefficient (r 2 ) and predicted correlation coefficient (r 2 Predicted) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge based structure activity relationship. Conclusion: The results from both these approaches, ligand based drug design and structure based drug design, are adequate and promising to discover protease inhibitors.

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An Integrated Soft Computing Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.1716622 ◽

2006 ◽

Cited By ~ 1

Author(s):

R. Andonie ◽

L. Fabry-Asztalos ◽

S. Abdul-Wahid ◽

C.J. Collar ◽

N. Salim

Keyword(s):

Biological Activity ◽

Protease Inhibitors ◽

Soft Computing ◽

Hiv 1 ◽

Computing Approach

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3D-QSAR and docking studies of HIV-1 protease inhibitors using R-group search and Surflex-dock

Medicinal Chemistry Research ◽

10.1007/s00044-016-1701-0 ◽

2016 ◽

Vol 25 (11) ◽

pp. 2619-2630 ◽

Cited By ~ 11

Author(s):

Jian-Bo Tong ◽

Min Bai ◽

Xiang Zhao

Keyword(s):

Protease Inhibitors ◽

3D Qsar ◽

Docking Studies ◽

Group Search ◽

Hiv 1

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Recent Advances in Computer-aided Antiviral Drug Design Targeting HIV-1 Integrase and Reverse Transcriptase Associated Ribonuclease H

Current Medicinal Chemistry ◽

10.2174/0929867328666210708090123 ◽

2021 ◽

Vol 28 ◽

Author(s):

Fengyuan Yang ◽

Jingyi Yang ◽

Zhao Zhang ◽

Gao Tu ◽

Xiaojun Yao ◽

...

Keyword(s):

Drug Resistance ◽

Drug Design ◽

Reverse Transcriptase ◽

Antiviral Drug ◽

Antiviral Drugs ◽

Rnase H ◽

Energy Calculation ◽

Recent Advances ◽

Computer Aided ◽

Hiv 1

: Acquired immunodeficiency syndrome (AIDS) has been a chronic, life-threatening disease for a long time. However, a broad range of antiretroviral drug regimens are applicable for the successful suppression of virus replication in human immunodeficiency virus type 1 (HIV-1) infected people. The mutation-induced drug resistance problems during the treatment of AIDS forced people to continuously look for new antiviral agents. HIV-1 integrase (IN) and reverse transcriptase associated ribonuclease (RT-RNase H), two pivotal enzymes in HIV-1 replication progress, has gain popularity as drug-able targets for designing novel HIV-1 antiviral drugs. During the development of HIV-1 IN and/or RT-RNase H inhibitors, computer-aided drug design (CADD), including homology modeling, pharmacophore, docking, molecular dynamics (MD) simulation, and binding free energy calculation, represents a significant tool to accelerate the discovery of new drug candidates and reduce costs in antiviral drug development. In this review, we summarized the recent advances in the design of single-and dual-target inhibitors against HIV-1 IN or/and RT-RNase H as well as the prediction of mutation-induced drug resistance based on computational methods. We highlighted the results of the reported literature and proposed some perspectives on the design of novel and more effective antiviral drugs in the future.

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QSAR and Docking Studies of DATA Analogues as HIV-1 Reverse Transcriptase Inhibitors

Letters in Drug Design & Discovery ◽

10.2174/1570180815666180413152636 ◽

2018 ◽

Vol 16 (2) ◽

pp. 153-159 ◽

Cited By ~ 1

Author(s):

Jianbo Tong ◽

Shan Lei ◽

Pei Zhan ◽

Shangshang Qin ◽

Yang Wang

Keyword(s):

Biological Activity ◽

Molecular Docking ◽

Reverse Transcriptase ◽

3D Qsar ◽

Docking Studies ◽

Reverse Transcriptase Inhibitors ◽

Electronic Density ◽

Topomer Comfa ◽

Rt Inhibitors ◽

Hiv 1

Background: Acquired Immunodeficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV) has seriously threatened human health, so development of new, selective and safe non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) remains a high priority for medical research. Diaryltriazine (DATA) have been identified as a new class of potent nonnucleoside HIV-1 Reverse Transcriptase (RT) inhibitors. The study deals with Topomer CoMFA (Comparative Molecular Field Analysis) and molecular docking to explore the important features of DATA analogues for exerting potent HIV-1 RT inhibitors activity. Methods: In this work, 40 DATA analogues were studied using a combination of molecular modeling techniques including Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR), molecular docking, and Topomer CoMFA were used to build 3D-QSAR models. Results: The results show that the Topomer CoMFA analysis has the cross-validation q2 = 0.800, SDCV = 0.45, the non-cross-validated r2 = 0.958, SD = 0.21, and the correlation coefficient of external validation Q2 ext = 0.965 showed that the model is reasonable and credible, and has a good predictive ability. Then binding mode pattern of the compounds to the binding site of enzyme was confirmed and the mechanism of drug and acceptor was studied by docking studies, the results showed that the drug and GLU138, LYS101, THR139 sites have an obvious function, these researches have provided an useful information for designing more effective HIV-1IN inhibitors. Conclusion: A series of 40 DATAs analogues was subjected to a 3D-QSAR study. Using Topomer CoMFA 3D-QSAR method built model, and the model has shown a good predictive and statistical validation. Substituent with low electronic density in the R5 and R3 positions and substituent with high electronic density in the R2 and C2 positions will increase the biological activity, small substituent on R4 positions and naphthyloxy as the spacer group C6 substituent hydrophobic will increase biological activity. This effect is supported by Topomer CoMFA contour map and docking results of HIV-1RT inhibition active site, the results of the 3D-QSAR and docking analyses have provided a guide for the synthesis of new putative inhibitors for HIV-1RT to improved inhibitory activity.

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