Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

HIV-1 Reverse Transcriptase/Integrase Dual Inhibitors: A Review of Recent Advances and Structure-activity Relationship Studies

The significant threat to humanity is HIV infection, and it is uncertain whether a definitive treatment or a safe HIV vaccine is. HIV-1 is continually evolving and resistant to commonly used HIV-resistant medications, presenting significant obstacles to HIV infection management. The drug resistance adds to the need for new anti-HIV drugs; it chooses ingenious approaches to fight the emerging virus. Highly Active Antiretroviral Therapy (HAART), a multi-target approach for specific therapies, has proved effective in AIDS treatment. Therefore, it is a dynamic system with high prescription tension, increased risk of medication reactions, and adverse effects, leading to poor compliance with patients. In the HIV-1 lifecycle, two critical enzymes with high structural and functional analogies are reverse transcriptase (RT) and integrase (IN), which can be interpreted as druggable targets for modern dual-purpose inhibitors. Designed multifunctional ligand (DML) is a new technique that recruited many targets to be achieved by one chemical individual. A single chemical entity that acts for multiple purposes can be much more successful than a complex multidrug program. The production of these multifunctional ligands as antiretroviral drugs is valued with the advantage that the viral-replication process may end in two or more phases. This analysis will discuss the RT-IN dual-inhibitory scaffolds' developments documented so far.

Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling

Drug-resistant cases of human immunodeficiency virus (HIV) nucleoside reverse transcriptase inhibitors (NRTI) are constantly accumulating due to the frequent mutations of the reverse transcriptase (RT). Predicting the potential drug resistance of HIV-1 NRTIs could provide instructions for the proper clinical use of available drugs. In this study, a novel proteochemometric (PCM) model was constructed to predict the drug resistance between six NRTIs against different variants of RT. Forty-seven dominant mutation sites were screened using the whole protein of HIV-1 RT. Thereafter, the physicochemical properties of the dominant mutation sites can be derived to generate the protein descriptors of RT. Furthermore, by combining the molecular descriptors of NRTIs, PCM modeling can be constructed to predict the inhibition ability between RT variants and NRTIs. The results indicated that our PCM model could achieve a mean AUC value of 0.946 and a mean accuracy of 0.873 on the external validation set. Finally, based on PCM modeling, the importance of features was calculated to reveal the dominant amino acid distribution and mutation patterns on RT, to reflect the characteristics of drug-resistant sequences.

Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach

The human immunodeficiency virus type 1 (HIV-1), one of the leading causes of infectious death globally, generates severe damages to people's immune systems and makes them susceptible to serious diseases. To date, there are no drugs that completely remove HIV from the body. This paper focuses on screening 224,205 natural compounds of ZINC15 NPs subset to identify those with bioactivity similar to non-nucleoside reverse transcriptase inhibitors (NNRTIs) as promising candidates to treat HIV-1. To reach the goal, an in silico approach involving 3D-similarity search, ADMETox, HIV protein-inhibitor prediction, docking, and MM-GBSA free-binding energies was trained. The FDA-approved HIV drugs, efavirenz, etravirine, rilpivirine, and doravirine, were used as queries. The prioritized compounds were subjected to ADMETox, docking, and MM-GBSA studies against HIV-1 reverse transcriptase (RT). Lys101, Tyr181, Tyr188, Trp229, and Tyr318 residues and free-binding energies have proved that ligands can stably bind to HIV-1 RT. Three natural products (ZINC37538901, ZINC38321654, and ZINC67912677) containing oxan and oxolan rings with hydroxyl substituents and one (ZINC2103242) having 3,6,7,8-tetrahydro-2H-pyrido[1,2-a]pyrazine-1,4-dione core exhibited comparable profiles to etravirine and doravirine, with ZINC2103242 being the most promising anti-HIV candidate in terms of drug metabolism and safety profile. These findings may open new avenues to guide the rational design of novel HIV-1 NNRTIs.

3-Hydroxypyrimidine-2, 4-dione Derivatives as HIV Reverse Transcriptase-Associated RNase H Inhibitors: QSAR Analysis and Molecular Docking Studies

AIDS, as a lethal disease, is caused by infection with the HIV virus that affects millions of people. Three essential enzymes should be encoded for replication of HIV virus: protease, integrase and reverse transcriptase (RT). RT has two different activities including DNA polymerase and ribonuclease H (RNase H). However, all of the marketed RT inhibitors target only the DNA polymerase activity. Therefore, ribonuclease H activity may serve as a new target for drug discovery. In the present study, a series of 3-Hydroxypyrimidine-2, 4-dione derivatives as potent RT-associated RNase H inhibitors were applied to QSAR analysis. Two methods including multiple linear regressions (MLR) and partial least squared based on genetic algorithm (GA-PLS) were utilized to find the relationship between the structural feathers and inhibitory activities of these compounds. The best multiple linear regression equation was generated by GA-PLS method. A combination of 2D autocorrelations, topological, atom-centered, and geometrical descriptors were selected by GA-PLS as they had more effects on the inhibitory activity. Then, the molecular docking studies were carried out. The results showed that the important amino acids inside the active site of the enzyme responsible for essential interactions were Gln475, Asp549, Tyr501, Ser515, Trp534, Asp493, Tyr472, and Gln480 which took part in hydrogen bond formation. Furthermore, docking energy was plotted against pIC₅₀ predicted by GA-PLS method. The result showed that there is a good correlation with R²=0.71. Consequently, these findings suggest that the better method, GA-PLS, could be applied to design new compounds and predict their inhibitory activity.

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview

BACKGROUND

Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times.

METHODS

The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost.

RESULTS

The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one.

CONCLUSION

Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.

New strategy for identifying potential natural HIV-1 non-nucleoside reverse transcriptase inhibitors against drug-resistance: an in silico study

Non-nucleosides reverse transcriptase inhibitors (NNRTIs), specifically targeting the HIV-1 reverse transcriptase (RT), play a unique role in anti-AIDS agents due to their high antiviral potency, structural diversity, and low toxicity in antiretroviral combination therapies used to treat HIV. However, due to the emergence of new drug-resistant strains, the development of novel NNRTIs with adequate potency, improved resistance profiles and less toxicity is highly required. In this work, a novel virtual screening strategy combined with structure-based drug design was proposed to discover the potential inhibitors against drug-resistant HIV strains. Seven structure-variant RTs, ranging from the wild type to a hypothetical multi-mutant were regarded as target proteins to perform structure-based virtual screening. Totally 23 small molecules with good binding affinity were identified from the Traditional Chinese Medicine database (TCM) as potential NNRTIs candidates. Among these hits, (+)-Hinokinin has confirmed anti-HIV activity, and some hits are structurally identical with anti-HIV compounds. Almost all these hits are consistent with external experimental results. Molecular simulations analysis revealed that top 2 hits (Pallidisetin A and Pallidisetin B) bind stably and in high affinity to HIV-RT, which are ready to be experimental confirmed. These results suggested that the strategy we proposed is feasible, trustworthy and effective. Our finding might be helpful in the identification of novel NNRTIs against drug-resistant, and also provide a new clue for the discovery of HIV drugs in natural products.Communicated by Ramaswamy H. Sarma.

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds

HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds are in great need. By targeting the major reverse transcription residues Y181, K103, and L100, we used the biological activities of compounds against these enzymes and the wild-type reverse transcriptase to create Naïve Bayes Networks. Through this machine learning approach, we could predict, with high accuracy, whether a compound would be susceptible to a loss of potency due to resistance. Also, we could perfectly predict retrospectively whether compounds would be susceptible to both a K103 mutant RT and a Y181 mutant RT. In the study presented here, our method outperformed a traditional molecular mechanics approach. This method should be of broad interest beyond drug discovery efforts, and serves to expand the utility of machine learning for the prediction of physical, chemical, or biological properties using the vast information available in the literature.

Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review

Currently, millions of people are living with human immunodeficiency virus type 1 (HIV-1), which causes acquired immunodeficiency syndrome. However, the spread of the HIV-1 resistance to antiviral agents is the major problem in the antiretroviral therapy and medical management of HIV-infected patients. HIV-1 reverse transcriptase (RT) is one of the key viral targets for HIV-1 inhibition. Therefore, the studies on the combatting the HIV resistance that occurs due to the structural changes in RT, are in great demand. This work aims to provide an overview of the state-of-the-art molecular docking approaches applied to the studies of the HIV-1 resistance, associated with RT structure changes. We have reviewed recent studies using molecular docking with mutant forms of RT. The work discusses the modifications of molecular docking, which have been developed to find the novel molecules active against resistance mutants of RT and/or recombinant strains of HIV-1. The perspectives of the existing algorithms of molecular docking to the studies on molecular mechanisms of resistance and selection of the correct binding poses for the reverse transcriptase inhibitors are discussed.

How to win the HIV-1 drug resistance hurdle race: running faster or jumping higher?

Infections by the human immunodeficiency virus type 1 (HIV-1), the causative agent of the acquired immunodeficiency syndrome (AIDS), are still totaling an appalling 36.7 millions worldwide, with 1.1 million AIDS deaths/year and a similar number of yearly new infections. All this, in spite of the discovery of HIV-1 as the AIDS etiological agent more than 30 years ago and the introduction of an effective combinatorial antiretroviral therapy (cART), able to control disease progression, more than 20 years ago. Although very effective, current cART is plagued by the emergence of drug-resistant viral variants and most of the efforts in the development of novel direct-acting antiviral agents (DAAs) against HIV-1 have been devoted toward the fighting of resistance. In this review, rather than providing a detailed listing of all the drugs and the corresponding resistance mutations, we aim, through relevant examples, at presenting to the general reader the conceptual shift in the approaches that are being taken to overcome the viral resistance hurdle. From the classic 'running faster' strategy, based on the development of novel DAAs active against the mutant viruses selected by the previous drugs and/or presenting to the virus a high genetic barrier toward the development of resilience, to a 'jumping higher' approach, which looks at the cell, rather than the virus, as a source of valuable drug targets, in order to make the cellular environment non-permissive toward the replication of both wild-type and mutated viruses.