In-silico Ligand and Structure Based Design of HIV-1 Protease Inhibitors: Current Trends and Future Directions

 

S. Ramasamy1, Sushmita Gupta2, Ritu Rani Chaudhary3, Amit Kumar Verma1*

1Department of Pharmacy, MJP Rohilkhand University, Bareilly 243006, Uttar Pradesh, India.

2Department of Applied Chemistry, Faculty of Engineering and Technology,

M. J. P. Rohilkhand University, Bareilly - 243006, Uttar Pradesh, India.

3Department of Chemistry, B.S.A. Degree College, Mathura, Uttar Pradesh, India.

*Corresponding Author E-mail: professoramitmjpuniversity@gmail.com

 

ABSTRACT:

A study of the function of the protease inhibitor as well as of highly active antiretroviral therapy is involved in the present work. The paper focused on the cause of drug resistance and related phenomena, the HIV protease structure and its catalytic mechanism, the production of HIV inhibitors based on CADD, as well as ligand-based drug design using QSAR and computational binding energy technique, other computational modelling, optimization based on CORAL software. In order to shed light on the potential growth of the new drug for the treatment of HIV, the debate on the prediction of the desired biological activity and the structural relationship studies and study related to the structure-based drug design and the most potent drugs was reviewed.

 

KEYWORDS: In-silico Ligand, HIV-1 Protease Inhibitors, Highly Active Anti-Retroviral Therapy (HAART), Support Vector Machine (SVM).

 

 


INTRODUCTION:

A few decades ago, AIDS was almost unknown, but it has influenced the lives of millions worldwide since its inception. Several attempts have been made to control HIV, and researchers around the world have since discovered a variety of successful drugs that slow the growth of the virus, but the full cure of HIV infection is still a far-reaching target that needs to be tackled immediately.

 

Nevertheless, the elucidation of the HIV-1 protease structure has been regarded as an important advance in the history of HIV science. In 1989, the first HIV protease structure was published. Since then, several structures have been identified and over a hundred structures are currently available in the protein database, which includes several enzyme genetic strains, enzyme complexes with several similar inhibitors and medications, and hundreds of enzyme mutations.

 

 

Hundreds other are housed in the pharmaceutical company's proprietary database and used to research and refine potential candidates for drugs. Overall, there is one HIV-1 protease now, of the best known medical background enzymes studied. HIV protease was first proposed by Kramer et al as a possible target for AIDS. HIV protease blockage contributes to the development of non-infectious virions that are immature. Over the last decade, compounds with the potential to this protease inhibitor has been intensively studied and several studies have been published on potent HIV-1 protease           inhibitors 1,2,3,4. In fact, a strong description of structural drug design is the design of protease inhibitors. In order to approximate the composition of viral peptides that the protease normally recognizes and cleaves, the chemical structures of the inhibitors have been selected.

 

Drug Resistance The high level of virus production and the high rate of error in reverse transcriptase activity are the key reasons for the growth of HIV drug resistance. According to Darwinism, if a mutant virus has some benefit over other viruses, such as reduced sensitivity to antiviral drugs, it will be preferred and developed to a large extent5.

In patients undergoing HAART as the first line of antiretroviral therapy, the development of viral resistance is only possible if HIV continues to replicate in the presence of drug therapy, i.e. if the concentration of the drug is inadequate to block viral replication but important for variants with reduced drug susceptibility to exert positive pressure. Viruses immune to all the components of the regimen will eventually emerge under these conditions. New drugs are becoming available that seem to be active against strains immune to many older drugs. These medicines are either new members of existing structural groups, with increased potency and enhanced pharmacokinetic properties, or new structural classes that are not yet susceptible to cross-resistance6. New medications, used in triple class formulations, are saved as salvation treatments for later use, to be introduced in cases of drug resistance or when therapy is first begun at a very advanced stage of infection.

 

Saquinavir was the first protease inhibitor to have been licenced and has been in clinical use since 1995. There are currently nine protease inhibitors which are clinically approved. Although the inhibitors on the market are highly selective, after extended use, they cause side effects such as lipodystrophy, hyperlipidaemia, insulin resistance and the emergence of resistant mutants. Therefore, the market for new HIV protease inhibitors is likely to be persistent.

 

Structure of HIV protease:

The enzyme HIV protease transforms the precursor polyproteins into mature proteins. One of the essential processes of the period of HIV replication is this post translational processing. Mutation of catalytic Asp25 to asparagine in HIV protease kills the catalytic activity of the enzyme, enhancing its classification as aspartyl protease, a class that also includes renin and pepsin. Virulence and infectivity are lacking in the virion formed by these mutants7.

 

The HIV protease shares sequence homology with other retroviral proteases around the active site. It is a “homodimer, with each monomer containing 99 amino acids”. In the absence of ligands, it shows C2 symmetry. In FIGURE 1, a pictorial representation of HIV protease is given. At the dimer interface, the amino and carboxyl termini from both monomers form a four stranded anti-parallel beta sheet. This structure is maintained by ionic interactions between each monomer's N-termini (beta strand residues 1-4 a) and C-termini (beta strand residues 96-99 q) and other monomers8. The beta strand b (residue 9-15) continues through a loop, which ends in the active site triad, to another beta strand c. The Asp (25, 25 '), Thr (26, 26'), Gly (27, 27 ') at the bottom of the dimer interface, the catalytic triad is. One aspartic acid is added to the catalytic triad by each monomer. The coplanar arrangement of catalytic aspartates is due to the hydrogen bond network that resembles "fireman's grip" between the catalytic triad-bearing loops. In this network of hydrogen bonds, Thr 26 and Thr 24 from both monomers are involved.

 

A twisted loop is terminated by the beta chain d (residues 30-35) that opposes the strand c (residues 36-42). In the monomer, there is an almost two-fold intra-molecular symmetry, making topologically identical to the first half of molecules in the second half of the molecule. The a 'beta strand (residues 43-49) and a portion of the longer b' beta strand (residues 52-66) (residues 52-58) form a flap. The active site is protected by these glycine rich flap areas, from both the monomers. The beta chain c '(residue 69-78) is linked to another beta chain d' (residue 83-85), related to another beta chain d '(residue 83-85) via a loop (residue 79-82) via a loop (residue 83-85) (residues 79-82). The beta strand d 'is followed by a well-defined helix (residues 86-94), which is in turn followed by the C-terminal beta strand q. Aψ-shaped beta sheet in the molecular core, is formed by four of the beta strands (c, d and d’, c’ and d’). This work is a feature of the aspartic protease family9.

 

Figure 1: A pictorial representation of HIV protease. Catalytic aspartates are represented in ball and stick model.

 

Finally, there are three separate sub-sites on each side of the catalytic triad. From the catalytic site, the numbering of subsites begins and continues on either side. The flanking amino acids that lead to the amino end are referred to as “P1, P2, P3” and those proceeding P1, P2, P3 are referred to as the carboxyl terminus and providing space for molecules of water essential for the recognition of substrates and/or release of products 10, 11. Starting from catalytic aspartic acid residues, S1, S2, S3 and S1 ', S2', S3 ', respectively, Subsites of the enzyme that interact non-covalently with the peptide's corresponding side chains (substrate or inhibitor) are named.

 

 

The S1 and S1 '(similarly, S2 and S2' etc.) sub-sites are positioned at identical locations using the above standard nomenclature. For the exception of the Aspartate active site, the two S1 sub-sites are strongly hydrophobic. The residues that make contact with the substrate or inhibitor side chains of “P1/P1 include Arg8, Leu23, Asp 25, Gly27, Gly48, Gly49, Ile50, Thr80, Thr81 and Val82. Other than Asp29, Asp29', Asp30 and Asp30 ', the S2/S2' subsites are mainly hydrophobic (Ala28/28 ', Val23/23', Ile47/47 ', Gly49/49', Ile50/50 ', Leu76/76' and Ile84/84')”. The inside pockets, the subsites of S2 and S2, are smaller than the binding sites of S1/S1 'or S3/S3' and have been revealed to be other precise, limiting the form and size of P2/P2 'residues in substrates or inhibitors compared to other protease molecule binding pockets'12,13.

 

Figure 2: Standard nomenclature P1….Pn, P1’…..Pn’ is used to designate amino acid residues of peptide substrates. The corresponding binding sites on the protease are referred to as S1….Sn, S1’……Sn subsites.

 

Need for new HIV-1 Protease inhibitors In patients with HIV-1 infection, attempts to completely suppress viral replication are limited because of the growing possibility of resistance, despite the availability of an increasing number of potent antiretroviral agents. However, the clinical advantages of the protease inhibit limited by two key factors. First, “because of poor aqueous solubility, low metabolic stability, high protein binding and poor membrane permeability, many of the HIV-1 protease inhibitors on the market, especially in the first generation, suffer from poor pharmacokinetic properties”. Second, a large number of mutated viral stains are produced by the pace at which the virus reproduces and the high number of mistakes in the process of viral replication.

 

In the event of drug failure, the above factors are significant determinants and thus new drug development techniques are seeking to circumvent the drug resistance issue of the virus by Focusing on either new targets or new compounds that are able to inhibit HIV strains that are immune to the protease inhibitors currently used. Figure 3 shows mutations (located in the protease) that are associated with tolerance (or decreased susceptibility) to PIs. Resistance to the marketed inhibitors of HIV-1 protease is thus a significant cause of concern for successful HIV treatment. Of vital importance is the improvement of new HIV-1 protease inhibitors that resolve these issues. It usually costs a pharmaceutical or biotechnology company about $900 million to deliver one new drug to the market which takes an average of 10 to 12 years. The use of computer-assisted drug design (CADD) approaches to this issue has the potential to dramatically reduce the time and effort needed to develop new drugs or improve existing ones in terms of their effectiveness.


 

Figure 3: Protease gene mutations related to decreased resistance to protease inhibitors

 


Development of inhibitors of HIV-1 protease through application of CADD Computational tools have become an absolute necessity for the creation of science-based information products in the evolving era of technology. CADD has a wide range of applications, including structure analysis, comparison of structures, lead compound design, active conformation and pharmacophore recognition, combinational library design, protein and binding structure, ligand binding, quantitative structure activity relationship (QSAR) studies, etc. and yielded some very good druggable compounds14. To develop therapeutically useful HIV-1 protease inhibitors, namely structure-based drug design and ligand drug design, two simple computer-based strategies have been used. All of these have significantly led to the development of potent protease inhibitors appropriate for clinical development. Drug design based on structure (SBDD) relies on the understanding of the protein's 3-D structure acquired through NMR spectroscopy/X-ray crystallography. As it requires direct visualisation of the whole protein, SBDD is also viewed as a direct approach. In this method, the ligand's interaction with the amino acid of the catalytic site can be seen and manipulated as needed. “Ligand-based drug design (LBDD)” is known as an unintended method, as opposed to SBDD, but it has emerged as one of the most common drug discovery techniques. LBDD is also used to systematically study the impact on a biological network of macromolecular targets of a wide number of drugs, such as molecules. The most imperative and commonly used instruments in ligand-based drug design are the 2-D structure activity relationship (2-D QSAR) and 3-D pharmacophore modelling, which can afford critical perceptions into the existence of drug target and molecule of a ligand interactions and Provide predictive models appropriate for the optimization of lead compounds. A universal pharmacophore, once established, can serve as a powerful instrument for rational drug design, de novo design and ADME studies. In addition, for chemo-genomics research, pharmacophore models may also be used.

 

Ligand based drug design QSAR is a rational approach for lead optimization among the various LBDD methods, especially when the target structure is not known (15). The fundamental principle of QSAR is that a relationship exists between a compound's physicochemical properties and its observed biological activity (16). For the design and optimization of novel ligands with selective and power desired, the relationship can be used. During the lead optimization level, the QSAR studies will help accelerate the production of drugs and can reduce the enormous time involved with conventional approaches. In order to ensure that any molecule developed and tested should be as meaningful as possible, QSAR analysis is becoming an economic necessity to minimise empiricism in drug design.

 

In contrast to easy QSAR methods based on regression analysis, where the relationship between input and output (e.g. linear or quadratic function) has to be inferred beforehand, NN does not need any previous model of how input and output are related and has the unique capacity to adjust to an extremely complicated non-linear interaction. As a result, the essential characteristics of “NN: non-linearity, adaptability, independence of any statistical and modelling assumptions, fault tolerance, university and real-time activity make them especially suitable for pharmacokinetic applications, particularly where extremely complex and unfamiliar responses are studied”17. Biological activity dependent on a local representation of the ligands is expected, neural network approaches have previously been introduced. For each of four substituent properties, the complexes of the sequence were represented by a mapping of vectors: volume, log p, dipole moment and a basic 'steric' parameter for its shape. Using neural networks, this definition of ligand has been checked on a collection of 42 HIV-1 protease inhibiting cyclic urea derivatives.

 

A correlation factor of 0.76 for the total collection of prediction and experiment and 0.888, the leave-one-out cross-validation using all the descriptors in the input was provided when three outliers were left out. Combinations for each substituent of two and three parameters were further checked to rank the importance of the four descriptors Usage of two five-inhibitor disjunctive test sets and vectors with in either preparation or in the test set, extreme descriptor values were used (sets A and B, respectively). The technique is a good interpolator (set A, accuracy 95-2 percent) A less potent extrapolator, however (set B, accuracy 85-2 percent). The 'steric' parameter combinations usually predict better than average, although quantity-containing ones are less effective. When log P, the dipole and the steric parameter on set A were used, 98.8-1.2 percent was the best predictor. The lowest rated descriptor set was obtained at the opposite end when substituting log P with the volume, giving 92.3-6.7 percent precision over set A.

 

Figure 4. Chemical formulae of the inhibitors. (B) Assignment of the steric parameter, corresponding to different molecular shapes.

 

For greater anti-HIV protease activities, validated QSAR modelling was performed on some N-aryl-oxazolidinone-5-carboxamides. Important models of atom-based descriptors, such as RTSA indices, Wang-Ford charges and various entire molecular descriptors, were developed by stepwise regression. By questioning these against an external dataset, the true predictability of QSAR models was justified. By this modelling, a representative, highly active compound was predicted.

 

Figure 5: General N-aryl-oxazolidinone-5-carboxamide structure with arbitrary numeration

 

Support Vector Machine (SVM), a modern and effective modelling method, has recently gained much interest in apps for pattern recognition and feature approximation. SVMs have been successfully used in bioinformatics to solve classification and association problems, HIV protease cleavage sites identification and protein groups prediction. SVMs have been used in chemistry as well, such as protein retention index prediction and further QSAR tests18,19,20,21,22,22,24,25.

 

A collection of HIV-inhibitors and their inhibitory constants were collected from the literature for this reason. For QSAR model growth, CORAL software based on Monte Carlo optimization was used. First, the dataset was divided into three random divisions and, secondly, each division was divided into planning, calibration, testing and validation sets. For model creation, a training set was used, while the left behind sets were used to assess the quality of the models produced. QSAR models have been established with the effect of cyclic rings on the repressive behavior without ruminate. The statistical performance of QSAR models generated from all splits was very good and met the requirements. The R2, Q2, s, R2 pred and r2m values clarified that the selected models in nature are good and sufficiently powerful to predict the molecules' inhibitory behaviour outside the training set. The existence of cyclic rings has also been suggested by statistical criteria to have a critical effect on inhibitory behaviour. In order to increase or decrease the repressive activity, it was found that the molecular fragments were important, explaining that models have mechanistic interpretation. Fullerene C60 is contemplated a strong inhibitor of HIV-1 protease activity because (a) the fullerene C60 diameter is near to the active site cavity of the HIV-1 protease diameter. (b) Fullerene C60, which is more stable and not easily degradable than other fullerenes of the genus. (c) A close interaction between fullerene C60 and the active HIV-1 protease site at van der Waals provides the hydrophobic surface. There are several fullerene C60 derivatives that have been tested for capabilities protease inhibition26,27,28,29,30,31,32.  

 

It has been stated that fullerene surface c60 is modified to form fullerene derivative compounds by the addition of oxygen atoms as well as groups of hydroxymethyl carbonyl (HMC). These compounds have O atoms + HMC groups of one, two, three, four or five at distinct stances on the phenyl ring. The impact of repeating these groups on the potential of the compounds proposed to reduce HIV protease have been investigated by measuring both the properties of the QSAR and docking simulation. The results show that, as the number of oxygen atoms + HMC groups in the compound increases, the solubility and hydrophilicity of the studied fullerene derivative increases. The compound could interact and bind to the HIV-1 protease active site with two oxygen atoms + HMC groups while docking calculations show that. This may be linked to active site HIV-1 protease residues which, with the exception of the two aspartic acids, are hydrophobic33,34, 35,36,37,38.

 

On the basis of the above-mentioned discussion, it can be said that Hydrophobic activity between the compound and the active site of the HIV-1 protease will decrease and increase the polarity and hydrophilicity of the compound. For quick prediction and virtual prescreening of PR inhibitory activity of HIV-1, a three-dimensional 3D-QSAR has been established39,40,41.

 

CONCLUSION:

Drug design is a very advanced field that involves a very systemic and stepwise scientific method to achieve the desired outcome. HIV medications are now available on the market, but a more advanced approach to HIV infection control is still needed. The wider aspects of anti-HIV drug design, starting from its historical context to its history, have been examined in the present article. The overwhelming knowledge of the functional significance of the protease inhibitor is properly discussed, including the introductory knowledge of its mechanism of action. Particular attention was given to identifying the causes associated with drug resistance. The key aim behind this is to pave the way for the production of new medicines with the best results. All the methods of estimation, modelling, QSAR and use of the various tools were discussed.

 

ACKNOWDEGEMENT:

Authors are thankful to Prof K. P. Singh Vice Chancellor, MJP Rohilkhand University, Bareilly for his motivation in writing this paper.

 

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

 

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Received on 04.01.2021            Modified on 03.05.2021

Accepted on 10.08.2021           © RJPT All right reserved

Research J. Pharm.and Tech 2022; 15(4):1477-1482.

DOI: 10.52711/0974-360X.2022.00245