Extracting the significant descriptors by 2D QSAR and docking efficiency of NRTI drugs: A Molecular Modeling Approach
A Banerjee, U Murty
docking, hiv-1, molecular modeling, nrti, qsar
A Banerjee, U Murty. Extracting the significant descriptors by 2D QSAR and docking efficiency of NRTI drugs: A Molecular Modeling Approach. The Internet Journal of Genomics and Proteomics. 2006 Volume 2 Number 2.
Acquired immunodeficiency syndrome (AIDS) is the most devastating pandemic in recent history of the mankind. It takes a heavy toll of life and is a major deterrent to the economic development of many countries. Human Immunodeficiency Virus type 1 (HIV-1) reverse transcriptase is an important target for chemotherapeutic agents against this devastating disease.
Nucleoside Reverse Transcriptase Inhibitors (NRTI) are known to be the best drugs as it causes chain termination hindering further viral protein synthesis. Rational design of NRTIs has become one of the most important activities in the pharmaceutical industry. Keeping in mind the fast viral mutation and increasing demand of improving the drug combination for HAART therapy, there is an urge for new drug synthesis of the same series. To get an insight about the right structural features needed to develop the NRTIs, 2D-QSAR and docking studies were carried out. CODESSA was used for QSAR study while SYBYL (FlexX) and GOLD were used to carry out the docking studies. 30 molecules were considered in the study. Among them, they were divided into training (21) and test set (9). The best QSAR model is obtained with an r2 value of 0.9516 and q2 value of 0.8544 with seven descriptors. This study will give a deep insight to understand the major role played by the descriptors of NRTI drugs and their binding efficiency in ligand drug interaction.
Since its first discovery in 1981, AIDS has claimed more than 20 million lives. According to the latest UNAIDS report of 2006, an estimated 39.5 million people are living with HIV/AIDS (UNAIDS report, 2006). About 4.3 million people were newly infected and more than 2.9 million people died from this deadly disease in 2006 alone (UNAIDS 2006). This reflects the enormity of the AIDS epidemic in the world, especially affecting Sub-Saharan Africa and South-East Asia. Another millions of people will be infected with HIV if the current trend of infection continues and in the absence of effective preventive measures (UNAIDS, 2006). The treatment of the Acquired Immuno Deficiency Syndrome (AIDS) is the most challenging worldwide medical problem.
During the past decade, there has been steady progress in the chemotherapy of AIDS due to the availability of more effective antiretroviral drugs (De Clercq E, 2005). Most of the current strategies for treating AIDS depend on inhibiting HIV-1 reverse transcriptase enzyme. All clinically used antiretroviral drugs belong to either the protease inhibitor (PI) or reverse transcriptase inhibitor (NRTIs) classes (Rawal
Other class which has drawn attention is NRTI drugs. All NRTI'S (2', 3'-dideoxynucleoside analogues) undergoes 3 phosphorylation steps that convert the parent compound successively to its 5' monophosphate, di and triphosphate. The triphosphate compound acts as a competitive inhibitor /alternate substrate for the target enzyme. This dideoxynucleoside incorporates in DNA chain and causes chain termination during either first or second strand synthesis of DNA. One of the first nucleoside analogues shown to have potent anti HIV activity in-vitro is Zidovudine (AZT). The 3'-OH is replaced in most of the available drugs with fluorine, nitrogen etc. The most recent drug is tenofovir. This drug possess one aliphatic chain and phosphonate group. It is incorporated into the primer strand following translocation to the priming or ‘P' site. Afterwards an alternative conformation for the adenosine base of tenofovir flipped through 180° from the first position. Appreciable approach for combinatorial drug therapy which led the path towards the success of the HAART therapy has been demonstrated in the past (Masanori Baba
In this paper, we report the 2D-QSAR analysis along with docking studies on a series of NRTI drugs. In addition to the QSAR studies, docking calculations were done using FLEXX and GOLD.
Materials and Methods
Dataset of 100 molecules was collected from the public domain of UNAIDS and after preliminary screening of the EC50 and IC50 values and proper literature support, a series of 30 compounds was considered in this study. All the molecules studied had the same parent skeleton (Fig 1).
IC50 is the concentration of the compound leading to 50% inhibitory effect. The logarithm of the inverse of this parameter has been used as biological end points (log IC 50) in the QSAR studies.
The selected protein HIV-1 reverse transcriptase cross-linked to tenofovir terminated template-primer (complex P) with 3.1 resolution and R value of 0.256 (work) and 0.295(free) was collected from Protein Data Bank (http://www.rcsb.org) (PDBID 1T03)(Fig 2).
Preparation of input for Quantitative Structure and Activity Relationship (QSAR) studies
The selected 30 molecules were build by using AMPAC™ 8.0 software. Chemical structures were optimized with the semi empirical AM1 method taking 0.01 as RMS gradient (Table 1) and saved as the out files. The output files were loaded into software CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) to calculate all possible descriptors.
Quantitative Structure and Activity Relationship (QSAR) studies
The theoretical basis of QSAR analysis is the presumed existence of a linear free energy relationship between physico-chemical descriptors of a molecule and its affinity for a receptor. CODESSA (Comprehensive Descriptors For Structural And Statistical Analysis) is a comprehensive program for developing quantitative structure activity/property relationships (QSAR/ QSPR), which integrates all necessary mathematical and computational tools to calculate a large variety of molecular descriptors on the basis of the 3D geometrical and quantum chemical structural input of chemical compounds . All the structures were optimized at AM1 level in AMPAC GUI 8 and the output files were used for descriptors calculation in CODESSA. . We considered Log IC50 as biological activity for our study. All the descriptors i.e. constitutional, topological, geometrical, electrostatic, quantum-chemical and thermodynamic were calculated followed by the regression analysis that was carried out using best multi linear regression (BMLR) method. Selected drug molecules were classified into training set (21) and test set (9) (Fig. 5). The test set includes mol2, mol5, mol42, mol13, mol69, mol84, mol97, mol95 and mol63 (Table.1).
Docking simulations for the selected 30 molecules with HIV-1 reverse transcriptase protein were carried out using the FlexX program interfaced with SYBYL 7.0 FlexX is a fast algorithm for the flexible docking of small ligands into fixed protein binding sites using an incremental construction process (Rarey
Cross docking calculations were performed on the same ligand data set using GOLD program. In the GOLD program, the default parameters (population size 100; selection-pressure 1.1; number of operations 10,0000; number of islands 5; niche size 2; and operator weights for migrate, mutate and crossover are 10, 95 and 95) were applied. The active site was defined with 10 Angstrom radius .GOLD scoring function that includes Van der Waals
The active site was detected from the binding pocket of Tenofovir. The active site consists of MET, ASP, MET and TRP residues at positions 230, 186, 184 and 229 respectively (Fig.3). These residues were part of chain A.
Results and Discussions
BMLR regression analysis was performed to get the QSAR equation. This resulted in total 11 models with a maximum of 12 descriptors (Table 3).To get the optimum number of descriptors the r 2 and q 2 values were taken and plotted against the number of descriptors (Fig. 4). From the plot, the saturation point was checked and the one having seven descriptors was selected as a best model and analyzed further.
The descriptors obtained from this model are as follows (Table 4):
Maximum n-n repulsion for a C-C bond (constitutional): -276.94
Exchange of eng+ e-e repulsion for a H-O bond (quantum chemical): - 0.2429
Minimum atomic state energy for a O atom (constitutional): 0.2029
Maximum n-n repulsion for a C-N bond (constitutional): -0.9649
HA dependent HDSA-1 [quantum chemical PC]: 0.02258
HASA-2/TMSA (total molecular surface area) [Zefirov's PC]: 47.678
Maximum valency of a H atom (constitutional): 104.64
Finally, four constitutional and three quantum chemical descriptors were obtained.
The descriptors encoding significant structural information such as Maximum n-n repulsion for a C-C bond (constitutional), Exchange of eng+ e-e repulsion for a H-O bond (quantum chemical), Minimum atomic state energy for a O atom (constitutional), Maximum n-n repulsion for a C-N bond (constitutional), HA dependent HDSA-1 [quantum chemical PC], HASA-2/TMSA (total molecular surface area) [Zefirov's PC], Maximum valency of a H atom (constitutional) can be used to get a unique characteristics of compounds to build the relationship between the structure and biological activity. The t-test results provide the contribution of the descriptors in the following order Max n-n repulsion for a C-C bond> Max n-n repulsion for a C-N bond> HASA-2/TMSA [Zefirov's PC]> Exch. Eng + e-e repulsion for a H-O bond> Max valency of a H atom> Intercept> Min atomic state energy for a O atom> HA dependent HDSA-1 [Quantum chemical PC]
The docking study was carried out by taking the reverse transcriptase enzyme (PDBID- 1T03) which was crystallized with a co-crystal, Tenofovir, a reported drug. This reverse transcriptase receptor contains A, B, H, L chain and it also contains a stretch of nucleotide molecule. The drug Tenofovir is bound in the A chain. The active site of the drug is determined for the purpose of docking the set of ligands. It contains Gly231, Asp 186, Met184, Met230, and Trp229 (Fig.3). The considered drugs were docked against this particular receptor.
In the FlexX docking result, the molecule 65 (2,6–Diaminopurine-2, 3dideoxydihydroribose) gave the most negative score of -15.7 kcal/mol (Table.5), which is having the highest interaction while in GOLD result molecule 65 is coming in the best ranking list with its 7 th conformation with a fitness score of 32.49(Table. 6).
HAART (Highly Active Anti Retro Viral Therapy) is one of the most promising treatment method for AIDS where different combination of NRTI, NNRTI or protease inhibitors are prescribed to the patient to prevent the virus in different stages of its life cycle. This kind of computational approach will help us to extract the important descriptors from the existing drug series to add up a new molecule in the NRTI drug list and to combat with fast drug resistance property of the virus.
The 2D QSAR study gave a clear correlation of the drug dataset with the biological activity and extracted few constitutional and quantum chemical descriptors which may become the key factor in future drug discovery process. The result indicates an effective role of surface area of the drugs for the activity. A probable role of the Fluorine and Nitrogen in increment of electrostatic interaction is also expected. Apart from this, the docking study will help us to check out the binding capability of the drug. Just the addition of a single drug in the series often results in offshoot of the drug combinations for HAART therapy.
Thus, it can be inferred that these factors (extracted descriptors and total negative binding energy range) play a major role in the maintenance of biological activity i.e. IC50 of the drugs of NRTI series. Binding of ligands depends on several interaction factors like electrostatic bonding, hydrogen bonding, hydrophobic interaction, quantum chemical interaction; thus descriptors encoding significant structural information such as constitutional; quantum chemical environment can be used to get unique characteristics of compounds to build the relationship between the structure and biological activity.
This study will enhance our understanding on the descriptors responsible to increase the drug activity and the special features of best molecule interacting with reverse transcriptase and hence, will result in a better drug synthesis strategy.
Authors are grateful to Dr. G. Narahari Sastry, Head, Molecular Modeling Group, Organic I division, IICT for his valuable suggestions and providing us the necessary software facilities.
Dr. U.S.N Murty Biology Division Indian Institute of Chemical Technology, Tarnaka, Hyderabad-500007, India Email: email@example.com