Molecular Modeling Studies Of HIV-1 Reverse Transcriptase And Some Of Its Inhibitors

Acquired immune deficiency syndrome (AIDS) is a set of symptoms resulting from thedamage to the human immune system caused by the human immunodeficiency virus (HIV).About 25 million people worldwide have died from this infection since the start of theepidemic, and 40.3 million people around the world are currently living with HIV/AIDS.There are two types of HIV: HIV-1 and HIV-2. The predominant virus is HIV-1.The prevalence of AIDS has been a big problem and endangering human life, therefore,the study of AIDS drugs becomes a current event in the world. In the infection process, thereverse transcriptase (RT) is very important. RT converts the single-stranded HIV RNA todouble-stranded HIV DNA which contains the instructions HIV needs to use a T-cell's geneticmachinery to reproduce itself. Hence, RT is one of the major targets for the treatment ofAIDS.Reverse transcriptase inhibitors (RTIs) block reverse transcriptase's enzymatic functionand prevent completion of synthesis of the double-stranded viral DNA, thus preventing HIVfrom multiplying. There are two forms of RT inhibitors according to their inhibitorymechanism: nucleoside/nucleotide (analog) reverse transcriptase inhibitors(NRTIs/ NtRTIs)and non-nucleoside reverse transcriptase inhibitors (NNRTIs).Computer-aided drug discovery and design have proven successful in many recentresearch programs. 2D-QSAR and 3D-QSAR have been used in the studies of anti-HIVinhibitors, as well as Molecular docking studies. The application of Moelcular Dyanmics inthe field of HIV-1 has involved studies of HIV-1 protease, reverse transcriptase, integrase andenzymes with their inhibitors. Some studies were performed with different QuantumChemistry methods.The thesis uses several techniques (QSAR, Molecular Docking and Molecular dyanmics)to correlate molecular structure features to their bioactivity, and to study the interaction modebetween reverse transcriptase and their inhibitors. We aim at comparing the different actionmode between RT and their different inhitor families, obtaining more information aboutwhich molecular features are favorable to activity, and aiding to design and synthesize highlyactive ant-HIV inhibitors. In Chapter 1,we present a general introduction of AIDS, HIV-1 reverse trascriptase, theirinhibitors and a brief description of the methods in Computer-Aided Drug Design (CADD). InChapter 2, we present the methods that are used in this work in a detailed way.In Chapter 3, we study a set of pyrimidine nucleosides RT inhibitors and establishquantitative structure-activity relationships (QSAR) using a comprehensive set of geometrical,electrostatic and quantum-chemical molecular descriptors, by multiple linear regression(MLR), support vector machine (SVM) and projection pursuit regression (PPR) methods.MLR yields a linear model withadetermination coefficient (R~2) and mean square error (MSE)of 0.729 and 0.36 for the training set and of 0.662 and 0.42 for the test set, respectively. SVMand PPR methods that we used to construct non-linear prediction models, lead to a better R~2of 0.850 (SVM) and 0.841 (PPR) and MSE of 0.22 (SVM) and 0.21 (PPR) for the sametraining set, together with R~2 of 0.830 (SVM) and 0.840 (PPR) and MSE of 0.27 (SVM) and0.30 (PPR) for the same test set, respectively. The prediction results of the SVM and PPRmodels are better than those of MLR. These models might help designing new pyridinenucleosides inhibitors with enhanced activity.In Chapter 4, we analyze in a similar way another series of HIV-1 reverse transcriptaseinhibitors: 2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners. We usetopological and geometrical, as well as quantum mechanical energy-related and chargedistribution-related descriptors to describe the structural features. We compare six techniques:multiple linear regression (MLR), multivariate adaptive regression splines (MARS), radialbasis function neural networks (RBFNN), general regression neural networks (GRNN),projection pursuit regression (PPR) and support vector machine (SVM) to establish QSARmodels for two data sets:anti-HIV-1 activity and HIV-1 reverse transcriptase binding affinity.Our results show that PPR and SVM models provide a powerful capacity of prediction.This 2D-QSAR analysis is completed with two more approaches: 3D-QSAR, relying onmolecular docking, and molecular dynamics in order to examine into more detail thedrug-protein interaction. Docking simulations are employed to position the inhibitors into theRT active site to determine the most probable binding mode and most reliable conformations.Then we develop comparative molecular field analysis (CoMFA) and comparative molecularsimilarity indices analysis (CoMSIA) approaches, using a complex receptor-based and ligand-based alignment procedure and different alignment modes to obtain highly reliable andpredictive CoMFA and CoMSIA models with cross-validated q~2 value of 0.723 and 0.760,respectively. The CoMFA and CoMSIA contour maps with the 3D structure of the target (thebinding site of RT) inlaid allow us to better understand the interaction between the RT proteinand the inhibitors and the structural requirements for inhibitory activity against HIV-1.Forinstance, we show that for 2-amino-6-arylsulfonylbenzonitriles inhibitors to have appreciableinhibitory activity, bulky and hydrophobic groups in 3-and 5-position of the B ring arerequired. Moreover, H-bond donor groups in 2-position of the A ring to build up H-bondingwith the Lys101 residue of the RT protein are also favorable to activity.We then perform dynamics (MD) simulations in water environment on the RTcomplexes with one representative of each of 3 series of inhibitors:2-amino-6-arylsulfonylbenzonitriles, and their thio and sulfinyl congeners. MolecularMechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular MechanicsGeneralized Born Surface Area (MM-GBSA) are applied to calculate the binding free energybased on the obtained MD trajectories. We carry out a comparison of interaction modes,binding free energy, contributions of the residues to the binding free energy and H-bonds withthe average structures. Our results show that there exist different interaction modes betweenRT and ligands due to the different sulfur functional groups in the inhibitors and to specificinteractions with some residues.In Chapter 5, we study a series of O-(2-phthalimidoethyl)-N-substituted thiocarbamatesand their ring-opened congeners as non-nucleoside HIV-1 reverse transcriptase inhibitors,using 2D-QSAR and 3D-QSAR methods. In 2D-QSAR studies, we used and comparedseveral methods: MLR, RBFNN, PPR, SVM and LS-SVM to build QSAR models. Thedescriptors were selected by heuristic method. Among the 70 compounds, we selected 56 asthe training set. The best results were generated by PPR with a square correlation coefficientR~2 of 0.873 for the training set and 0.755 for the test set. Based on the same conformations ofthe compounds, we then performed ligand-based 3D-QSAR studies, with a cross-validated q~2of 0.701 (with 5 components) in CoMFA and 0.672 (with 6 components) in CoMSIA (SH).In order to obtain more information about the RT receptor interaction with theseinhibitors we performed further studies based on molecular docking. Our results indicate that an H-bond between Lys101 of the protein and ligands exists in most cases. Moreover, someother interactions, such as Van der Waals force, exist contributing to the binding affinitybetween receptor and ligand. The docking conformations of 59O-(2-phthalimidoethyl)-N-substituted thiocarbamates were generated to carry outreceptor-based 3D-QSAR studies. 53 compounds were divided into training set (43) and testset(10). With the training set, a q~2 of 0.488 was obtained in CoMFA, while a higher value ofq~2: 0.642 was obtained in CoMSIA with three SHD descriptors. The steric, electrostatic,hydrophobic and H donor features of the compounds make much contribution to thebioactivity of the inhibitors.