Study On The Applications Of QSAR And Molecular Simulation Methods In Drug Design

In recent years, with the development and applications of computer technology and the rapid progress of information science, it has become a hot area that studying the quantitative structure activity relationship of drug molecules and the interactions between drug molecules and biological macromolecules by computer-aided molecular simulation methods. Starting from the earliest Hansch approach, computer-aided molecular modeling methods have been developed, applied very extensively, and achieved fruitful results.Among the computer-aided molecular design methods, the commonly used methods can be roughly divided into two categories:ligand-based approaches and receptor-based approaches. The former one focuses on the internal relationship between structural features of drug molecules and their activities; from the aspect of interactions between drug molecules and biological macromolecules, the latter one investigates the molecular mechanism of the reason why drug molecules have activities and why this molecule is higher than that one. In this dissertation, we investigate the influences of conformations and modeling methods on prediction accuracy of activities; meanwhile, receptor-based approaches are combined with ligand-based methods to improve prediction accuracy.In this dissertation, a brief description of the QSAR principle, homology modeling, molecular docking and molecular dynamics simulation were given in Chapter 1. Each step of QSAR study was illustrated in detail, including sketching molecular structures, descriptors calculation and selection, data splitting, model building, model validations, definition of model application domain and model interpretation, etc. At the same time, the basic theories and methods of homology modeling, molecular docking and molecular dynamics simulation were introduced briefly.In Chapter 2, the base sequence autocorrelation (BSA) descriptors were used to describe structures of oligonucleotides and to develop accurate quantitative structure-retention relationship (QSRR) models of oligonucleotides in ion-pair reversed-phase high-performance liquid chromatography. Through the combination use of multiple linear regression (MLR) and genetic algorithm (GA), QSRR models were developed at temperatures of 30?,40?,50?,60?and 80?, respectively. Satisfactory results were obtained for the single-temperature models (STM). Multi-temperature model (MTM) was also developed that can be used for predicting the retention time at any temperature. The correlation coefficients of retention time prediction for the test set based on the MTM model at 30?,40?,50?,60?and 80?were 0.978,0.982,0.989,0.988 and 0.996, respectively. The corresponding absolute average relative deviations (AARD) for the test set at each temperature were all less than 1%. The new strategy of feature representation and multi-temperatures modeling is a very promising tool for QSRR modeling with good predictive ability for predicting retention time of oligonucleotides at multiple temperatures under the studied condition.In Chapter 3, quantitative structure-activity relationship (QSAR) studies on a series of selective inhibitors of the cyclin-dependent kinase 4 (CDK4) were performed by using two conventional global modeling methods (multiple linear regression (MLR) and support vector machine (SVM)), local lazy regression (LLR) as well as consensus models. It is remarkable that the LLR model could improve the performance of the QSAR model significantly. In addition, due to the fact that each model can predict certain compounds more accurately than other models, the above three derived models were all used as sub-models to build consensus models using three different strategies:average consensus model (ACM), simple weighted consensus model (SWCM) and hat weighted consensus model (HWCM). Through the analysis of the results, the HWCM consensus strategy, firstly proposed in this work, proved to be more reliable and robust than the best single LLR model, ACM and SWCM models.In Chapter 4, molecular docking guided active conformation selection was used in the quantitative structure-activity relationship (QSAR) study of a series of novel protoporphyrinogen oxidase (PPO) inhibitors with herbicidal activities. The developed model can be used for the rational and accurate prediction of herbicidal activities of these inhibitors from their molecular structures. Molecular docking study was carried out to dock the inhibitors into the PPO active site and to obtain the rational active conformations. Based on the conformations generated from molecular docking, satisfactory predictive results were obtained by genetic algorithm-multiple linear regression (GA-MLR) model according to the internal and external validations. The model gave correlation coefficient R2 of 0.972 and 0.953, absolute average relative deviation AARD of 2.24% and 2.75% for the training set and test set, respectively. The results from this work demonstrate that the molecular docking-guided active conformation selection strategy is rational and useful in the QSAR study of these PPO inhibitors and for the quantitative prediction of their herbicidal activities. The results obtained could be helpful to the design of new derivatives with potential herbicidal activities.In Chapter 5, three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a series of thiazolone derivatives as novel inhibitors bound to the allosteric site of Hepatitis C Virus (HCV) NS5B polymerase were developed based on CoMFA and CoMSIA analyses. Two different conformations of the template molecule and different CoMSIA field/fields were considered to build predictive CoMFA and CoMSIA models. The CoMFA and CoMSIA models with best predictive abilities were obtained by using of the template conformation from X-ray crystal structures. The best CoMFA and CoMSIA models gave q2 values of 0.621 and 0.685, and r2 values of 0.950 and 0.940, respectively for the 51 compounds in the training set. The predictive ability of the two models was also validated by using a test set of 16 compounds which gave rpred2 values 0.685 and 0.822, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps enables the interpretation of their structure-activity relationship and could be very useful in the design of new inhibitors with desired activity.In Chapter 6, given that immunoproteasome inhibitors are currently being developed for a variety of potent therapeutic purposes, the unique specificity of an?',?'-epoxyketone peptide (UK 101) towards the LMP2 subunit of the immunoproteasome (analogous to (35 subunit of the constitutive proteasome) has been investigated in this study for the first time by employing homology modeling, molecular docking, molecular dynamics simulation, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. Based on the simulated binding structures, the calculated binding free energies are in qualitative agreement with the corresponding experimental data and the selectivity of UK101 is explained reasonably. The observed selectivity of UK101 for the LMP2 subunit is rationalized by the requirement for both a linear hydrocarbon chain at the N-terminus and a bulky group at the C-terminus of the inhibitor, because that LMP2 subunit has a much more favorable hydrophobic pocket interacting with the linear hydrocarbon chain, and the bulky group at the C-terminus has a steric clash with the Tyr 169 in (35 subunit. Finally, our results help to clarify why UK101 is specific to the LMP2 subunit of immunoproteasome, and this investigation should be valuable for rational design of more potent LMP2-specific inhibitors. In addition, a series of reversible covalent LMP2 inhibitors were studied by molecular docking to investigate the possible binding mode, and the binding abilities were estimated based on the Goldscore method; the predicted binding abilities were qualitative agreement with the experimental activities. Based on the binding mode information, we revised the structures of the most active inhibitor in order to improve the inhibitory activity to LMP2; the new designed compounds were docked into the active site of LMP2 and Goldscore was calculated for each new compound. It proved that the new compounds indeed have higher inhibitory activity, which indicates that the docking method and binding ability estimated method employed in this study are reliable to do the covalent docking on LMP2.