| With the completion of Human Genome Project (HGP), the post-genomic era focusing on the functional genomics and proteomics researches has already come. Studies on the relationship between protein structure and function, and its actual application are important parts of the proteomics research. Protein molecules play crucial roles in the life activities through recognition and interactions with ligand molecules. These interactions are the necessary prerequisites for gene regulation, signal transduction, immunoreaction etc. Therefore, the studies of the interactions between protein receptor and ligand are important for understanding biological regulation mechanism, and cellular structure and function, which will also provide the theory foundation for designing and discovering new drug targets. That's why the study of the interactions and recognition between protein and ligand is one of the pioneering and hot spot in life science field in recent years the time.Since determining protein structures with experimental approach is still challenging, recently, with the continuous progress in computers'processing ability and the rapid development and extensive application of theoretical simulation, molecular modeling methods, such as molecular dynamics (MD), molecular docking and free energy computation, have become important tools for exploring the interaction process of protein with ligand. Molecular modeling not only enables us to understand and reveal the essence of life phenomena in the level of molecule, subunit or even atom, but also provides strong theoretical support to experimental results. With the improvement of molecular modeling theory and technology advances, molecular modeling methods are increasingly being used in the research of protein structure-function relationship, protein-ligand mutual recognition, as well as rational drug design.The prevalence of AIDS dramatically threatens human life health, which makes the drug design against AIDS as a hot field and many countries are spending huge money on it. Human immunodeficiency virus (HIV) integrase (IN) and transmembrane protein gp41 are important targets for designing and developing the novel anti-HIV drug. In the life cycle of HIV, IN aids the integration of viral DNA into the host chromosome and the transmembrane protein gp41 plays a crucial role in the process of virus intrude into the cells. Consequently, the study on the recognition mechanism of IN and gp41with inhibitors is important for the development of anti-HIV drugs. The dissertation was segmented into two parts, in the first part, the binding modes of HIV-1 IN with the a peptide inhibitor from LEDGF/p75 was explored, and the inhibitory mechanism of this inhibitor was explained; in the second part, a series of anti-HIV fusion inhibitors were investigated to establish a prediction model using Topomer CoMFA, and the model was demonstrated have ideal predictive ability.The main content of this dissertation includes the following two parts: 1. Study on inhibitory mechanism and binding mode of the p75-derived peptide to HIV-1 integraseThe binding modes of IN core domain with the p75-derived peptide were first obtained by using molecular docking approach combined with MD simulations. Through the comparison between the complex modes of IN with p75-derived peptide and p75, it was found that the binding modes of IN with the two systems are quite similar. Ala-128,Leu-102,Trp-132,Met-178 and Ile-365, which play crucial roles in crystal structure of IN-p75 is also important to the simulated complex. The result of simulations reveal that the p75-derived peptide can competitive inhibit p75 binding to the interface of IN dimmer, and then inhibit IN binding viral DNA.2. Study on 3D-QSAR of anti-HIV fusion inhibitors using Topomer CoMFAIn this dissertation, we selected the gp41 inhibitor as our targets, and investigated the interactions between gp41 inhibitors and gp41 hydrophobic pocket through the work of 3D-QSAR methods. We selected small molecule inhibitors from the literature, namely is the training set and constructed model by using Topomer CoMFA, then according to the statistical method we can analyze the predicative ability of the model and get a ideal Topomer CoMFA model. These results may provide meaningful guidance and help to the future work including the structure modification, new drug design and predicament. |
