Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

BACKGROUND: Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. METHODS: First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. RESULTS: A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. CONCLUSIONS: The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Computational modeling methods for QSAR studies on HIV-1 integrase inhibitors (2005-2010).

The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.

Computational analysis of HIV-1 protease protein binding pockets.

Mutations that arise in HIV-1 protease after exposure to various HIV-1 protease inhibitors have proved to be a difficult aspect in the treatment of HIV. Mutations in the binding pocket of the protease can prevent the protease inhibitor from binding to the protein effectively. In the present study, the crystal structures of 68 HIV-1 proteases complexed with one of the nine FDA approved protease inhibitors from the Protein Data Bank (PDB) were analyzed by (a) identifying the mutational changes with the aid of a developed mutation map and (b) correlating the structure of the binding pockets with the complexed inhibitors. The mutations of each crystal structure were identified by comparing the amino acid sequence of each structure against the HIV-1 wild-type strain HXB2. These mutations were visually presented in the form of a mutation map to analyze mutation patterns corresponding to each protease inhibitor. The crystal structure mutation patterns of each inhibitor (in vitro) were compared against the mutation patterns observed in in vivo data. The in vitro mutation patterns were found to be representative of most of the major in vivo mutations. We then performed a data mining analysis of the binding pockets from each crystal structure in terms of their chemical descriptors to identify important structural features of the HIV-1 protease protein with respect to the binding conformation of the HIV-1 protease inhibitors. Data mining analysis is performed using several classification techniques: Random Forest (RF), linear discriminant analysis (LDA), and logistic regression (LR). We developed two hybrid models, RF-LDA and RF-LR. Random Forest is used as a feature selection proxy, reducing the descriptor space to a few of the most relevant descriptors determined by the classifier. These descriptors are then used to develop the subsequent LDA, LR, and hierarchical classification models. Clustering analysis of the binding pockets using the selected descriptors used to produce the optimal classification models reveals conformational similarities of the ligands in each cluster. This study provides important information in understanding the structural features of HIV-1 protease which cannot be studied by other existing in vivo genomic data sets.