Efficiency of cell-free and cell-associated virus in mucosal transmission of human immunodeficiency virus type 1 and simian immunodeficiency virus.

Effective strategies are needed to block mucosal transmission of human immunodeficiency virus type 1 (HIV-1). Here, we address a crucial question in HIV-1 pathogenesis: whether infected donor mononuclear cells or cell-free virus plays the more important role in initiating mucosal infection by HIV-1. This distinction is critical, as effective strategies for blocking cell-free and cell-associated virus transmission may be different. We describe a novel ex vivo model system that utilizes sealed human colonic mucosa explants and demonstrate in both the ex vivo model and in vivo using the rectal challenge model in rhesus monkeys that HIV-1-infected lymphocytes can transmit infection across the mucosa more efficiently than cell-free virus. These findings may have significant implications for our understanding of the pathogenesis of mucosal transmission of HIV-1 and for the development of strategies to prevent HIV-1 transmission.

Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina.

BACKGROUND: The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. METHODOLOGY/PRINCIPAL FINDINGS: Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. CONCLUSIONS/SIGNIFICANCE: In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.