An insight into the opening path to semi-open conformation of HIV-1 protease by molecular dynamics simulation

Switches of hydrogen bonds during ligand-protein association processes determine binding kinetics

Revealing the processes of ligand-protein associations deepens our understanding of molecular recognition and binding kinetics. Hydrogen bonds (H-bonds) play a crucial role in optimizing ligand-protein interactions and ligand specificity. In addition to the formation of stable H-bonds in the final bound state, the formation of transient H-bonds during binding processes contributes binding kinetics that define a ligand as a fast or slow binder, which also affects drug action. However, the effect of forming the transient H-bonds on the kinetic properties is little understood. Guided by results from coarse-grained Brownian dynamics simulations, we used classical molecular dynamics simulations in an implicit solvent model and accelerated molecular dynamics simulations in explicit waters to show that the position and distribution of the H-bond donor or acceptor of a drug result in switching intermolecular and intramolecular H-bond pairs during ligand recognition processes. We studied two major types of HIV-1 protease ligands: a fast binder, xk263, and a slow binder, ritonavir. The slow association rate in ritonavir can be attributed to increased flexibility of ritonavir, which yields multistep transitions and stepwise entering patterns and the formation and breaking of complex H-bond pairs during the binding process. This model suggests the importance of conversions of spatiotemporal H-bonds during the association of ligands and proteins, which helps in designing inhibitors with preferred binding kinetics.

Anti-HIV drug development through computational methods

Although highly active antiretroviral therapy (HAART) is effective in controlling the progression of AIDS, the emergence of drug-resistant strains increases the difficulty of successful treatment of patients with HIV infection. Increasing numbers of patients are facing the dilemma that comes with the running out of drug combinations for HAART. Computational methods play a key role in anti-HIV drug development. A substantial number of studies have been performed in anti-HIV drug development using various computational methods, such as virtual screening, QSAR, molecular docking, and homology modeling, etc. In this review, we summarize recent advances in the application of computational methods to anti-HIV drug development for five key targets as follows: reverse transcriptase, protease, integrase, CCR5, and CXCR4. We hope that this review will stimulate researchers from multiple disciplines to consider computational methods in the anti-HIV drug development process.

New insights into the in silico prediction of HIV protease resistance to nelfinavir

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases.