An insight into the binding mechanism of Viprinin and its morpholine and piperidine derivatives with HIV-1 Vpr: molecular dynamics simulation, principal component analysis and binding free energy calculation study

In silico investigation of binding propensity of hematoxylin derivative and damnacanthal for their potential inhibitory effect on HIV-1 Vpr from different subtypes

HIV-1, the causative agent of AIDS leads to many deaths worldwide though few options are available as therapeutics. To deal with the continuous mutation in the virus genome, requirement of new drugs is always there. Subtype variation plays a crucial role in case of HIV-1 therapeutics development. In this study, we want to investigate some pre examined molecules that can be effective for HIV-1 VPR. Inhibition of several protein-protein interactions with the small molecules will lead to identify some molecules as therapeutics other than the conventional drugs. We retrieved the sequences of different subtypes from the database and representative sequences were identified. Representative structures were modelled and validated using MD simulations. Forty molecules, showing anti Vpr activity in vitro were identified from literature survey and those were docked with each subtype representative structures. Two molecules a stable Hematoxylin Derivative (SHD) and Damnacanthal (D3), these were shown to be bind more effectively for all the subtypes. The stability of the protein and those two small molecule complexes were identified again with MD simulation followed by the binding energy calculation. Thus, these molecules can be thought as any option other than the conventional drug targeting HIV-1 Vpr.Communicated by Ramaswamy H. Sarma.

Decoding drug resistant mechanism of V32I, I50V and I84V mutations of HIV-1 protease on amprenavir binding by using molecular dynamics simulations and MM-GBSA calculations

Mutations V32I, I50V and I84V in the HIV-1 protease (PR) induce drug resistance towards drug amprenavir (APV). Multiple short molecular dynamics (MSMD) simulations and molecular mechanics generalized Born surface area (MM-GBSA) method were utilized to investigate drug-resistant mechanism of V32I, I50V and I84V towards APV. Dynamic information arising from MSMD simulations suggest that V32I, I50V and I84V highly affect structural flexibility, motion modes and conformational behaviours of two flaps in the PR. Binding free energies calculated by MM-GBSA method suggest that the decrease in binding enthalpy and the increase in binding entropy induced by mutations V32I, I50V and I84V are responsible for drug resistance of the mutated PRs on APV. The energetic contributions of separate residues on binding of APV to the PR show that V32I, I50V and I84V highly disturb the interactions of two flaps with APV and mostly drive the decrease in binding ability of APV to the PR. Thus, the conformational changes of two flaps in the PR caused by V32I, I50V and I84V play key roles in drug resistance of three mutated PR towards APV. This study can provide useful dynamics information for the design of potent inhibitors relieving drug resistance.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.