Virtual screening for anti-HIV-1 RT and anti-HIV-1 PR inhibitors from the Thai medicinal plants database: a combined docking with neural networks approach

The virtual screening approach for docking small molecules into a known protein structure is a powerful tool for drug design. In this work, a combined docking and neural network approach, using a self-organizing map, has been developed and applied to screen anti-HIV-1 inhibitors for two targets, HIV-1 RT and HIV-1 PR, from active compounds available in the Thai Medicinal Plants Database. Based on nevirapine and calanolide A as reference structures in the HIV-1 RT binding site and XK-263 in the HIV-1 PR binding site, 2,684 compounds in the database were docked into the target enzymes. Self-organizing maps were then generated with respect to three types of pharmacophoric groups. The map of the reference structures were then superimposed on the feature maps of all screened compounds. Only the structures having similar features to the reference compounds were accepted. By using the SOMs, the number of candidates for HIV-1 RT was reduced to six and nine compounds consistent with nevirapine and calanolide A, respectively, as references. For the HIV-1 PR target, there are 135 screened compounds showed good agreement with the XK-263 feature map. These screened compounds will be further tested for their HIV-1 inhibitory affinities. The obtained results indicate that this combined method is clearly helpful to perform the successive screening and to reduce the analyzing step from AutoDock and scoring procedure.     

Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA.

In this work, a new ansatz is presented that combines molecular dynamics simulations with MM-PBSA (Molecular Mechanics Poisson-Boltzmann/surface area) to rank the binding affinities of 12 TIBO-like HIV-1 RT inhibitors. Encouraging results have been obtained not only for the relative binding free energies, but also for the absolute ones, which have a root-mean-square deviation of 1.0 kcal/mol (the maximum error is 1.89 kcal/mol). Since the root-mean-square error is rather small, this approach can be reliably applied in ranking the ligands from the databases for this important target. Encouraged by the results, we decided to apply MM-PBSA combined with molecular docking to determine the binding mode of efavirenz SUSTIVA(TM) another promising HIV-1 RT inhibitor for which no ligand-protein crystal structure had been published at the time of this work. To proceed, we define the following ansatz: Five hundred picosecond molecular dynamics simulations were first performed for the five binding modes suggested by DOCK 4.0, and then MM-PBSA was carried out for the collected snapshots. MM-PBSA successfully identified the correct binding mode, which has a binding free energy about 7 kcal/mol more favorable than the second best mode. Moreover, the calculated binding free energy (-13.2 kcal/mol) is in reasonable agreement with experiment (-11.6 kcal/mol). In addition, this procedure was also quite successful in modeling the complex and the structure of the last snapshot was quite close to that of the measured 2,3 A resolution crystal (structure the root-mean-square deviation of the 54 C(alpha) around the binding site and the inhibitor is 1.1 A). We want to point out that this result was achieved without prior knowledge of the structure of the efavirenz/RT complex. Therefore, molecular docking combined with MD simulations followed by MM-PBSA analysis is an attractive approach for modeling protein complexes a priori.     

CoMFA 3D-QSAR analysis of HIV-1 RT nonnucleoside inhibitors, TIBO derivatives based on docking conformation and alignment

HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as nonnucleoside RT inhibitors (NNRTIs) and nucleoside analogues. NNRTIs bind in a region not associated with the active site of the enzyme. Within the NNRTI category, there is a set of inhibitors commonly referred to as TIBO inhibitors. Fifty TIBO inhibitors were used in the work to build 3-D QSAR models. The two known crystal structures of complexes are used to investigate and validate the docking protocol. The results show that the docking simulations reproduce the crystal complexes very well with RMSDs of approximately 1 A and approximately 0.6 A for 1REV and 1COU, respectively. The alignment of molecules and "active" conformation selection are the key to a successful 3D-QSAR model by CoMFA. The flexible docking (Autodock3) was used on determination of "active" conformation and molecular alignment, and CoMFA and CoMSIA were used to develop 3D-QSAR models of 50 TIBOs in the work. The 3D-QSAR models demonstrate a good ability to predict the activity of studied compounds (r2 = 0.972, 0.944, q2 = 0.704, 0.776). It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the binding structure of the complex. The results demonstrate that the combination of ligand-based and receptor-based modeling is a powerful approach to build 3D-QSAR models.     

A new pinoresinol-type lignan from Ligularia kanaitizensis

A new pinoresinol-type Lignan, 9alpha-angloyloxypinoresinol (1), was isolated from the roots and rhizomes of Ligularia kanaitizensis (Franch.) Hand.-Mazz, in addition to a known compound, 9alpha-hydroxypinoresinol (2). The structure of this new lignan (1) was established on the basis of 1D and 2D NMR experiments. Anti-HIV-1 RT biological assay showed that 1 was inhibitory to HIV-1 RT.