In silico modeling and empirical study of 4-n-Butylresorcinol nanoliposome formulation.

A study to incorporate in silico modeling with an empirical experiment has been carried out to formulate nanoliposome containing 4-n-butylresorcinol as the active ingredient. The in silico modeling was performed using molecular dynamics simulation followed by radius of gyration observation to provide insight into the mechanisms of 4-n-butylresorcinol stabilization by liposome due to their nano-size. The empirical experiment was conducted by formulating the nanoliposome using soy lecithin phospholipid formula as suggested by the in silico modeling followed by determining its particle size as well as its shape. From their incorporation, it was found that 3200 phospholipid molecules were selected in formulating nanoliposome containing 4-n-butylresorcinol. The results of the nanoliposomes size observation in the modeling of 3200 lipid molecules was 87.01 (± 0.59) nm, whereas the size from the empirical study was 87.57 (± 0.06) nm. Communicated by Ramaswamy H. Sarma.

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS.

We describe here our tool named PyPLIF HIPPOS, which was newly developed to analyze the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds up the computational times by separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Information in this application note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, we present here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.

Natural Peptides in Drug Discovery Targeting Acetylcholinesterase.

Acetylcholinesterase-inhibitory peptide has gained much importance since it can inhibit acetylcholinesterase (AChE) and increase the availability of acetylcholine in cholinergic synapses, enhancing cholinergic transmission in pharmacological treatment of Alzheimer's disease (AD). Natural peptides have received considerable attention as biologically important substances as a source of AChE inhibitors. These natural peptides have high potential pharmaceutical and medicinal values due to their bioactivities as neuroprotective and neurodegenerative treatment activities. These peptides have attracted great interest in the pharmaceutical industries, in order to design potential peptides for use in the prophylactic and therapy purposes. Some natural peptides and their derivatives have high commercial values and have succeeded in reaching the pharmaceutical market. A large number of peptides are already in preclinical and clinical pipelines for treatment of various diseases. This review highlights the recent researches on the various natural peptides and future prospects for AD management.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

UNLABELLED: Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα). AVAILABILITY: PyPLIF is freely available at http://code.google.com/p/pyplif.

Structure-based design of eugenol analogs as potential estrogen receptor antagonists.

Eugenol is an essential oil mainly found in the buds and leaves of clove (Syzygium aromaticum (L.) Merrill and Perry), which has been reported to have activity on inhibition of cell proliferation and apoptosis induction in human MCF-7 breast cancer cells. This biological activity is correlated to its activity as an estrogen receptor antagonist. In this article, we present the construction and validation of structure-based virtual screening (SBVS) protocols to identify the potent estrogen receptor α (ER) antagonists. The selected protocol, which gave acceptable enrichment factors as a virtual screening protocol, subsequently used to virtually screen eugenol, its analogs and their dimers. Based on the virtual screening results, dimer eugenol of 4-[4-hydroxy-3-(prop-2-en-1- yl)phenyl]-2-(prop-2-en-1-yl)phenol is recommended to be developed further in order to discover novel and potent ER antagonists.