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.

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). PyPLIF is freely available at http://code.google.com/p/pyplif.

The histamine H₄ receptor (H₄R) is the latest identified histamine receptor to emerge as a potential drug target for inflammatory diseases. Animal models are employed to validate this potential drug target. Concomitantly, various H₄R orthologs have been cloned, including the human, mouse, rat, guinea pig, monkey, pig, and dog H₄Rs. In this article, we expressed all these H₄R orthologs in human embryonic kidney 293T cells and compared their interactions with currently used standard H₄R ligands, including the H₄R agonists histamine, 4-methylhistamine, guanidinylethyl isothiourea (VUF 8430), the H₄R antagonists 1-[(5-chloro-1H-indol-2-yl)carbonyl]-4-methylpiperazine (JNJ 7777120) and [(5-chloro-1H-benzimidazol-2-yl)carbonyl]-4-methylpiperazine (VUF 6002), and the inverse H₄R agonist thioperamide. Most of the evaluated ligands display significantly different affinities at the different H₄R orthologs. These “natural mutants” of H₄R were used to study ligand-receptor interactions by using chimeric human-pig-human and pig-human-pig H₄R proteins and site-directed mutagenesis. Our results are a useful reference for ligand selection for studies in animal models of diseases and offer new insights in the understanding of H₄R-ligand receptor interactions.