Dataset Information

A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach.

ABSTRACT: The nicotinic acetylcholine receptor (nAChR) is a member of the ligand-gated ion channel family and is implicated in many neurological events. Yet, the receptor is difficult to target without high-resolution structures. In contrast, the structure of the acetylcholine binding protein (AChBP) has been solved to high resolution, and it serves as a surrogate structure of the extra-cellular domain in nAChR. Here we conduct a virtual screening study of the AChBP using the relaxed-complex method, which involves a combination of molecular dynamics simulations (to achieve receptor structures) and ligand docking. The library screened through comes from the National Cancer Institute, and its ligands show great potential for binding AChBP in various manners. These ligands mimic the known binders of AChBP; a significant subset docks well against all species of the protein and some distinguish between the various structures. These novel ligands could serve as potential pharmaceuticals in the AChBP/nAChR systems.

SUBMITTER: Babakhani A

PROVIDER: S-EPMC2684879 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach.

Babakhani Arneh A Talley Todd T TT Taylor Palmer P McCammon J A JA

Computational biology and chemistry 20090108 2

The nicotinic acetylcholine receptor (nAChR) is a member of the ligand-gated ion channel family and is implicated in many neurological events. Yet, the receptor is difficult to target without high-resolution structures. In contrast, the structure of the acetylcholine binding protein (AChBP) has been solved to high resolution, and it serves as a surrogate structure of the extra-cellular domain in nAChR. Here we conduct a virtual screening study of the AChBP using the relaxed-complex method, which ...[more]

PMID: 19186108

Similar Datasets

Project description:The physiological roles of estrogen in sexual differentiation and development, female and male reproductive processes, and bone health are complex and diverse. Numerous natural and synthetic chemical compounds, commonly known as endocrine disrupting chemicals (EDCs), have been shown to alter the physiological effects of estrogen in humans and wildlife. As such, these EDCs may cause unanticipated and even undesirable effects. Large-scale in vitro and in vivo screening of chemicals to assess their estrogenic activity would demand a prodigious investment of time, labor, and money and would require animal testing on an unprecedented scale. Approaches in silico are increasingly recognized as playing a vital role in screening and prioritizing chemicals to extend limited resources available for experimental testing. Here, we evaluated a multistep procedure that is suitable for in silico (virtual) screening of large chemical databases to identify compounds exhibiting estrogenic activity. This procedure incorporates Shape Signatures, a novel computational tool that rapidly compares molecules on the basis of similarity in shape, polarity, and other bio-relevant properties. Using 4-hydroxy tamoxifen (4-OH TAM) and diethylstilbestrol (DES) as input queries, we employed this scheme to search a sample database of approximately 200,000 commercially available organic chemicals for matches (hits). Of the eight compounds identified computationally as potentially (anti)estrogenic, biological evaluation confirmed two as heretofore unknown estrogen antagonists. Subsequent radioligand binding assays confirmed that two of these three compounds exhibit antiestrogenic activities comparable to 4-OH TAM. Molecular modeling studies of these ligands docked inside the binding pocket of estrogen receptor alpha (ERalpha) elucidated key ligand-receptor interactions that corroborate these experimental findings. The present study demonstrates the utility of our computational scheme for this and related applications in drug discovery, predictive toxicology, and virtual screening.

Project description:BACKGROUND:Despite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods. RESULTS:The chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model. CONCLUSIONS:We report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds.

Dataset Information

A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach.

Publications

A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure