Dataset Information

Receptor-based virtual screening of EGFR kinase inhibitors from the NCI diversity database.

ABSTRACT: Epidermal growth factor receptor (EGFR) abnormalities have been associated with several types of human cancer. The crystal structures of its tyrosine kinase domain (EGFR-TK) complexed with small molecule inhibitors revealed the kinase inhibition modes, prompting us to search for novel anti-cancer drugs. A total of 1,990 compounds from the National Cancer Institute (NCI) diversity set with nonredundant structures have been tested to inhibit cancer cell lines with unknown mechanism. Cancer inhibition through EGFR-TK is one of the mechanisms of these compounds. In this work, we performed receptor-based virtual screening against the NCI diversity database. Using two different docking algorithms, AutoDock and Gold, combined with subsequent post-docking analyses, we found eight candidate compounds with high scoring functions that all bind to the ATP-competitive site of the kinase. None of these compounds belongs to the main group of the currently known EGFR-TK inhibitors. Binding mode analyses revealed that the way these compounds complexed with EGFR-TK differs from quinazoline inhibitor binding and the interaction mainly involves hydrophobic interactions. Also, the common kinase-inhibitor (NH---N and CO---HC) hydrogen bonds between the hinge region and the hit compounds are rarely observed. Our results suggest that these molecules could be developed as novel lead compounds in anti-cancer drug design.

SUBMITTER: Choowongkomon K

PROVIDER: S-EPMC6264413 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Receptor-based virtual screening of EGFR kinase inhibitors from the NCI diversity database.

Choowongkomon Kiattawee K Sawatdichaikul Orathai O Songtawee Napat N Limtrakul Jumras J

Molecules (Basel, Switzerland) 20100604 6

Epidermal growth factor receptor (EGFR) abnormalities have been associated with several types of human cancer. The crystal structures of its tyrosine kinase domain (EGFR-TK) complexed with small molecule inhibitors revealed the kinase inhibition modes, prompting us to search for novel anti-cancer drugs. A total of 1,990 compounds from the National Cancer Institute (NCI) diversity set with nonredundant structures have been tested to inhibit cancer cell lines with unknown mechanism. Cancer inhibit ...[more]

PMID: 20657425

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Project description:Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Dataset Information

Receptor-based virtual screening of EGFR kinase inhibitors from the NCI diversity database.

Publications

Receptor-based virtual screening of EGFR kinase inhibitors from the NCI diversity database.

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