In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators.

TitleIn silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators.
Publication TypeJournal Article
Year of Publication2007
AuthorsXie L, Wang J, Bourne PE
JournalPLoS Comput Biol
Volume3
Issue11
Paginatione217
Date Published2007 Nov
ISSN1553-7358
KeywordsAmino Acid Sequence, Binding Sites, Computer Simulation, Models, Chemical, Models, Molecular, Molecular Sequence Data, Protein Binding, Protein Conformation, Protein Interaction Mapping, Receptors, Estrogen, Selective Estrogen Receptor Modulators, Sequence Analysis, Protein, Structure-Activity Relationship, Toxicity Tests
Abstract

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein-drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein-ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein-ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.

DOI10.1371/journal.pcbi.0030217
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/18052534?dopt=Abstract
PMCPMC2098847
Alternate TitlePLoS Comput. Biol.
PubMed ID18052534