Dataset Information

BINANA: a novel algorithm for ligand-binding characterization.

ABSTRACT: Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), which is freely available for download at http://www.nbcr.net/binana/. To demonstrate the utility of the new algorithm, we use BINANA to confirm that the number of hydrophobic contacts between a ligand and its protein receptor is positively correlated with ligand potency. Additionally, we show how BINANA can be used to search through a large ligand-receptor database to identify those complexes that are remarkable for selected binding features, and to identify lead candidates from a virtual screen with specific, desirable binding characteristics. We are hopeful that BINANA will be useful to computational chemists and structural biologists who wish to automatically characterize many ligand-receptor complexes for key binding characteristics.

SUBMITTER: Durrant JD

PROVIDER: S-EPMC3099006 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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BINANA: a novel algorithm for ligand-binding characterization.

Durrant Jacob D JD McCammon J Andrew JA

Journal of molecular graphics & modelling 20110119 6

Computational chemists and structural biologists are often interested in characterizing ligand-receptor complexes for hydrogen-bond, hydrophobic, salt-bridge, van der Waals, and other interactions in order to assess ligand binding. When done by hand, this characterization can become tedious, especially when many complexes need be analyzed. In order to facilitate the characterization of ligand binding, we here present a novel Python-implemented computer algorithm called BINANA (BINding ANAlyzer), ...[more]

PMID: 21310640

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Project description:The presence of intact ligand-binding domain (LBD) ensures the strict androgen-dependent regulation of androgen receptor (AR): binding of androgen induces structural reorganization of LBD resulting in release of AR from HSP90, suppression of nuclear export which otherwise dominates over import and nuclear translocation of AR as a transcription factor. Thus, loss or defects of the LBD abolish constraint from un-liganded LBD as exemplified by constitutively active AR variants (AR-Vs), which are associated with emerging resistance mechanism to anti-AR therapy in castration-resistant prostate cancer (mCRPC). Recent analysis of the AR splicing landscapes revealed mCRPC harboring multiple AR-Vs with diverse patterns of inclusion/exclusion of exons (exons 4-8) corresponding to LBD to produce namely exon-skipping variants. In silico construction for these AR-Vs revealed four novel AR-Vs having unique features: Exclusion of specified exons introduces a frameshift in variants v5es, v6es and v7es. ARv56es maintains the reading frame resulting in the inclusion of the C-terminal half of the LBD. We systematically characterized these AR-Vs regarding their subcellular localization, affinity for HSP90 and transactivation capability. Notably, ARv5es was free from HSP90, exclusively nuclear, and constitutively active similarly as previously reported for v567es. In contrast, v6es and v7es were similar in that they are cytoplasmic, transcriptionally inactive and bind HSP90, ARv56es was present in both nucleus and cytoplasm, does not bind HSP90 and is transcriptionally inactive. Converting these transcriptionally inactive AR-Vs into active forms, we identified the two separate elements that allosterically suppress otherwise constitutively active AR-Vs; one in exon 5 for v6es and v7es and the other in exon 8 for v56es. Our findings identify a novel constitutively active AR-V, ARv5es and establish a method to predict potential activities of AR-Vs carrying impaired LBD.

Dataset Information

BINANA: a novel algorithm for ligand-binding characterization.

Publications

BINANA: a novel algorithm for ligand-binding characterization.

Similar Datasets

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