Dataset Information

A novel approach for assessing macromolecular complexes combining soft-docking calculations with NMR data.

ABSTRACT: We present a novel and efficient approach for assessing protein-protein complex formation, which combines ab initio docking calculations performed with the protein docking algorithm BiGGER and chemical shift perturbation data collected with heteronuclear single quantum coherence (HSQC) or TROSY nuclear magnetic resonance (NMR) spectroscopy. This method, termed "restrained soft-docking," is validated for several known protein complexes. These data demonstrate that restrained soft-docking extends the size limitations of NMR spectroscopy and provides an alternative method for investigating macromolecular protein complexes that requires less experimental time, effort, and resources. The potential utility of this novel NMR and simulated docking approach in current structural genomic initiatives is discussed.

SUBMITTER: Morelli XJ

PROVIDER: S-EPMC2374225 | biostudies-literature | 2001 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel approach for assessing macromolecular complexes combining soft-docking calculations with NMR data.

Morelli X J XJ Palma P N PN Guerlesquin F F Rigby A C AC

Protein science : a publication of the Protein Society 20011001 10

We present a novel and efficient approach for assessing protein-protein complex formation, which combines ab initio docking calculations performed with the protein docking algorithm BiGGER and chemical shift perturbation data collected with heteronuclear single quantum coherence (HSQC) or TROSY nuclear magnetic resonance (NMR) spectroscopy. This method, termed "restrained soft-docking," is validated for several known protein complexes. These data demonstrate that restrained soft-docking extends ...[more]

PMID: 11567104

Similar Datasets

Project description:A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

Dataset Information

A novel approach for assessing macromolecular complexes combining soft-docking calculations with NMR data.

Publications

A novel approach for assessing macromolecular complexes combining soft-docking calculations with NMR data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets