It is becoming clear that integration of data derived from a variety of bio-physical techniques at multiple levels of resolution is essential for the structural analysis of large complexes ( Thalassinos et al., 2013). However, structural characterization of large complexes by high resolution experimental techniques, such as X-ray crystallography or NMR spectroscopy, is challenging, whereas electron microscopy or mass spectrometry (MS) produce low resolution data ( Alber et al., 2007). Detailed structural characterization of such complexes is a primary goal of structural biology. A cell consists of hundreds of different functional complexes, such as the RNA exosome, the proteasome and the nuclear pore complex ( Robinson et al., 2007). Many cellular processes are performed by multimolecular protein complexes ( Krogan et al., 2006). It placed correctly most of the subunits of multimolecular complexes of up to 16 subunits and significantly outperformed the CombDock and Haddock multimolecular docking methods.Ĭontact: or information: Supplementary data are available at Bioinformatics online. The method was tested on several representative complexes, both in the bound and unbound cases. The optimal assembly of the individual subunits is formulated as an Integer Linear Programming task. The algorithm accepts as input atomic resolution structures of the individual subunits obtained from X-ray, NMR or homology modeling, and interaction data between the subunits obtained from mass spectrometry. Results: We present a novel integrative computational modeling method, which integrates both low and high resolution experimental data. Experimental techniques can provide atomic resolution structures of single proteins and small complexes, or low resolution data of large multimolecular complexes. Motivation: Atomic resolution modeling of large multimolecular assemblies is a key task in Structural Cell Biology.
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