TY - JOUR
T1 - A method for integrative structure determination of protein-protein complexes
AU - Schneidman-Duhovny, Dina
AU - Rossi, Andrea
AU - Avila-Sakar, Agustin
AU - Kim, Seung Joong
AU - Velázquez-Muriel, Javier
AU - Strop, Pavel
AU - Liang, Hong
AU - Krukenberg, Kristin A.
AU - Liao, Maofu
AU - Kim, Ho Min
AU - Sobhanifar, Solmaz
AU - Dötsch, Volker
AU - Rajpal, Arvind
AU - Pons, Jaume
AU - Agard, David A.
AU - Cheng, Yifan
AU - Sali, Andrej
N1 - Funding Information: Funding: DSD has been funded by the Weizmann Institute Advancing Women in Science Postdoctoral Fellowship. We also acknowledge support from NIH R01 GM083960, NIH U54 RR022220 and Rinat (Pfizer) Inc. The SIBYLS beamline at Lawrence Berkeley National Laboratory is supported by the DOE program Integrated Diffraction Analysis Technologies (IDAT). We are also grateful for computer hardware gifts from Ron Conway, Mike Homer, Intel, Hewlett-Packard, IBM and NetApp.
PY - 2012/12
Y1 - 2012/12
N2 - Motivation: Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution.Results: Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein-protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein-protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen-J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure.Availability: http://salilab.org/ idockC.
AB - Motivation: Structural characterization of protein interactions is necessary for understanding and modulating biological processes. On one hand, X-ray crystallography or NMR spectroscopy provide atomic resolution structures but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling assembly structures from individual components frequently suffer from high false-positive rate, rarely resulting in a unique solution.Results: Here, we present a combined approach that computationally integrates data from a variety of fast and accessible experimental techniques for rapid and accurate structure determination of protein-protein complexes. The integrative method uses atomistic models of two interacting proteins and one or more datasets from five accessible experimental techniques: a small-angle X-ray scattering (SAXS) profile, 2D class average images from negative-stain electron microscopy micrographs (EM), a 3D density map from single-particle negative-stain EM, residue type content of the protein-protein interface from NMR spectroscopy and chemical cross-linking detected by mass spectrometry. The method is tested on a docking benchmark consisting of 176 known complex structures and simulated experimental data. The near-native model is the top scoring one for up to 61% of benchmark cases depending on the included experimental datasets; in comparison to 10% for standard computational docking. We also collected SAXS, 2D class average images and 3D density map from negative-stain EM to model the PCSK9 antigen-J16 Fab antibody complex, followed by validation of the model by a subsequently available X-ray crystallographic structure.Availability: http://salilab.org/ idockC.
UR - http://www.scopus.com/inward/record.url?scp=84870777217&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/bioinformatics/bts628
DO - https://doi.org/10.1093/bioinformatics/bts628
M3 - مقالة
C2 - 23093611
SN - 1367-4803
VL - 28
SP - 3282
EP - 3289
JO - Bioinformatics
JF - Bioinformatics
IS - 24
ER -