American Chemical Society
Browse
ac0c05468_si_001.pdf (15.32 MB)

Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy

Download (15.32 MB)
journal contribution
posted on 2021-05-17, 16:33 authored by Justin T. Seffernick, Shane M. Canfield, Sophie R. Harvey, Vicki H. Wysocki, Steffen Lindert
A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein–protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein–protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein–protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD100 of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD100 less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.

History