A method for integrative structure determination of protein-protein complexes

Abstract

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.

Fig. 1.

Schematic representation of the integrative docking method. The number of possible configurations for two docked proteins is on the order of ∼10¹¹ (three rotational degrees of freedom sampled in five degrees interval and three translational degrees of freedom sampled at 1 Å interval). As the method proceeds, the number of considered configurations decreases

Fig. 2.

Success rate of integrative docking for Benchmark 4.0. (A) Success rate in prediction of orientation (top, top10) and interface (top-I, top10-I) for standard docking and docking restrained by NMR-RTC, CXMS, SAXS, EM2D and EM3D. (B) Success rate for predicting a near-native model within the top 10 models as a function of complex size for standard docking as well as docking restrained by NMR-RTC, CXMS, SAXS, EM2D and EM3D. (C) Success rate for predicting a near-native model within the top 10 models as a function of complex shape for standard docking as well as docking restrained by NMR-RTC, CXMS and SAXS

Fig. 3.

Modeling of the PCSK9–J16 Fab complex. (A) Scoring funnels as a function of L-RMSD for different experimental filters. (B) Top-scoring cluster representatives (red, green, gold and yellow) for integrative docking with SAXS, EM2D and EM3D filters, superimposed on X-ray crystallographic structure (blue). The models are superimposed on PCSK9 (prodomain in cyan, catalytic domain in blue and C-terminal domain in dark blue). (C) Fit of the top-scoring cluster representatives to the SAXS profile, EM2D class averages and EM3D density map