Integrative structure and function of the yeast exocyst complex

Abstract

Exocyst is an evolutionarily conserved hetero-octameric tethering complex that plays a variety of roles in membrane trafficking, including exocytosis, endocytosis, autophagy, cell polarization, cytokinesis, pathogen invasion, and metastasis. Exocyst serves as a platform for interactions between the Rab, Rho, and Ral small GTPases, SNARE proteins, and Sec1/Munc18 regulators that coordinate spatial and temporal fidelity of membrane fusion. However, its mechanism is poorly described at the molecular level. Here, we determine the molecular architecture of the yeast exocyst complex by an integrative approach, based on a 3D density map from negative-stain electron microscopy (EM) at~16 Å resolution, 434 disuccinimidyl suberate and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride cross-links from chemical-crosslinking mass spectrometry, and partial atomic models of the eight subunits. The integrative structure is validated by a previously determined cryo-EM structure, cross-links, and distances from in vivo fluorescence microscopy. Our subunit configuration is consistent with the cryo-EM structure, except for Sec5. While not observed in the cryo-EM map, the integrative model localizes the N-terminal half of Sec3 near the Sec6 subunit. Limited proteolysis experiments suggest that the conformation of Exo70 is dynamic, which may have functional implications for SNARE and membrane interactions. This study illustrates how integrative modeling based on varied low-resolution Abbreviations: CATCHR, complexes associated with tethering containing helical rods; CXMS, chemical-crosslinking mass spectrometry; DSS, disuccinimidyl suberate; EDC, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride; EM, electron microscopy; GMM, Gaussian mixture model; IMP, integrative modeling platform; LC/MS, liquid chromatography and mass spectrometry; MTC, multisubunit tethering complex; PH domain, Pleckstrin homology; PMI, python modeling interface; RMSD, root-mean-square deviation; RMSF, root-mean-square fluctuation; SNARE, soluble NSF attachment protein receptor. Sai J. Ganesan and Michael J. Feyder authors contributed equally to this work.

Improving integrative 3D modeling into low‐ to medium‐resolution electron microscopy structures with evolutionary couplings

“…When combined with other experimental techniques, evolutionary couplings have the ability to improve structural predictions. The Integrative Modeling Platform 21 can use distance restraints, including chemical crosslinks, along with atomic models and electron microscopy data to produce predictions of large molecular ensembles 22,23 . These methods use curated scoring functions combined with Monte Carlo sampling and simulated annealing to produce ensembles of models that satisfy experimental restraints.…”

Improving integrative 3D modeling into low- to medium- resolution EM structures with evolutionary couplings

“…When combined with other experimental techniques, evolutionary couplings have the ability to improve structural predictions. The Integrative Modeling Platform 21 can use distance restraints, including chemical crosslinks, along with atomic models and electron microscopy data to produce predictions of large molecular ensembles 22; 23 . These methods use curated scoring functions combined with Monte Carlo sampling and simulated annealing to produce ensembles of models that satisfy experimental restraints.…”

PrISM: Precision for Integrative Structural Models

“…Data from complementary experiments is combined with physical principles, statistical inference, and existing models to produce the structure (Russel et al , 2012; Webb et al , 2018; Rout and Sali, 2019; Saltzberg et al , 2021). Several large assemblies have been recently determined by this approach, yielding insights on various processes such as transcription (Robinson et al , 2015), gene regulation and DNA repair (Luo et al , 2015), intra-cellular transport (Kim et al , 2018; Ganesan et al , 2020), cell cycle progression (Viswanath, Bonomi, et al , 2017), immune response and metabolism (Lasker et al , 2012; Gutierrez et al , 2020).…”