Determining Protein Complex Structures Based on a Bayesian Model of in Vivo Förster Resonance Energy Transfer (FRET) Data

Massimiliano Bonomi,Riccardo Pellarin,Seung Joong Kim,Daniel Russel,Bryan A Sundin,Michael Riffle,Daniel Jaschob,Richard Ramsden,Trisha N Davis,Eric G.D Muller,Andrej Sali

doi:10.1074/mcp.m114.040824

Abstract

The use of in vivo Förster resonance energy transfer (FRET) data to determine the molecular architecture of a protein complex in living cells is challenging due to data sparseness, sample heterogeneity, signal contributions from multiple donors and acceptors, unequal fluorophore brightness, photobleaching, flexibility of the linker connecting the fluorophore to the tagged protein, and spectral cross-talk. We addressed these challenges by using a Bayesian approach that produces the posterior probability of a model, given the input data. The posterior probability is defined as a function of the dependence of our FRET metric FRETR on a structure (forward model), a model of noise in the data, as well as prior information about the structure, relative populations of distinct states in the sample, forward model parameters, and data noise. The forward model was validated against kinetic Monte Carlo simulations and in vivo experimental data collected on nine systems of known structure. In addition, our Bayesian approach was validated by a benchmark of 16 protein complexes of known structure. Given the structures of each subunit of the complexes, models were computed from synthetic FRETR data with a distance root-mean-squared deviation error of 14 to 17 Å. The approach is implemented in the open-source Integrative Modeling Platform, allowing us to determine macromolecular structures through a combination of in vivo FRETR data and data from other sources, such as electron microscopy and chemical cross-linking.

Highlights

From the ‡Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158; §Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; ʈDepartment of Biochemistry, University of Washington, Seattle, Washington 98195
We proceeded with comparisons of FRETR predictions from molecular dynamics simulations to in vivo experimental data that were collected from yeast cells expressing constructs of CFP and YFP separated by any one of nine defined linkers and protein structures
The accuracy of structural modeling using synthetic FRETR data and the structures of each individual subunit was assessed via comparison of native molecular architectures of 16 protein complexes with their models computed with our Bayesian approach

Summary

Introduction

We developed a Bayesian approach that converts data from in vivo Forster resonance energy transfer (FRET) spectroscopy into quantitative distance restraints suitable for structural modeling. FRET occurs when two spectrally matched fluorescent molecules are in close proximity and excitation energy is transferred from the donor to the acceptor fluorophore through nonradiative dipole– dipole coupling (Fig. 1A). The efficiency of this process [13] is a common experimentally derived variable of in vitro single-molecule experiments [14]. Bayesian Modeling of in Vivo FRET Data probe distances over the range of 1 to 10 nm, resulting in spatial restraints for modeling the structure of the studied complex [15, 16]

Methods

Results

Conclusion