FRETting about the affinity of bimolecular protein-protein interactions

Chemical and Biological Strategies for Profiling Protein-Protein Interactions in Living Cells

Protein-protein interactions (PPIs) play critical roles in almost all cellular signal transduction events. Characterization of PPIs without interfering with the functions of intact cells is very important for basic biology study and drug developments. However, the ability to profile PPIs especially those weak/transient interactions in their native states remains quite challenging. To this end, many endeavors are being made in developing new methods with high efficiency and strong operability. By coupling with advanced fluorescent microscopy and mass spectroscopy techniques, these strategies not only allow us to visualize the subcellular locations and monitor the functions of protein of interest (POI) in real time, but also enable the profiling and identification of potential unknown interacting partners in high-throughput manner, which greatly facilitates the elucidation of molecular mechanisms underlying numerous pathophysiological processes. In this review, we will summarize the typical methods for PPIs identification in living cells and their principles, advantages and limitations will also be discussed in detail.

Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures

Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.

The characterization of protein interactions - what, how and how much?

Protein interactions underlie most molecular events in biology. Many methods have been developed to identify protein partners, to measure the affinity with which these biomolecules interact and to characterise the structures of the complexes. Each approach has its own advantages and limitations, and it can be difficult for the newcomer to determine which methodology would best suit their system. This review provides an overview of many of the techniques most widely used to identify protein partners, assess stoichiometry and binding affinity, and determine low-resolution models for complexes. Key methods covered include: yeast two-hybrid analysis, affinity purification mass spectrometry and proximity labelling to identify partners; size-exclusion chromatography, scattering methods, native mass spectrometry and analytical ultracentrifugation to estimate stoichiometry; isothermal titration calorimetry, biosensors and fluorometric methods (including microscale thermophoresis, anisotropy/polarisation, resonance energy transfer, AlphaScreen, and differential scanning fluorimetry) to measure binding affinity; and crosslinking and hydrogen-deuterium exchange mass spectrometry to probe the structure of complexes.

Structure-guided evolution of Green2 toward photostability and quantum yield enhancement by F145Y substitution

Quantum yield is a determinant for fluorescent protein (FP) applications and enhancing FP brightness through gene engineering is still a challenge. Green2, our de novo FP synthesized by microfluidic picoarray and cloning, has a significantly lower quantum yield than enhanced green FP, though they have high homology and share the same chromophore. To increase its quantum yield, we introduced an F145Y substitution into Green2 based on rational structural analysis. Y145 significantly increased the quantum yield (0.22 vs. 0.18) and improved the photostability (t_1/2 , 73.0 s vs. 46.0 s), but did not affect the excitation and emission spectra. Further structural analysis showed that the F145Y substitution resulted in a significant electrical field change in the immediate environment of the chromophore. The perturbation of electrostatic charge around the chromophore lead to energy barrier changes between the ground and excited states, which resulted in the enhancement of quantum yield and photostability. Our results illustrate a typical example of engineering an FP based solely on fluorescence efficiency optimization and provide novel insights into the rational evolution of FPs.

Advanced FRET normalization allows quantitative analysis of protein interactions including stoichiometries and relative affinities in living cells

FRET (Fluorescence Resonance Energy Transfer) measurements are commonly applied to proof protein-protein interactions. However, standard methods of live cell FRET microscopy and signal normalization only allow a principle assessment of mutual binding and are unable to deduce quantitative information of the interaction. We present an evaluation and normalization procedure for 3-filter FRET measurements, which reflects the process of complex formation by plotting FRET-saturation curves. The advantage of this approach relative to traditional signal normalizations is demonstrated by mathematical simulations. Thereby, we also identify the contribution of critical parameters such as the total amount of donor and acceptor molecules and their molar ratio. When combined with a fitting procedure, this normalization facilitates the extraction of key properties of protein complexes such as the interaction stoichiometry or the apparent affinity of the binding partners. Finally, the feasibility of our method is verified by investigating three exemplary protein complexes. Altogether, our approach offers a novel method for a quantitative analysis of protein interactions by 3-filter FRET microscopy, as well as flow cytometry. To facilitate the application of this method, we created macros and routines for the programs ImageJ, R and MS-Excel, which we make publicly available.

Exploring Protein⁻Protein Interaction in the Study of Hormone-Dependent Cancers

Estrogen receptors promote target gene transcription when they form a dimer, in which two identical (homodimer) or different (heterodimer) proteins are bound to each other. In hormone-dependent cancers, hormone receptor dimerization plays pivotal roles, not only in the pathogenesis or development of the tumors, but also in the development of therapeutic resistance. Protein–protein interactions (PPIs), including dimerization and complex formation, have been also well-known to be required for proteins to exert their functions. The methods which could detect PPIs are genetic engineering (i.e., resonance energy transfer) and/or antibody technology (i.e., co-immunoprecipitation) using cultured cells. In addition, visualization of the target proteins in tissues can be performed using antigen–antibody reactions, as in immunohistochemistry. Furthermore, development of microscopic techniques (i.e., electron microscopy and confocal laser microscopy) has made it possible to visualize intracellular and/or intranuclear organelles. We have recently reported the visualization of estrogen receptor dimers in breast cancer tissues by using the in situ proximity ligation assay (PLA). PLA was developed along the lines of antibody technology development, and this assay has made it possible to visualize PPIs in archival tissue specimens. Localization of PPI in organelles has also become possible using super-resolution microscopes exceeding the resolution limit of conventional microscopes. Therefore, in this review, we summarize the methodologies used for studying PPIs in both cells and tissues, and review the recently reported studies on PPIs of hormones.