Improved RAD51 binders through motif shuffling based on the modularity of BRC repeats.

This is the final version. Available from the National Academy of Sciences via the DOI in this record. SI Appendix contains detailed descriptions of the cloning of bacterial expression constructs for the 64 shuffled BRC peptide variants, cloning of mammalian expression constructs, and notes on the soluble expression of the shuffled BRC peptide variants. Also included is a description of ITC used to cross-validate the microfluidic measurements, single concentration point measurements carried out with microfluidics, and exemplary titrations carried out by microfluidics. The fluorescence anisotropy data obtained for the 64 separate titrations as well as the Matlab script used in the analysis have been uploaded as separate files. The supplementary data also contain an analysis on the effect of shuffling of BRC peptides and in particular on the effect of the exact shuffle cutoff point placement. X-ray crystallography electron density map images, data collection, and refinement statistics are also to be found in SI Appendix. Additional cell images highlighting the pan-nuclear signal of RAD51 are also included in SI Appendix. The coordinates and corresponding structure factors for the monomeric RAD51:BRC8-2 complex have been deposited to the PDB under accession code 6HQU. As described previously (49), the transformation from intensity maps into anisotropy values from image data was carried out with a custom Matlab code available on GitHub (https://github.com/quantitativeimaging/icetropy). A custom Matlab script used to fit Kd values for the unlabeled competitive GB1-BRC peptides can be found in SI Appendix, Datasets S1–S4. All other study data are included in the article and/or supporting information.Exchanges of protein sequence modules support leaps in function unavailable through point mutations during evolution. Here we study the role of the two RAD51-interacting modules within the eight binding BRC repeats of BRCA2. We created 64 chimeric repeats by shuffling these modules and measured their binding to RAD51. We found that certain shuffled module combinations were stronger binders than any of the module combinations in the natural repeats. Surprisingly, the contribution from the two modules was poorly correlated with affinities of natural repeats, with a weak BRC8 repeat containing the most effective N-terminal module. The binding of the strongest chimera, BRC8-2, to RAD51 was improved by -2.4 kCal/mol compared to the strongest natural repeat, BRC4. A crystal structure of RAD51:BRC8-2 complex shows an improved interface fit and an extended β-hairpin in this repeat. BRC8-2 was shown to function in human cells, preventing the formation of nuclear RAD51 foci after ionizing radiation.Biotechnology and Biological Sciences Research CouncilEuropean Research CouncilMarie Curie Research GrantCancer Research UKEngineering and Physical Sciences Research CouncilEngineering and Physical Sciences Research CouncilWellcome TrustWellcome TrustMedical Research CouncilMedical Research CouncilSchweizerischer Nationalfond

MagFRET : the first genetically encoded fluorescent Mg2+ sensor

Magnesium has important structural, catalytic and signaling roles in cells, yet few tools exist to image this metal ion in real time and at subcellular resolution. Here we report the first genetically encoded sensor for Mg2+, MagFRET-1. This sensor is based on the high-affinity Mg2+ binding domain of human centrin 3 (HsCen3), which undergoes a transition from a molten-globular apo form to a compactly-folded Mg2+-bound state. Fusion of Cerulean and Citrine fluorescent domains to the ends of HsCen3, yielded MagFRET-1, which combines a physiologically relevant Mg2+ affinity (Kd = 148 µM) with a 50% increase in emission ratio upon Mg2+ binding due to a change in FRET efficiency between Cerulean and Citrine. Mutations in the metal binding sites yielded MagFRET variants whose Mg2+ affinities were attenuated 2- to 100-fold relative to MagFRET-1, thus covering a broad range of Mg2+ concentrations. In situ experiments in HEK293 cells showed that MagFRET-1 can be targeted to the cytosol and the nucleus. Clear responses to changes in extracellular Mg2+ concentration were observed for MagFRET-1-expressing HEK293 cells when they were permeabilized with digitonin, whereas similar changes were not observed for intact cells. Although MagFRET-1 is also sensitive to Ca2+, this affinity is sufficiently attenuated (Kd of 10 µM) to make the sensor insensitive to known Ca2+ stimuli in HEK293 cells. While the potential and limitations of the MagFRET sensors for intracellular Mg2+ imaging need to be further established, we expect that these genetically encoded and ratiometric fluorescent Mg2+ sensors could prove very useful in understanding intracellular Mg2+ homeostasis and signaling

Genetically encoded fluorescent probes for Intracellular Zn2+ imaging

In this chapter we provide an overview of the various genetically encoded fluorescent Zn2+ sensors that have been developed over the past 5 to 10 years. We focus on sensors based on Förster resonance energy transfer (FRET), as these have so far proven to be the most useful for detecting Zn2+ in biological samples. Our goal is to provide a balanced discussion of the pros and cons of the various sensors and their application in intracellular imaging. Following the description of the various sensors, several recent applications of these sensors are discussed. We end the chapter by identifying remaining challenges in this field and discussing future perspectives

Genetically encoded fluorescent probes for Intracellular Zn2+ imaging

In this chapter we provide an overview of the various genetically encoded fluorescent Zn2+ sensors that have been developed over the past 5 to 10 years. We focus on sensors based on Förster resonance energy transfer (FRET), as these have so far proven to be the most useful for detecting Zn2+ in biological samples. Our goal is to provide a balanced discussion of the pros and cons of the various sensors and their application in intracellular imaging. Following the description of the various sensors, several recent applications of these sensors are discussed. We end the chapter by identifying remaining challenges in this field and discussing future perspectives