In this thesis we used single-molecule FRET to investigate the kinetic properties of a protein called Argonaute. While traditionally one uses bulk methods to investigate the molecular properties of proteins, bulk methods do not confer information that is transient, since that is inherently averaged out. Single-molecule methods, as their name imply, allow one to observe interactions between individual molecules and their substrates. This allows one to see fast kinetics which may otherwise be missed. Furthermore, while the bulk methods give one only the average kinetics, single molecule methods also give the distribution of probabilities to access certain bound or conformational states, which in turn can give the observer information of the nature of the stochastic process. We rely in this thesis on single-molecule FRET, an abbreviation for Förster Resonance Energy Transfer: It’s a process where energy is transferred through dipole-dipole interaction from a donor fluorophore to an acceptor fluorophore. The distance between fluorophores determines the efficiency of transfer on a length scale of » 10 nm. Since many biological processes take place on this length scale, this technique is exquisitely suitable to study biological processes real-time on the smallest scale, whether it is conformational changes, protein-protein interactions, or in this case, protein target search studies.@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

In the version of this article initially published, the data in Fig. 6h and Extended Data Fig. 6j were incorrect due to errors in the simulation code. The correct figures are shown below. The Fig. 6h caption has been updated to “Arrows indicate unmasking time of the best fit.”, as the panel now contains more than one arrow. The Extended Data Fig. 6j caption incorrectly stated “Simulated cleavage curves for 10 and 0.1 nM siRNA concentration using fast or slow unmasking rates (average unmasking time of 1 s and 800 s, respectively).” The correct average unmasking time for 0.1 nM siRNA is 1,200 s. The errors have been corrected in the HTML and PDF versions of the article. (Figure presented.).@en

Super-resolution imaging allows for the visualization of cellular structures on a nanoscale level. DNA-PAINT (DNA point accumulation in nanoscale topology) is a super-resolution method that depends on the binding and unbinding of DNA imager strands. The current DNA-PAINT technique suffers from slow acquisition due to the low binding rate of the imager strands. Here we report on a method where imager strands are loaded into a protein, Argonaute (Ago), which allows for faster binding. Ago preorders the DNA imager strand into a helical conformation, allowing for 10 times faster target binding. Using a 2D DNA origami structure, we demonstrate that Ago-assisted DNA-PAINT (Ago-PAINT) can speed up the current DNA-PAINT technique by an order of magnitude, while maintaining the high spatial resolution. We envision this tool to be useful for super-resolution imaging and other techniques that rely on nucleic acid interactions.@en

Argonaute (Ago) proteins are of key importance in many cellular processes. In eukaryotes, Ago can induce translational repression followed by deadenylation and degradation of mRNA molecules through base pairing of microRNAs (miRNAs) with a complementary target on a mRNA sequence. In bacteria, Ago eliminates foreign DNA through base pairing of siDNA (small interfering DNA) with a target on a DNA sequence. Effective targeting activities of Ago require fast recognition of the cognate target sequence among numerous off-target sites. Other target search proteins such as transcription factors (TFs) are known to rely on facilitated diffusion for this goal, but it is undetermined to what extent these small nucleic acid-guided proteins utilize this mechanism. Here, we review recent single-molecule studies on Ago target search. We discuss the consequences of the recent findings on the search mechanism. Furthermore, we discuss the open standing research questions that need to be addressed for a complete picture of facilitated target search by small nucleic acids.@en

Prokaryotic Argonaute proteins (pAgos) constitute a diverse group of endonucleases of which some mediate host defense by utilizing small interfering DNA guides (siDNA) to cleave complementary invading DNA. This activity can be repurposed for programmable DNA cleavage. However, currently characterized DNA-cleaving pAgos require elevated temperatures (≥65°C) for their activity, making them less suitable for applications that require moderate temperatures, such as genome editing. Here, we report the functional and structural characterization of the siDNA-guided DNA-targeting pAgo from the mesophilic bacterium Clostridium butyricum (CbAgo). CbAgo displays a preference for siDNAs that have a deoxyadenosine at the 5'-end and thymidines at nucleotides 2-4. Furthermore, CbAgo mediates DNA-guided DNA cleavage of AT-rich double stranded DNA at moderate temperatures (37°C). This study demonstrates that certain pAgos are capable of programmable DNA cleavage at moderate temperatures and thereby expands the scope of the potential pAgo-based applications.@en

At the core of homologous DNA repair, RecA catalyzes the strand exchange reaction. This process is initiated by a RecA loading protein, which nucleates clusters of RecA proteins on single-stranded DNA. Each cluster grows to cover the single-stranded DNA but may leave 1- to 2-nucleotide (nt) gaps between the clusters due to three different structural phases of the nucleoprotein filaments. It remains to be revealed how RecA proteins eliminate the gaps to make a seamless kilobase-long filament. We develop a single-molecule fluorescence assay to observe the novel internal dynamics of the RecA filament. We directly observe the structural phases of individual RecA filaments and find that RecA proteins move their positions along the substrate DNA to change the phase of the filament. This reorganization process, which is a prerequisite step for interjoining of two adjacent clusters, requires adenosine triphosphate hydrolysis and is tightly regulated by the recombination hotspot, Chi. Furthermore, RecA proteins recognize and self-align to a 3-nt-period sequence pattern of TGG. This sequence-dependent phase bias may help the RecA filament to maintain structural integrity within the kilobase-long filament for accurate homology search and strand exchange reaction.@en

Small interfering RNAs (siRNAs) promote RNA degradation in a variety of processes and have important clinical applications. siRNAs direct cleavage of target RNAs by guiding Argonaute2 (AGO2) to its target site. Target site accessibility is critical for AGO2-target interactions, but how target site accessibility is controlled in vivo is poorly understood. Here, we use live-cell single-molecule imaging in human cells to determine rate constants of the AGO2 cleavage cycle in vivo. We find that the rate-limiting step in mRNA cleavage frequently involves unmasking of target sites by translating ribosomes. Target site masking is caused by heterogeneous intramolecular RNA-RNA interactions, which can conceal target sites for many minutes in the absence of translation. Our results uncover how dynamic changes in mRNA structure shape AGO2-target recognition, provide estimates of mRNA folding and unfolding rates in vivo, and provide experimental evidence for the role of mRNA structural dynamics in control of mRNA-protein interactions.@en

Small interfering RNAs (siRNAs) promote RNA degradation in a variety of processes and have important clinical applications. siRNAs direct cleavage of target RNAs by guiding Argonaute2 (AGO2) to its target site. Target site accessibility is critical for AGO2-target interactions, but how target site accessibility is controlled in vivo is poorly understood. Here, we use live-cell single-molecule imaging in human cells to determine rate constants of the AGO2 cleavage cycle in vivo. We find that the rate-limiting step in mRNA cleavage frequently involves unmasking of target sites by translating ribosomes. Target site masking is caused by heterogeneous intramolecular RNA-RNA interactions, which can conceal target sites for many minutes in the absence of translation. Our results uncover how dynamic changes in mRNA structure shape AGO2-target recognition, provide estimates of mRNA folding and unfolding rates in vivo, and provide experimental evidence for the role of mRNA structural dynamics in control of mRNA-protein interactions.@en