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bioRxivBiophysics

The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex

D. J. Kauert, J. Madariaga Marcos, M. Rutkauskas, A. Wulfken, I. Songailiene, T. Sinkunas, V. Siksnys, R. Seidel

PDFDOIPreprint

Abstract

The discovery1,2 and the pioneering applications3 of CRISPR-Cas effector complexes have provided powerful gene-editing tools. The effector complexes are guided to the targeted genomic locus by the complementarity of their CRISPR RNA (crRNA)4,5. Recognition of double-stranded DNA targets proceeds via DNA unwinding and base-pairing between crRNA and the DNA target strand resulting in the formation of an R-loop structure5,6. Full R-loop formation is the prerequisite for the subsequent DNA cleavage. While the CRISPR-Cas technology is easy to use, efficient and highly versatile, therapeutic applications are hampered by the off-target effects due to the recognition of unintended sequences with multiple mismatches7. This process is still poorly understood on a mechanistic level8,9. Particularly, the lack of insight into the energetics and dynamics of the R-loop formation hinders a direct modelling of the R-loop formation for off-target prediction. Here we set up ultrafast DNA unwinding experiments based on plasmonic DNA nanorotors to follow the R-loop formation by the Cascade effector complex in real time, close to base pair resolution. We directly resolve a weak global downhill bias of the energy landscape of the forming R-loop followed by a steep uphill bias for the final base pairs. We furthermore show a modulation of the landscape by base flips and mismatches. These data provide that Cascade-mediated R-loop formation occurs on short time scales in single base pair steps of sub-millisecond duration, but on longer time scales in six-base pair intermediate steps in agreement with the structural periodicity of the crRNA-DNA hybrid. We expect that the knowledge about the energy landscapes of R-loop formation of CRISPR-Cas effector complexes will pave the way for a detailed understanding and prediction of off-target recognition10.

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