Enhanced specificity mutations perturb allosteric signaling in CRISPR-Cas9

Abstract

CRISPR-Cas9 is a molecular tool with transformative genome editing capabilities. At the molecular level, an intricate allosteric signaling is critical for DNA cleavage, but its role in the specificity enhancement of the Cas9 endonuclease is poorly understood. Here, multi-microsecond molecular dynamics is combined with solution NMR and graph theory-derived models to probe the allosteric role of key specificity-enhancing mutations. We show that mutations responsible for increasing the specificity of Cas9 alter the allosteric structure of the catalytic HNH domain, impacting the signal transmission from the DNA recognition region to the catalytic sites for cleavage. Specifically, the K855A mutation strongly disrupts the allosteric connectivity of the HNH domain, exerting the highest perturbation on the signaling transfer, while K810A and K848A result in more moderate effects on the allosteric communication. This differential perturbation of the allosteric signal correlates to the order of specificity enhancement (K855A > K848A ~ K810A) observed in biochemical studies, with the mutation achieving the highest specificity most strongly perturbing the signaling transfer. These findings suggest that alterations of the allosteric communication from DNA recognition to cleavage are critical to increasing the specificity of Cas9 and that allosteric hotspots can be targeted through mutational studies for improving the system's function.

Molecular Dynamics Simulations Establish the Molecular Basis for the Broad Allostery Hotspot Distributions in the Tetracycline Repressor

“…The key realization is that allostery hotspots may contribute by mediating either interdomain communications or intradomain energetics. Thus, our analysis highlights that the “communication pathway” ,,− and “shifting ensemble” , views of protein allostery are not in conflict, since both types of allostery hotspots are likely to contribute in a single system, which explains their broad distribution. Taking this perspective about allostery hotspots will broaden the strategies to modulate protein allostery in mechanistic and engineering studies.…”

“…Previous studies usually focused on one of the mechanisms. For example, most molecular dynamics simulations, 27−31 structural, 24,25 and motion 34,35 based analyses focused entirely on residues that either propagate conformational changes (e.g., hinge residues) or undergo conformational transitions themselves; accordingly, such analyses generally aimed to identify a group of residues that form pathways of "information transfer" between the functional and effector sites. By contrast, analyses based entirely on thermodynamic models 13,56 argued against the existence of specific allostery pathways 87 and emphasized a holistic view of residual contributions in terms of modulating free energies (or statistical weights) of key conformational states.…”

“…In particular, we find that analysis of JS divergence is valuable for selecting collective variables that best characterize conformational changes and identifying residues that undergo major local conformational transitions. While covariance matrix-based analyses have been used extensively in the literature for identifying allosteric pathways between functional and effector sites, − our work highlights that such analyses do not have to be limited to interdomain communications; instead, judiciously designed covariance analyses can also be useful for identifying sites that modulate allostery by controlling intradomain energetics. Along this line, for systems that are well described by a conformational selection model, our study suggests that focusing on the covariance-based analyses in the apo state is likely most productive.…”

“…Along this line, for systems that are well described by a conformational selection model, our study suggests that focusing on the covariance-based analyses in the apo state is likely most productive. With increasing computational power, MD simulations hold great promise in helping test engineering ideas and designs, although systematic comparisons between computations and experiments, , as done in the current work, are required as essential validations.…”

“…For a more efficient exploration of allostery hotspots and analysis of their contributions, various computational approaches have been proposed over the years, ranging from sequence-based techniques that focus on statistics of amino acid coevolution , through structural-informatics based models probing propagation of conformational distortions , to various molecular dynamics-based approaches that analyze transient intermediates or networks of motional coupling − among protein residues. While experimental support for computationally predicted hotspots has been reported in many studies, − the validation is often not comprehensive due to, again, the limited amount of experimental data. Many previous computational studies conducted analyses with relatively short molecular simulations, typically on the order of a few hundreds of nanoseconds or less, limiting the robustness of the results.…”