Search for: All records

TheVibrio choleraeCascade–TniQ complex unveiled a new paradigm in biology, demonstrating that CRISPR-associated proteins can direct DNA transposition. Despite the tremendous potential of “knocking-in” genes at desired sites, the mechanisms underlying DNA binding and transposition remain elusive. In this system, a conformational change of the Cas8 protein is essential for DNA binding, yet how it occurs is unclear. Here, structural modeling and free energy simulations reconstruct the Cas8 helical bundle and reveal an open–closed conformational change that is key for the complex’s function. We show that when Cascade–TniQ binds RNA, the Cas8 bundle changes conformation mediated by the interaction with the Cas7.1 protein. This interaction promotes the bundle’s transition toward the open state, priming the complex for DNA binding. As the target DNA binds the guide RNA, the opening of the Cas8 bundle becomes more favorable, exposing positively charged residues and facilitating their interaction with DNA, which ultimately leads the DNA-binding process to completion. These outcomes provide a dynamic representation of a critical conformational change in one of the largest CRISPR systems and illustrate its role at critical steps of the Cascade–TniQ biophysical function, advancing our understanding of nucleic acid binding and transposition mechanisms. 

Abstract CRISPR-based DNA adenine base editors (ABEs) hold remarkable promises to address human genetic diseases caused by point mutations. ABEs were developed by combining CRISPR-Cas9 with a transfer RNA (tRNA) adenosine deaminase enzyme and through directed evolution, conferring the ability to deaminate DNA. However, the molecular mechanisms driving the efficient DNA deamination in the evolved ABEs remain unresolved. Here, extensive molecular simulations and biochemical experiments reveal the biophysical basis behind the astonishing base editing efficiency of ABE8e, the most efficient ABE to date. We demonstrate that the ABE8e’s DNA deaminase domain, TadA8e, forms remarkably stable dimers compared to its tRNA-deaminating progenitor and that the strength of TadA dimerization is crucial for DNA deamination. The TadA8e dimer forms robust interactions involving its R98 and R129 residues, the RuvC domain of Cas9 and the DNA. These locking interactions are exclusive to ABE8e, distinguishing it from its predecessor, ABE7.10, and are indispensable to boost DNA deamination. Additionally, we identify three critical residues that drive the evolution of ABE8e toward improved base editing by balancing the enzyme’s activity and stability, reinforcing the TadA8e dimer and improving the ABE8e’s functionality. These insights offer new directions to engineer superior ABEs, advancing the design of safer precision genome editing tools. 

Abstract CRISPR-Cas12a is a powerful RNA-guided genome-editing system that generates double-strand DNA breaks using its single RuvC nuclease domain by a sequential mechanism in which initial cleavage of the non-target strand is followed by target strand cleavage. How the spatially distant DNA target strand traverses toward the RuvC catalytic core is presently not understood. Here, continuous tens of microsecond-long molecular dynamics and free-energy simulations reveal that an α-helical lid, located within the RuvC domain, plays a pivotal role in the traversal of the DNA target strand by anchoring the crRNA:target strand duplex and guiding the target strand toward the RuvC core, as also corroborated by DNA cleavage experiments. In this mechanism, the REC2 domain pushes the crRNA:target strand duplex toward the core of the enzyme, while the Nuc domain aids the bending and accommodation of the target strand within the RuvC core by bending inward. Understanding of this critical process underlying Cas12a activity will enrich fundamental knowledge and facilitate further engineering strategies for genome editing. 

Abstract Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. It utilizes a CRISPR RNA (crRNA) to target RNA sequences and trigger a composite active site formed by two ‘Higher Eukaryotes and Prokaryotes Nucleotide’ (HEPN) domains, cleaving any solvent-exposed RNA. In this system, an intriguing form of allosteric communication controls the RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory to decipher this intricate activation mechanism. We show that the binding of a target RNA acts as an allosteric effector, by amplifying the communication signals over the dynamical noise through interactions of the crRNA at the buried HEPN1-2 interface. By introducing a novel Signal-to-Noise Ratio (SNR) of communication efficiency, we reveal critical allosteric residues—R377, N378, and R973—that rearrange their interactions upon target RNA binding. Alanine mutation of these residues is shown to select target RNA over an extended complementary sequence beyond guide-target duplex for RNA cleavage, establishing the functional significance of these hotspots. Collectively our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of highly selective RNA-based cleavage and detection tools. 

Abstract An increasingly pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target–RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and Cas13a variants to use in future easier-to-implement Cas13-based RNA detection applications. 

Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.