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We present a new class of DNA‐based nanoswitches that, upon enzymatic repair, could undergo a conformational change mechanism leading to a change in fluorescent signal. Such folding‐upon‐repair DNA nanoswitches are synthetic DNA sequences containingO⁶‐methyl‐guanine (O⁶‐MeG) nucleobases and labelled with a fluorophore/quencher optical pair. The nanoswitches are rationally designed so that only upon enzymatic demethylation of theO⁶‐MeG nucleobases they can form stable intramolecular Hoogsteen interactions and fold into an optically active triplex DNA structure. We have first characterized the folding mechanism induced by the enzymatic repair activity through fluorescent experiments and Molecular Dynamics simulations. We then demonstrated that the folding‐upon‐repair DNA nanoswitches are suitable and specific substrates for different methyltransferase enzymes including the human homologue (hMGMT) and they allow the screening of novel potential methyltransferase inhibitors.

 

Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Here, we report on the critical role of molecular dynamics (MD) simulations in discovering the mechanism of allosteric communication within CRISPR‐Cas9, a leading genome editing machinery with enormous promises for medicine and biotechnology. MD revealed how allostery intervenes during at least three steps of the CRISPR‐Cas9 function: affecting DNA recognition, mediating the cleavage and interfering with the off‐target activity. An allosteric communication that activates concerted DNA cleavages was found to led through the L1/L2 loops, which connect the HNH and RuvC catalytic domains. The identification of these “allosteric transducers” inspired the development of novel variants of the Cas9 protein with improved specificity, opening a new avenue for controlling the CRISPR‐Cas9 activity. Discussed studies also highlight the critical role of the recognition lobe in the conformational activation of the catalytic HNH domain. Specifically, the REC3 region was found to modulate the dynamics of HNH by sensing the formation of the RNA:DNA hybrid. The role of REC3 was revealed to be particularly relevant in the presence of DNA mismatches. Indeed, interference of REC3 with the RNA:DNA hybrid containing mismatched pairs at specific positions resulted in locking HNH in an inactive “conformational checkpoint” conformation, thereby hampering off‐target cleavages. Overall, MD simulations established the fundamental mechanisms underlying the allosterism of CRISPR‐Cas9, aiding engineering strategies to develop new CRISPR‐Cas9 variants for improved genome editing.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

 

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., “Gaussian approximation”). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica‐exchange GaMD (rex‐GaMD) and replica‐exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new “selective GaMD” algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep‐GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small‐molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein–protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

Molecular and Statistical Mechanics > Free Energy Methods

 

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. 

Abstract Ab-initio molecular dynamics enables following the dynamics of biological systems from the first principles, describing the electronic structure and offering the opportunity to “watch” the evolution of biochemical processes with unique resolution, beyond the capabilities of state-of-the-art experimental techniques. This article reports the role of first-principles ( ab-initio ) molecular dynamics (MD) in the CRISPR-Cas9 genome editing revolution, achieving a profound understanding of the enzymatic function and offering valuable insights for enzyme engineering. We introduce the methodologies and explain the use of ab-initio MD simulations to establish the two-metal dependent mechanism of DNA cleavage in the RuvC domain of the Cas9 enzyme, and how a second catalytic domain, HNH, cleaves the target DNA with the aid of a single metal ion. A detailed description of how ab-initio MD is combined with free-energy methods—i.e., thermodynamic integration and metadynamics—to break and form chemical bonds is given, explaining the use of these methods to determine the chemical landscape and establish the catalytic mechanism in CRISPR-Cas9. The critical role of classical methods is also discussed, explaining theory and application of constant pH MD simulations, used to accurately predict the catalytic residues’ protonation states. Overall, first-principles methods are shown to unravel the electronic structure and reveal the catalytic mechanism of the Cas9 enzyme, providing valuable insights that can serve for the design of genome editing tools with improved catalytic efficiency or controllable activity. 

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At the core of the CRISPR-Cas9 genome-editing technology, the endonuclease Cas9 introduces site-specific breaks in DNA. Here, multi-microsecond molecular dynamics, free-energy and multiscale simulations are combined with solution NMR and DNA cleavage experiments to resolve the catalytic mechanism of target DNA cleavage. We show that the conformation of an active HNH nuclease is tightly dependent on the catalytic Mg2+, unveiling its cardinal structural role. Solution NMR, DNA cleavage assays and molecular simulations of the Mg2+-bound HNH convey on the formation of the active state and show that the protonation state of catalytic H840 is strongly affected by active site mutations. Finally, ab-initio QM(DFT)/MM simulations and metadynamics establish that DNA cleavage occurs through the identified active state, showing that the catalysis is activated by H840 and aided by K866, in line with DNA cleavage experiments. This information is critical to ameliorating Cas9 function, and helping the development of genome-editing tools.