Unraveling the Reaction Mechanism of Enzymatic C5-Cytosine Methylation of DNA. A Combined Molecular Dynamics and QM/MM Study of Wild Type and Gln119 Variant

Base Dynamics in the HhaI Protein Binding Site

Protein-DNA interactions play an important role in numerous biological functions within the living cell. In many of these interactions, the DNA helix is significantly distorted upon protein-DNA complex formation. The HhaI restriction-modification system is one such system, where the methylation target is flipped out of the helix when bound to the methyltransferase. However, the base flipping mechanism is not well understood. The dynamics of the binding site of the HhaI methyltransferase and endonuclease (underlined) within the DNA oligomer [d(G1A2T3A4G5C6G7C8T9A10T11C12)]2 are studied using deuterium solid-state NMR (SSNMR). SSNMR spectra obtained from DNAs deuterated on the base of nucleotides within and flanking the [5'-GCGC-3']2 sequence indicate that all of these positions are structurally flexible. Previously, conformational flexibility within the phosphodiester backbone and furanose ring within the target sequence has been observed and hypothesized to play a role in the distortion mechanism. However, whether that distortion was occurring through an active or passive mechanism remained unclear. These NMR data demonstrate that although the [5'-GCGC-3']2 sequence is dynamic, the target cytosine is not passively flipping out of the double-helix on the millisecond-picosecond time scale. Additionally, although previous studies have shown that both the furanose ring and phosphodiester backbone experience a change in dynamics upon methylation, which may play a role in recognition and cleavage by the endonuclease, our observations here indicate that methylation has no effect on the dynamics of the base itself.

Insights into the Inhibitory Mechanisms of the Covalent Drugs for DNMT3A

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebularine (ZEB) inhibit the DNMT family with no specificities, and consequently would bring side effects during the treatment. Therefore, it is vital to understand the inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of the AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed how the AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a methyl-transferred intermediate (IM3). The IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

Unveiling the Methyl Transfer Mechanisms in the Epigenetic Machinery DNMT3A-3L: A Comprehensive Study Integrating Assembly Dynamics with Catalytic Reactions

In epigenetic mechanisms, DNA methyltransferase 3 alpha (DNMT3A) acts as an initiator for DNA methylation and prevents the downstream genes from expressing. Perturbations of DNMT3A functions may cause uncontrolled gene expression, resulting in pathogenic consequences such as cancers. It is, therefore, vitally important to understand the catalytic process of DNMT3A in its biological macromolecule assembly, viz., heterotetramer: (DNMT3A-3 L)^dimer. In this study, we utilized molecular dynamics (MD) simulations, Markov State Models (MSM), and quantum mechanics/molecular mechanics simulations (QM/MM) to investigate the de novo methyl transfer process. We identified the dynamics of the key residues relevant to the insertion of the target cytosine (dC) into the catalytic domain of DNMT3A, and the detailed potential energy surface of the seven-step reaction referring to methyl transfer. Our calculated potential energy barrier (22.51 kcal/mol) approximates the former experimental data (23.12 kcal/mol). The conformational change of the 5-methyl-cytosine (5mC) intermediate was found necessary in forming a four-water chain for the elimination step, which is unique to the other DNMTs. The biological assembly facilitates the creation of such a water chain, and the elimination occurs in an asynchronized mechanism in the two catalytic pockets. We anticipate the findings can enable a better understanding of the general mechanisms of the de novo methyl transfer for fulfilling the key enzymatic functions in epigenetics. And the unique elimination of DNMT3A might ignite novel methods for designing anti-cancer and tumor inhibitors of DNMTs.

Modeling Ensembles of Enzyme Reaction Pathways with Hi-MSM Reveals the Importance of Accounting for Pathway Diversity

Conventional quantum mechanical-molecular mechanics (QM/MM) simulation approaches for modeling enzyme reactions often assume that there is one dominant reaction pathway and that this pathway can be sampled starting from an X-ray structure of the enzyme. These assumptions reduce computational cost; however, their validity has not been extensively tested. This is due in part to the lack of a rigorous formalism for integrating disparate pathway information from dynamical QM/MM calculations. Here, we present a way to model ensembles of reaction pathways efficiently using a divide-and-conquer strategy through Hierarchical Markov State Modeling (Hi-MSM). This approach allows information on multiple, distinct pathways to be incorporated into a chemical kinetic model, and it allows us to test these two assumptions. Applying Hi-MSM to the reaction carried out by dihydrofolate reductase (DHFR) we find (i) there are multiple, distinct pathways significantly contributing to the overall flux of the reaction that the conventional approach does not identify and (ii) that the conventional approach does not identify the dominant reaction pathway. Thus, both assumptions underpinning the conventional approach are violated. Since DHFR is a relatively small enzyme, and configuration space scales exponentially with protein size, accounting for multiple reaction pathways is likely to be necessary for most enzymes.

Computational Insights in DNA Methylation: Catalytic and Mechanistic Elucidations for Forming 3-Methyl Cytosine

Methylation at C5 position of cytosine (5 mC) is the most abundantly occurring methylation process at CpG island, which has been well known as an epigenetic modification linked to many human diseases. Recently, another methylation approach has been discovered to show that DNA methyltransferases (DNMTs) promote the addition of the methyl group at position 3 to yield 3 mC. The existence of 3 mC can cause severe damages to the DNA strand, such as blocking its replication, repair, and transcription, affecting its stability, and initiating a double-strand DNA break. To gain a deeper insight into the formation of 3 mC, we have performed density functional theory (DFT) modeling studies at different levels of theory to clearly map out the mechanistic details for this new methylation approach. Our computed results are in harmony with pertinent experimental observations and shed light on a crucial off-target activity of DNMTs.

Using Theory To Extend the Scope of Azobenzene Drugs in Chemotherapy: Novel Combinations for a Specific Delivery

Gut microorganisms metabolize azobenzene compounds (Ph¹ -N=N-Ph² ) into free aniline products (Ph¹ -NH₂ +H₂ N-Ph² ), a process that has been largely investigated to reduce dyes residues in the textile industry. However, the action of bacterial core enzymes such as azoreductases (AzoR) might also help to deliver prodrugs that become active when they reach the colonic region, a mechanism with potential applications for the treatment of inflammatory bowel disease (IBD) and colorectal cancer. So far, three azo-bonded prodrugs of 5-aminosalicylic acid (5-ASA), for example, sulfasalazine, olsalazine and balsalazide, have been used for colon-targeted delivery. The present contribution describes the first rational design of a novel azobenzene prodrug thanks to a computational approach, with a focus on linking 5-ASA to another approved anti-inflammatory drug. The resulting prodrugs were assessed for their degradation upon AzoR action. Replacing the original carriers by irsogladine is found to improve action.

Towards a Better Understanding of Computational Models for Predicting DNA Methylation Effects at the Molecular Level

Human DNA is a very sensitive macromolecule and slight changes in the structure of DNA can have disastrous effects on the organism. When nucleotides are modified, or changed, the resulting DNA sequence can lose its information, if it is part of a gene, or it can become a problem for replication and repair. Human cells can regulate themselves by using a process known as DNA methylation. This methylation is vitally important in cell differentiation and expression of genes. When the methylation is uncontrolled, however, or does not occur in the right place, serious pathophysiological consequences may result. Excess methylation causes changes in the conformation of the DNA double helix. The secondary structure of DNA is highly dependent upon the sequence. Therefore, if the sequence changes slightly the secondary structure can change as well. These slight changes will then cause the doublestranded DNA to be more open and available in some places where large adductions can come in and react with the DNA base pairs. Computer models have been used to simulate a variety of biological processes including protein function and binding, and there is a growing body of evidence that in silico methods can shed light on DNA methylation. Understanding the anomeric effect that contributes to the structural and conformational flexibility of furanose rings through a combination of quantum mechanical and experimental studies is critical for successful molecular dynamic simulations.

Molecular Basis of SARS-CoV-2 Infection and Rational Design of Potential Antiviral Agents: Modeling and Simulation Approaches

The emergence in late 2019 of the coronavirus SARS-CoV-2 has resulted in the breakthrough of the COVID-19 pandemic that is presently affecting a growing number of countries. The development of the pandemic has also prompted an unprecedented effort of the scientific community to understand the molecular bases of the virus infection and to propose rational drug design strategies able to alleviate the serious COVID-19 morbidity. In this context, a strong synergy between the structural biophysics and molecular modeling and simulation communities has emerged, resolving at the atomistic level the crucial protein apparatus of the virus and revealing the dynamic aspects of key viral processes. In this Review, we focus on how in silico studies have contributed to the understanding of the SARS-CoV-2 infection mechanism and the proposal of novel and original agents to inhibit the viral key functioning. This Review deals with the SARS-CoV-2 spike protein, including the mode of action that this structural protein uses to entry human cells, as well as with nonstructural viral proteins, focusing the attention on the most studied proteases and also proposing alternative mechanisms involving some of its domains, such as the SARS unique domain. We demonstrate that molecular modeling and simulation represent an effective approach to gather information on key biological processes and thus guide rational molecular design strategies.

Computational Protocol to Understand P450 Mechanisms and Design of Efficient and Selective Biocatalysts

Cytochrome P450 enzymes have gained significant interest as selective oxidants in late-stage chemical synthesis. Their broad substrate scope enables them to be good candidates for their use in non-natural reactivity. Directed evolution evolves new enzyme biocatalysts that promote alternative reactivity for chemical synthesis. While directed evolution has proven useful in developing biocatalysts for specific purposes, this process is very time and labor intensive, and therefore not easily repurposed. Computational analysis of these P450 enzymes provides great insights into the broad substrate scope, the variety of reactions catalyzed, the binding specificity and the study of novel biosynthetic reaction mechanisms. By discovering new P450s and studying their reactivities, we uncover new insights into how this reactivity can be harnessed. We discuss a standard protocol using both DFT calculations and MD simulations to study a variety of cytochrome P450 enzymes. The approach entails theozyme models to study the mechanism and transition states via DFT calculations and subsequent MD simulations to understand the conformational poses and binding mechanisms within the enzyme. We discuss a few examples done in collaboration with the Tang and Sherman/Montgomery groups toward elucidating enzyme mechanisms and rationally designing new enzyme mutants as tools for selective C-H functionalization methods.

Understanding the R882H mutation effects of DNA methyltransferase DNMT3A: a combination of molecular dynamics simulations and QM/MM calculations

The AML-related high-frequent R882H mutation of DNA (cytosine-5)-methyltransferase 3A (DNMT3A), a key enzyme forde novoepigenetic methylation in human beings, was characterized by a disturbing conformation ofS-adenosylmethionine (SAM).

Maintenance DNA Methyltransferase Activity in the Presence of Oxidized Forms of 5-Methylcytosine: Structural Basis for Ten Eleven Translocation-Mediated DNA Demethylation

A precise balance of DNA methylation and demethylation is required for epigenetic control of cell identity, development, and growth. DNA methylation marks are introduced by de novo DNA methyltransferases DNMT3a/b and are maintained throughout cell divisions by DNA methyltransferase 1 (DNMT1), which adds methyl groups to hemimethylated CpG dinucleotides generated during DNA replication. Ten eleven translocation (TET) dioxygenases oxidize 5-methylcytosine (mC) to 5-hydroxymethylcytosine (hmC), 5-formylcytosine (fC), and 5-carboxylcytosine (caC), a process known to induce DNA demethylation and gene reactivation. In this study, we investigated the catalytic activity of human DNMT1 in the presence of oxidized forms of mC. A mass spectrometry-based assay was employed to study the kinetics of DNMT1-mediated cytosine methylation in CG dinucleotides containing C, mC, hmC, fC, or caC across from the target cytosine. Homology modeling, coupled with molecular dynamics simulations, was used to explore the structural consequences of mC oxidation with regard to the geometry of protein-DNA complexes. The DNMT1 enzymatic activity was strongly affected by the oxidation status of mC, with the catalytic efficiency decreasing in the following order: mC > hmC > fC > caC. Molecular dynamics simulations revealed that DNMT1 forms an unproductive complex with DNA duplexes containing oxidized forms of mC as a consequence of altered interactions of the target recognition domain of the protein with the C-5 substituent on cytosine. Our results provide new structural and mechanistic insight into TET-mediated DNA demethylation.

Inhibition studies of DNA methyltransferases by maleimide derivatives of RG108 as non-nucleoside inhibitors

AIM

DNA methyltransferases (DNMTs) are important drug targets for epigenetic therapy of cancer. Nowadays, non-nucleoside DNMT inhibitors are in development to address high toxicity of nucleoside analogs. However, these compounds still have low activity in cancer cells and mode of action of these compounds remains unclear.

MATERIALS & METHODS

In this work, we studied maleimide derivatives of RG108 by biochemical, structural and computational approaches to highlight their inhibition mechanism on DNMTs.

RESULTS

Findings demonstrated a correlation between cytotoxicity on mesothelioma cells of these compounds and their inhibitory potency against DNMTs. Noncovalent and covalent docking studies, supported by crystallographic (apo structure of M.HhaI) and differential scanning fluorimetry assays, provided detailed insights into their mode of action and revealed essential residues for the stabilization of such compounds inside DNMTs. [Formula: see text].

Free energy profiles for two ubiquitous damaging agents: methylation and hydroxylation of guanine in B-DNA

DNA methylation and hydroxylation are two ubiquitous reactions in DNA damage induction, yet insights are scarce concerning the free energy of activation within B-DNA. We resort to multiscale simulations to investigate the attack of a hydroxyl radical and of the primary diazonium onto a guanine embedded in a solvated dodecamer. Reaction free energy profiles characterize two strongly exergonic processes, yet allow unprecedented quantification of the barrier towards this damage reaction, not higher than 6 kcal mol^-1 and sometimes inexistent, and of the exergonicities. In the case of the [G(C8)-OH]˙ intermediate, we challenge the functional dependence of such simulations: recently-proposed functionals, such as M06-2X and LC-BLYP, agree on a ∼4 kcal mol^-1 barrier, whereas the hybrid GGA B3LYP functional predicts a barrier-less pathway. In the long term, multiscale approaches can help build up a unified panorama of DNA lesion induction. These results stress the importance of DFT/MM-MD simulations involving new functionals towards the sound modelling of biomolecule damage even in the ground state.

Tryptophan lyase (NosL): mechanistic insights into amine dehydrogenation and carboxyl fragment migration by QM/MM calculations

QM/MM calculations suggest two feasible pathways for the breaking of the C–C bond of the substrate. The breaking of the Cα–Cβ bond leads to the final product, whereas the cleavage of the Cα–C bond will terminate in the EPR-trapped radical intermediate.

Computational tools for the evaluation of laboratory-engineered biocatalysts

Biocatalysis is based on the application of natural catalysts for new purposes, for which enzymes were not designed. Although the first examples of biocatalysis were reported more than a century ago, biocatalysis was revolutionized after the discovery of an in vitro version of Darwinian evolution called Directed Evolution (DE). Despite the recent advances in the field, major challenges remain to be addressed. Currently, the best experimental approach consists of creating multiple mutations simultaneously while limiting the choices using statistical methods. Still, tens of thousands of variants need to be tested experimentally, and little information is available on how these mutations lead to enhanced enzyme proficiency. This review aims to provide a brief description of the available computational techniques to unveil the molecular basis of improved catalysis achieved by DE. An overview of the strengths and weaknesses of current computational strategies is explored with some recent representative examples. The understanding of how this powerful technique is able to obtain highly active variants is important for the future development of more robust computational methods to predict amino-acid changes needed for activity.