Small-Molecule Inhibitors of the m7G-RNA Writer METTL1.

We discovered the first inhibitors of the m7G-RNA writer METTL1 by high-throughput docking and an enzymatic assay based on luminescence. Eleven compounds, which belong to three different chemotypes, show inhibitory activity in the range 40-300 µM. Two adenine derivatives identified by docking have very favorable ligand efficiency of 0.34 and 0.31 kcal/mol per non-hydrogen atom, respectively. Molecular dynamics simulations provide evidence that the inhibitors compete with the binding of the cosubstrate S-adenosyl methionine to METTL1. We also present a soakable crystal form that was used to determine the structure of the complex of METTL1 with sinefungin at a resolution of 1.85 Å.

The catalytic mechanism of the RNA methyltransferase METTL3.

The complex of methyltransferase-like proteins 3 and 14 (METTL3-14) is the major enzyme that deposits N6-methyladenosine (m6A) modifications on messenger RNA (mRNA) in humans. METTL3-14 plays key roles in various biological processes through its methyltransferase (MTase) activity. However, little is known about its substrate recognition and methyl transfer mechanism from its cofactor and methyl donor S-adenosylmethionine (SAM). Here, we study the MTase mechanism of METTL3-14 by a combined experimental and multiscale simulation approach using bisubstrate analogues (BAs), conjugates of a SAM-like moiety connected to the N6-atom of adenosine. Molecular dynamics simulations based on crystal structures of METTL3-14 with BAs suggest that the Y406 side chain of METTL3 is involved in the recruitment of adenosine and release of m6A. A crystal structure with a BA representing the transition state of methyl transfer shows a direct involvement of the METTL3 side chains E481 and K513 in adenosine binding which is supported by mutational analysis. Quantum mechanics/molecular mechanics (QM/MM) free energy calculations indicate that methyl transfer occurs without prior deprotonation of adenosine-N6. Furthermore, the QM/MM calculations provide further support for the role of electrostatic contributions of E481 and K513 to catalysis. The multidisciplinary approach used here sheds light on the (co)substrate binding mechanism, catalytic step, and (co)product release, and suggests that the latter step is rate-limiting for METTL3. The atomistic information on the substrate binding and methyl transfer reaction of METTL3 can be useful for understanding the mechanisms of other RNA MTases and for the design of transition state analogues as their inhibitors.

Disrupting Dimeric ß-Amyloid by Electric Fields.

The early oligomers of the amyloid Aß peptide are implicated in Alzheimer's disease, but their transient nature complicates the characterization of their structure and toxicity. Here, we investigate the stability of the minimal toxic species, i.e., ß-amyloid dimers, in the presence of an oscillating electric field. We first use deep learning (AlphaFold-multimer) for generating initial models of Aß42 dimers. The flexibility and secondary structure content of the models are then analyzed by multiple runs of molecular dynamics (MD). Structurally stable models are similar to ensemble representatives from microsecond-long MD sampling. Finally, we employ the validated model as the starting structure of MD simulations in the presence of an external oscillating electric field and observe a fast decay of ß-sheet content at high field strengths. Control simulations using the helical dimer of the 42-residue leucine zipper peptide show higher structural stability than the Aß42 dimer. The simulation results provide evidence that an external electric field (oscillating at 1 GHz) can disrupt amyloid oligomers which should be further investigated by experiments with brain organoids in vitro and eventually in vivo.

The catalytic mechanism of the RNA methyltransferase METTL3.

The complex of methyltransferase-like proteins 3 and 14 (METTL3-14) is the major enzyme that deposits N6-methyladenosine (m6A) modifications on mRNA in humans. METTL3-14 plays key roles in various biological processes through its methyltransferase (MTase) activity. However, little is known about its substrate recognition and methyl transfer mechanism from its cofactor and methyl donor S-adenosylmethionine (SAM). Here, we study the MTase mechanism of METTL3-14 by a combined experimental and multiscale simulation approach using bisubstrate analogues (BAs), conjugates of a SAM-like moiety connected to the N6-atom of adenosine. Molecular dynamics simulations based on crystal structures of METTL3-14 with BAs suggest that the Y406 side chain of METTL3 is involved in the recruitment of adenosine and release of m6A. A crystal structure with a bisubstrate analogue representing the transition state of methyl transfer shows a direct involvement of the METTL3 side chains E481 and K513 in adenosine binding which is supported by mutational analysis. Quantum mechanics/molecular mechanics (QM/MM) free energy calculations indicate that methyl transfer occurs without prior deprotonation of adenosine-N6. Furthermore, the QM/MM calculations provide further support for the role of electrostatic contributions of E481 and K513 to catalysis. The multidisciplinary approach used here sheds light on the (co)substrate binding mechanism, catalytic step, and (co)product release catalysed by METTL3, and suggests that the latter step is rate-limiting. The atomistic information on the substrate binding and methyl transfer reaction of METTL3 can be useful for understanding the mechanisms of other RNA MTases and for the design of transition state analogues as their inhibitors.

Domino Effect in Allosteric Signaling of Peptide Binding.

While being a thoroughly studied model of dynamic allostery in a small protein, the pathway of signal transduction in the PDZ3 domain has not been fully determined. Here, we investigate peptide binding to the PDZ3 domain by conventional and fully data-driven analyses of molecular dynamics simulations. First, we identify isoleucine 37 as a key residue by widely used computational procedures such as cross-correlation and community network analysis. Simulations of the Ile37Ala mutant show disruption of the coordinated movements of spatially close regular elements of secondary structure. Then, we employ a recently developed unsupervised, data-driven procedure to determine an optimized reaction coordinate (slowest-relaxation eigenvector) of peptide binding. We use this reaction coordinate to improve sampling by restarting additional simulations from the transition state region. Significant differences in the distributions of some of the pairwise residue distances in the bound and unbound states emerge from the projection onto the optimized reaction coordinate. The unsupervised analysis shows that allosteric signaling is transduced from the ß2 strand, which forms part of the peptide binding site, to the spatially adjacent ß3 and ß4 strands, and from there to the α3 helix. The domino-like transmission of a (peptide binding) signal along ß strands and α helices that are close in three-dimensional space is likely to be a general mechanism of allostery in single-domain proteins.