Dimensionality of Carbon Nanomaterials Determines the Binding and Dynamics of Amyloidogenic Peptides: Multiscale Theoretical Simulations

Experimental studies have demonstrated that nanoparticles can affect the rate of protein self-assembly, possibly interfering with the development of protein misfolding diseases such as Alzheimer's, Parkinson's and prion disease caused by aggregation and fibril formation of amyloid-prone proteins. We employ classical molecular dynamics simulations and large-scale density functional theory calculations to investigate the effects of nanomaterials on the structure, dynamics and binding of an amyloidogenic peptide apoC-II(60-70). We show that the binding affinity of this peptide to carbonaceous nanomaterials such as C60, nanotubes and graphene decreases with increasing nanoparticle curvature. Strong binding is facilitated by the large contact area available for π-stacking between the aromatic residues of the peptide and the extended surfaces of graphene and the nanotube. The highly curved fullerene surface exhibits reduced efficiency for π-stacking but promotes increased peptide dynamics. We postulate that the increase in conformational dynamics of the amyloid peptide can be unfavorable for the formation of fibril competent structures. In contrast, extended fibril forming peptide conformations are promoted by the nanotube and graphene surfaces which can provide a template for fibril-growth

Interplay of sequence, conformation, and binding at the peptide-titania interface as mediated by water

The initial stages of the adsorption of a hexapeptide at the aqueous titania interface are modeled using atomistic molecular dynamics simulations. This hexapeptide has been identified by experiment [Sano, K. L; Shiba, K.J. Am. Chem. Soc. 2003, 125. 14234] to bind to Ti particles. we explore the current hypothesis presented by these authors that binding at this peptide-titania interface is the result of electrostatic interactions and rind that contact with the surface appears to take place via a pair of oppositely charged groups in the peptide. Our data indicate that the peptide may initially recognize the water layers at the interface, not the titania surface itself, via these charged groups. We also report results of simulations for hexapeptide sequences with selected single-point mutations for alanine and compare these behaviors with those suggested from observed binding affinities from existing alanine scan experiments. Our results indicate that factors in addition to electrostatics also contribute, with the structural rigidity conferred by proline suggested to play a significant role. Finally, our findings suggest that intrapeptide interaction may provide mechanisms for surface detachment that could be detrimental to binding at the interface

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery