Modelling peptide adsorption energies on gold surfaces with an effective implicit solvent and surface model

Probing Solid-Binding Peptide Self-Assembly Kinetics Using a Frequency Response Cooperativity Model

Biomolecular adsorption has great significance in medical, environmental, and technological processes. Understanding adsorption equilibrium and binding kinetics is essential for advanced process implementation. This requires identifying intrinsic determinants that predict optimal adsorption properties at bio-hybrid interfaces. Solid-binding peptides (SBPs) have targetable intrinsic properties involving peptide-peptide and peptide-solid interactions, which result in high-affinity material-selective binding. Atomic force microscopy investigations confirmed this complex interplay of multi-step peptide assemblies in a cooperative modus. Yet, most studies report adsorption properties of SBPs using non-cooperative or single-step adsorption models. Using non-cooperative kinetic models for predicting cooperative self-assembly behavior creates an oversimplified view of peptide adsorption, restricting implementing SBPs beyond their current use. To address these limitations and provide insight into surface-level events during self-assembly, a novel method, the Frequency Response Cooperativity model, was developed. This model iteratively fits adsorption data through spectral analysis of several time-dependent kinetic parameters. The model, applied to a widely used gold-binding peptide data obtained using a quartz crystal microbalance with dissipation, verified multi-step assembly. Peak deconvolution of spectral plots revealed distinct differences in the size and distribution of the kinetic rates present during adsorption across the concentrations. This approach provides new fundamental insights into the intricate dynamics of self-assembly of biomolecules on surfaces.

On the interactions of peptides with gold nanoparticles: effects of sequence and size

Peptide-based self-assembly and synthesis techniques have emerged as a viable approach to designing active and stable inorganic nanostructures in aqueous media. In the present study, we use all-atom molecular dynamic (MD) simulations to study the interactions of ten short peptides (namely A3, AgBP1, AgBP2, AuBP1, AuBP2, GBP1, Midas2, Pd4, Z1, and Z2) with different gold nanoparticles (of different diameters ranging from 2 to 8 nm). Our MD simulation results imply that the gold nanoparticles have a remarkable effect on the stability and conformational properties of peptides. Moreover, the size of the gold nanoparticles and the type of peptide amino acid sequences play important roles in the stability of the peptide-AuNP complexes. Our results reveal that some amino acids such as Tyr, Phe, Met, Lys, Arg, and Gln have direct contact with the metal surface in comparison with Gly, Ala, Pro, Thr, and Val residues. The peptide adsorption on the surface of the gold nanoparticles is favorable from the energetic viewpoint, in which the van der Waals (vdW) interactions between the peptides and metal surface can be considered as one of the driving forces for the complexation process. The calculated Gibbs binding energies indicate that AuNPs have more sensitivity against the GBP1 peptide in the presence of different peptides. Overall, the results of this study can provide new insight into the peptide interaction with the gold nanoparticles from the molecular viewpoint, which can be important for designing new biomaterials based on the peptides and gold nanoparticles.Communicated by Ramaswamy H. Sarma.

Peptide adsorption on silica surfaces: Simulation and experimental insights

The understanding of interactions between proteins with silica surface is crucial for a wide range of different applications: from medical devices, drug delivery and bioelectronics to biotechnology and downstream processing. We show the application of EISM (Effective Implicit Surface Model) for discovering the set of peptide interactions with silica surface. The EISM is employed for a high-speed computational screening of peptides to model the binding affinity of small peptides to silica surfaces. The simulations are complemented with experimental data of peptides with silica nanoparticles from microscale thermophoresis and from infrared spectroscopy. The experimental work shows excellent agreement with computational results and verifies the EISM model for the prediction of peptide-surface interactions. 57 peptides, with amino acids favorable for adsorption on Silica surface, are screened by EISM model for obtaining results, which are worth to be considered as a guidance for future experimental and theoretical works. This model can be used as a broad platform for multiple challenges at surfaces which can be applied for multiple surfaces and biomolecules beyond silica and peptides.