Advancing Rational Control of Peptide-Surface Complexes

High-Throughput Computational Screening of Solid-Binding Peptides

Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening.

Size Matters: Free-Energy Calculations of Amino Acid Adsorption over Pristine Graphene

There is still growing interest in graphene interactions with proteins, both for its possible biological applications and due to concerns over detrimental effects at the cellular level. As with any process involving proteins, an understanding of amino acid composition is desirable. In this work, we systematically studied the adsorption process of amino acids onto pristine graphene via rigorous free-energy calculations. We characterized the free energy, potential energy, and entropy of the adsorption of all proteinogenic amino acids. The energetic components were further separated into pair interaction contributions. A linear correlation was found between the free energy and the solvent accessible surface area change during adsorption (ΔSASAads) over pristine graphene and uncharged amino acids. Free energies over pristine graphene were compared with adsorption onto graphene oxide, finding an almost complete loss of the favorability of amino acid adsorption onto graphene. Finally, the correlation with ΔSASAads was used to successfully predict the free energy of adsorption of several penta-l-peptides in different structural states and sequences. Due to the relative ease of calculating the ΔSASAads compared to free-energy calculations, it could prove to be a cost-effective predictor of the free energy of adsorption for proteins onto nonpolar surfaces.

Molecular Driving Force for Facet Selectivity of Sequence-Defined Amphiphilic Peptoids at Au-Water Interfaces

Shape-controlled colloidal nanocrystal syntheses often require facet-selective solution-phase chemical additives to regulate surface free energy, atom addition/migration fluxes, or particle attachment rates. Because of their highly tunable properties and robustness to a wide range of experimental conditions, peptoids represent a very promising class of next-generation functional additives for control over nanocrystal growth. However, understanding the origin of facet selectivity at the molecular level is critical to generalizing their design. Herein we employ molecular dynamics simulations and biased sampling methods and report stronger selectivity to Au(111) than to Au(100) for Nce3Ncp6, a peptoid that has been shown to assist the formation of 5-fold twinned Au nanostars. We find that facet selectivity is achieved through synergistic effects of both peptoid-surface and solvent-surface interactions. Moreover, the amphiphilic nature of Nce3Ncp6 together with the order of peptoid-peptoid and peptoid-surface binding energies, that is, peptoid-Au(100) < peptoid-peptoid < peptoid-Au(111), further amplifies its distinct collective behavior on different Au surfaces. Our studies provide a fundamental understanding of the molecular origin of facet-selective adsorption and highlight the possibility of future designs and uses of sequence-defined peptoids for predictive syntheses of nanocrystals with designed shapes and properties.

Self-Assembling Peptides and Carbon Nanomaterials Join Forces for Innovative Biomedical Applications

Self-assembling peptides and carbon nanomaterials have attracted great interest for their respective potential to bring innovation in the biomedical field. Combination of these two types of building blocks is not trivial in light of their very different physico-chemical properties, yet great progress has been made over the years at the interface between these two research areas. This concise review will analyze the latest developments at the forefront of research that combines self-assembling peptides with carbon nanostructures for biological use. Applications span from tissue regeneration, to biosensing and imaging, and bioelectronics.