A hierarchical coarse-grained (all-atom-to-all-residue) computer simulation approach: self-assembly of peptides

Guiding principles for peptide nanotechnology through directed discovery

Life's diverse molecular functions are largely based on only a small number of highly conserved building blocks - the twenty canonical amino acids. These building blocks are chemically simple, but when they are organized in three-dimensional structures of tremendous complexity, new properties emerge. This review explores recent efforts in the directed discovery of functional nanoscale systems and materials based on these same amino acids, but that are not guided by copying or editing biological systems. The review summarises insights obtained using three complementary approaches of searching the sequence space to explore sequence-structure relationships for assembly, reactivity and complexation, namely: (i) strategic editing of short peptide sequences; (ii) computational approaches to predicting and comparing assembly behaviours; (iii) dynamic peptide libraries that explore the free energy landscape. These approaches give rise to guiding principles on controlling order/disorder, complexation and reactivity by peptide sequence design.

Pairwise amino acid secondary structural propensities

We investigate the propensities for amino acids to form a specific secondary structure when they are paired with other amino acids. Our investigations use molecular dynamics (MD) computer simulations, and we compare the results to those from the Protein Data Bank (PDB). Proper comparison requires weighting of the MD results in a manner consistent with the relative frequency of appearance in the PDB of each possible pair of amino acids. We find that the propensity for an amino acid to assume a secondary structure varies dramatically depending on the amino acid that is before or after it in the primary sequence. This cooperative effect means that when selecting amino acids to facilitate the formation of a secondary structure in peptide engineering experiments, the adjacent amino acids must be considered.

What makes a good graphene-binding peptide? Adsorption of amino acids and peptides at aqueous graphene interfaces

Molecular dynamics simulations of the aqueous biomolecule–graphene interface have predicted the free energy of adsorption of amino acids and the structure of peptides.

How does growth hormone releasing hexapeptide self-assemble in nanotubes?

Growth hormone releasing peptide, GHRP-6, a hexapeptide (His-(D-Trp)-Ala-Trp-(D-Phe)-Lys-NH2, MW = 872.44 Da) that belongs to a class of synthetic growth hormone secretagogues, can stimulate growth hormone secretion from somatotrophs in several species including humans. In the present study, we demonstrate that GHRP-6 dispersed in aqueous solution, at pH 7.0, room temperature of 22 °C, is able to form long nanotubes, which is evidenced by combining small angle X-ray scattering (SAXS), transmission electron microscopy and molecular dynamics simulation results. Such nanotubes possess inner and outer cross-sections equal to 6.7(2) nm and 13.4(5) nm, respectively. The mechanism of peptide self-assembly was determined by molecular dynamics simulations revealing that the peptides self-assemble like amphiphilic molecules in aqueous solution in a partially interdigitated structure. In this case, the position of the positively charged amino terminus is located at the peptide-water interface, whereas the neutral NH2-capped carboxy terminus remains buried at the hydrophobic core. In contrast, the long side chain of Lys-6 stretches out of the hydrophobic core positioning its positive charge near the cylinder surface. The peptide configuration in the nanotube wall comes from the interplay between the hydrophobic interactions of the aromatic side chains of GHRP-6 and the electrostatic repulsion of its cationic charges. On increasing the peptide concentration, the long nanotubes self-arrange in solution displaying a bi-dimensional hexagonal-like packing in the SAXS curves, with a center-to-center distance of ∼15 nm. Further, we also show that the nanostructure formed in solution is quite stable and is preserved following transfer to a solid support.

Conformational response to solvent interaction and temperature of a protein (Histone h3.1) by a multi-grained monte carlo simulation

Interaction with the solvent plays a critical role in modulating the structure and dynamics of a protein. Because of the heterogeneity of the interaction strength, it is difficult to identify multi-scale structural response. Using a coarse-grained Monte Carlo approach, we study the structure and dynamics of a protein (H3.1) in effective solvent media. The structural response is examined as a function of the solvent-residue interaction strength (based on hydropathy index) in a range of temperatures (spanning low to high) involving a knowledge-based (Miyazawa-Jernigan(MJ)) residue-residue interaction. The protein relaxes rapidly from an initial random configuration into a quasi-static structure at low temperatures while it continues to diffuse at high temperatures with fluctuating conformation. The radius of gyration (R_g) of the protein responds non-monotonically to solvent interaction, i.e., on increasing the residue-solvent interaction strength (f_s), the increase in R_g (f_s≤f_sc) is followed by decay (f_s≥f_sc) with a maximum at a characteristic value (f_sc) of the interaction. Raising the temperature leads to wider spread of the distribution of the radius of gyration with higher magnitude of f_sc. The effect of solvent on the multi-scale (λ: residue to R_g) structures of the protein is examined by analyzing the structure factor (S(q),|q| = 2π/λ is the wave vector of wavelength, λ) in detail. Random-coil to globular transition with temperature of unsolvated protein (H3.1) is dramatically altered by the solvent at low temperature while a systematic change in structure and scale is observed on increasing the temperature. The interaction energy profile of the residues is not sufficient to predict its mobility in the solvent. Fine-grain representation of protein with two-node and three-node residue enhances the structural resolution; results of the fine-grained simulations are consistent with the finding described above of the coarse-grained description with one-node residue.