Towards understanding structure-stability and surface properties of laminin peptide YIGSR and mutants

Properties of laminin peptide YIGSR and its mutated sequences YIGSD, YIGSS, YIGSN and YIGSQ have been investigated using molecular dynamics simulations (MDS) and Langmuir films at air/water interface. Simulation studies on laminin peptide YIGSR were performed in the isothermal-isobaric (N, P, T) ensemble, with run up to 5 ns in water as well as lipid environment at 298 K. From different initial configurations, shape transformations of the peptides on the timescale of nanoseconds were observed. The results showed YIGSR to be the most stable peptide with the order of minimized energy being YIGSR<YIGSQ<YIGSD<YIGSN<YIGSS. Subsequent experiments with newly synthesized amphiphilic derivatives of the mutated peptides were carried out for their monolayer formation and stability at air/water interface using surface pressure-molecular area (pi-A) and surface potential-molecular area (DeltaV-A) isotherms. The surface and interface activity of these compounds followed a similar trend as with the MDS studies. Results suggest that single amino acid mutation leads to large changes in minimized energy, surface activity and different rates to reach stable conformation. The native YIGSR is the most stable sequence with highest surface activity while YIGSS is least stable and has the lowest surface activity. This corroborated the results of the MDS.

Protein three-dimensional structure generation with an empirical hydrophobic penalty function

Given current computational environments, it is worthwhile to establish amino acid residue-level functions which approximate protein folds quite well. Such functions must be the interim steps toward protein three-dimensional structure prediction. I have shown that an empirical hydrophobic penalty function of protein, derived from the number of residues in a sphere around each residue, could be utilized to distinguish the correctly folded structure from the incorrect ones. In order to assess the predictive power of the penalty function, I have generated conformations by randomly changing main chain dihedral angles, and applied the penalty function to them. If only a local region was allowed to change its conformation, native-like structures could be generated within a reasonable computational time. In global simulations, however, a considerable number of nonnative conformations, which gave as small a penalty value as that of the native protein, were found. Although some of the conformations were compact and globular, they were quite different from the native structure in that they lacked most of the secondary structures. This result shows that the penalty function alone cannot define the native structure, and that substructure information may help the penalty function to reach the correctly folded structure.