Accéder au contenu
Merck

Sequence-dependent interaction of β-peptides with membranes.

The journal of physical chemistry. B (2010-10-05)
Jagannath Mondal, Xiao Zhu, Qiang Cui, Arun Yethiraj
RÉSUMÉ

Recent experimental studies have revealed interesting sequence dependence in the antimicrobial activity of β-peptides, which suggests the possibility of a rational design of new antimicrobial agents. To obtain insight into the mechanism of membrane activity, we present a computer simulation study of the adsorption of these molecules to a single-component lipid membrane. Two classes of molecules are investigated: 10-residue oligomers of 14-helical sequences, and four sequences of random copolymeric β-peptides. The oligomers of interest are globally amphiphilic (GA) and nonglobally amphiphilic (non-GA) sequences of 10-residue, 14-helical sequences. In solution and at the interface, all oligomers maintain a helical structure throughout the simulation. The penetration of the molecules into the membrane and the orientation of the molecules at the interface depend strongly on the sequence. We attribute this to the propensity of the β-phenylalanine (βF) residues for membrane penetration. For the four sequences of random copolymeric β-peptides, simulations of an implicit solvent and membrane model show that the strength of adsorption of the polymers is strongly correlated with their efficiency to segregate the hydrophobic and cationic residues. The simulations suggest simple strategies for the design of candidates for antimicrobial β-peptides. Collectively, these results further support the conclusion from several recent studies that neither global amphiphilicity nor regular secondary structure is required for short peptides to effectively interact with the membrane. Moreover, although we study only the binding process, the fact that there is a correlation between the sequence dependence in the calculated binding properties and the experimentally observed antimicrobial activity suggests that efficient binding to the membrane might be a good predictor for high antimicrobial activity.