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Association of helical beta-peptides and their aggregation behavior from the potential of mean force in explicit solvent.

ABSTRACT: Helical beta-peptides have been shown to fold into well-defined structures. In aqueous solution, some beta-peptides self-assemble into nanoscale fibers, aggregates, and liquid crystalline phases. Molecular simulations, at an atomistic level, are used to examine, in a systematic manner, the interactions between distinct beta-peptide molecules. The relationship between side-chain chemistry (and position along the backbone) and, in particular, aggregation behaviors, is assessed by calculating the potential of mean force or dimerization free energy of two peptides in explicit water. The free energy profiles as a function of separation for helical, amphiphilic beta-peptides are consistent with experimental observations, and help explain the origins of aggregate or fiber formation in solution. Close examination of the energetic and entropic contributions to the free energy reveals that, depending on the position of certain side groups along the molecule, the tendency of two peptides to aggregate can be driven by entropy or by energy, respectively. In contrast to findings from previous works that employed a coarse representation of the solvent, it is shown that water-peptide interactions play key roles in the association behavior of beta-peptides.

SUBMITTER: Miller CA

PROVIDER: S-EPMC2711497 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Association of helical beta-peptides and their aggregation behavior from the potential of mean force in explicit solvent.

Miller Clark A CA Gellman Samuel H SH Abbott Nicholas L NL de Pablo Juan J JJ

Biophysical journal 20090601 11

Helical beta-peptides have been shown to fold into well-defined structures. In aqueous solution, some beta-peptides self-assemble into nanoscale fibers, aggregates, and liquid crystalline phases. Molecular simulations, at an atomistic level, are used to examine, in a systematic manner, the interactions between distinct beta-peptide molecules. The relationship between side-chain chemistry (and position along the backbone) and, in particular, aggregation behaviors, is assessed by calculating the p ...[more]

PMID: 19486660

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Project description:BackgroundThe development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials.ResultsThe frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading.ConclusionThe knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

Dataset Information

Association of helical beta-peptides and their aggregation behavior from the potential of mean force in explicit solvent.

Publications

Association of helical beta-peptides and their aggregation behavior from the potential of mean force in explicit solvent.

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