Energy landscape of a peptide consisting of α‐helix, 3₁₀‐helix, β‐turn, β‐hairpin, and other disordered conformations

Abstract

The energy landscape of a peptide [Ace-Lys-Gln-Cys-Arg-Glu-Arg-Ala-Nme] in explicit water was studied with a multicanonical molecular dynamics simulation, and the AMBER parm96 force field was used for the energy calculation. The peptide was taken from the recognition helix of the DNA-binding protein, c-Myb. A rugged energy landscape was obtained, in which the random-coil conformations were dominant at room temperature. The CD spectra of the synthesized peptide revealed that it is in the random state at room temperature. However, the 300 K canonical ensemble, Q(300K), contained ␣-helix, 3 10 -helix, ␤-turn, and ␤-hairpin structures with small but notable probabilities of existence. The complete ␣-helix, imperfect ␣-helix, and random-coil conformations were separated from one another in the conformational space. This means that the peptide must overcome energy barriers to form the ␣-helix. The overcoming process may correspond to the hydrogen-bond rearrangements from peptide-water to peptide-peptide interactions. The ␤-turn, imperfect 3 10 -helix, and ␤-hairpin structures, among which there are no energy barriers at 300 K, were embedded in the ensemble of the random-coil conformations. Two types of ␤-hairpin with different ␤-turn regions were observed in Q(300K). The two ␤-hairpin structures may have different mechanisms for the ␤-hairpin formation. The current study proposes a scheme that the random state of this peptide consists of both ordered and disordered conformations. In contrast, the energy landscape obtained from the parm94 force field was funnel like, in which the peptide formed the helical conformation at room temperature and random coil at high temperature.Keywords: Folding; rugged surface; funnel; ␤-hairpin; ␣-helix; random state; multicanonical; force fieldThe thermodynamic stability and the folding process of a polypeptide chain are determined by the energy landscape of the system. One picture for the landscape is a funnel-like surface, which has the advantage of quickly folding the chain into a unique tertiary structure. The other picture is a rugged surface, in which a number of energy local minima are widely spread, and the chain thermally fluctuates among the minima at a given temperature.The folding funnel has been studied with simplified models (Bryngelson et al. 1995;Chan and Dill 1998;Dill 1999;Istrail et al. 1999;Nakamura and Sasai 1999), in which the chain was designed to fold into a unique, stable structure at the ground state or at a low temperature. An advantage of the simplified model is that the thermodynamically important states can be counted relatively precisely, and thus the free energies of the states are evaluated. The simplified model is also used to specify the determinant factors to fold the chain into unique tertiary structures (Irback and Potthast

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

“…One of the biased conformations corresponds to the native. All atom MD simulations for short peptides extracted from proteins support this idea (26,27). In the FA method, it is assumed that such biased conformations of short segments can be approximated by fragment conformations retrieved from the structure database by sequence-comparison methods.…”

Comparison of multiple Amber force fields and development of improved protein backbone parameters

“…On the other hand, the changes introduced in the less frequently used ff96 were observed to overestimate β-strand propensity [14][15][16][17] . Because the backbone dihedral parameters are shared by all amino acids, regardless of the type of sidechain, their cumulative effect is most likely responsible for the bias towards the specific secondary structure.…”

β‐Hairpins, α‐helices, and the intermediates among the secondary structures in the energy landscape of a peptide from a distal β‐hairpin of SH3 domain

“…After sampling a wide energy range, the energy and conformational distributions can be obtained at an arbitral temperature in the energy range. [45][46][47][48][49][50] The canonical distribution P(E,T) is unknown a priori, but obtained through an iteration of the multicanonical MD runs. Prior to the multicanonical runs, a preparative conventional canonical MD simulation (prerun) is done at T 0 .…”

“…The method to derive a canonical energy distribution from the multicanonical flat energy distribution is given. [45][46][47][48][49][50] The iterative procedure is repeated until the sampling reaches the room-temperature region. Finally, the conformations sampled in the last multicanonical run (i.e., production run) are used for the analysis.…”

“…36,37 The multicanonical MD method can sample the conformational space widely without trapping into energy minima and obtain the energy landscape of peptides in explicit water. 49,50 The SH3 domain is a small mainly ␤-protein, 54 and the folding of this protein has been studied. [55][56][57][58][59][60][61][62][63][64][65][66][67] The work 59 -63, suggested that the distal ␤-hairpin may be ordered in the transition state ensemble.…”