Using one-step perturbation to predict the folding equilibrium of differently stereochemically substituted β-peptides

Literature Information

Publication Date 2010-10-29
DOI 10.1039/C0CP00833H
Impact Factor 3.676
Authors

Wilfred F. van Gunsteren


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Abstract

The one-step perturbation technique is used to predict the folding equilibria for 16 peptides with different stereochemical side-chain substitutions through one or two long-time simulations, one of an unphysical reference state and another of one of the 16 peptides for which many folding events can be sampled. The accuracy of the one-step perturbation results was investigated by comparing to results available from long-time MD simulations of particular peptides. Their folding free energies were reproduced within statistical accuracy. The one-step perturbation results show that an axial substitution at either the Cα or the Cβ position destabilizes the 314-helical conformation of the hepta-β-peptide, which is consistent with data inferred from experimental CD spectra. The methodology reduces the number of required separate simulations by an order of magnitude.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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