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Randomizing of Oligopeptide Conformations by Nearest Neighbor Interactions between Amino Acid Residues
Journal article   Open access   Peer reviewed

Randomizing of Oligopeptide Conformations by Nearest Neighbor Interactions between Amino Acid Residues

Reinhard Schweitzer-Stenner, Bridget Milorey and Harald Schwalbe
Biomolecules (Basel, Switzerland), v 12(5), p684
11 May 2022
PMID: 35625612
url
https://doi.org/10.3390/biom12050684View
Published, Version of Record (VoR)CC BY V4.0 Open

Abstract

Amino Acids - chemistry Molecular Conformation Oligopeptides - chemistry Peptides - chemistry Proteins - chemistry
Flory's random coil model assumes that conformational fluctuations of amino acid residues in unfolded poly(oligo)peptides and proteins are uncorrelated (isolated pair hypothesis, IPH). This implies that conformational energies, entropies and solvation free energies are all additive. Nearly 25 years ago, analyses of coil libraries cast some doubt on this notion, in that they revealed that aromatic, but also β-branched side chains, could change the J(H H ) coupling of their neighbors. Since then, multiple bioinformatical, computational and experimental studies have revealed that conformational propensities of amino acids in unfolded peptides and proteins depend on their nearest neighbors. We used recently reported and newly obtained Ramachandran plots of tetra- and pentapeptides with non-terminal homo- and heterosequences of amino acid residues to quantitatively determine nearest neighbor coupling between them with a Ising type model. Results reveal that, depending on the choice of amino acid residue pairs, nearest neighbor interactions either stabilize or destabilize pairs of polyproline II and β-strand conformations. This leads to a redistribution of population between these conformations and a reduction in conformational entropy. Interactions between residues in polyproline II and turn(helix)-forming conformations seem to be cooperative in most cases, but the respective interaction parameters are subject to large statistical errors.

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Web of Science research areas
Biochemistry & Molecular Biology
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