Transmembrane Self-Assembled Cyclic Peptide Nanotubes Based on α-Residues and Cyclic δ-Amino Acids: A Computational Study
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Frontiers Media
Abstract
Self-assembling cyclic peptide nanotubes have been shown to function as synthetic, integral transmembrane channels. The combination of natural and nonnatural aminoacids in the sequence of cyclic peptides enables the control not only of their outer surface but also of the inner cavity behavior and properties, affecting, for instance, their permeability to different molecules including water and ions. Here, a thorough computational study on a new class of self-assembling peptide motifs, in which δ-aminocycloalkanecarboxylic acids are alternated with natural α-amino acids, is presented. The presence of synthetic δ-residues creates hydrophobic regions in these α,δ-SCPNs, which makes them especially attractive for their potential implementation in the design of new drug or diagnostic agent carrier systems. Using molecular dynamics simulations, the behavior of water molecules, different ions (Li+, Na+, K+, Cs+, and Ca2+), and their correspondent counter Cl− anions is extensively investigated in the nanoconfined environment. The structure and dynamics are mutually combined in a diving immersion inside these transmembrane channels to discover a fascinating submarine nanoworld where star-shaped water channels guide the passage of cations and anions therethrough.
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Blanco-González A, Calvelo M, Garrido PF, Amorín M, Granja JR, Piñeiro Á and Garcia-Fandino R (2021) Transmembrane Self-Assembled Cyclic Peptide Nanotubes Based on α-Residues and Cyclic δ-Amino Acids: A Computational Study. Front. Chem. 9:704160. doi: 10.3389/fchem.2021.704160
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https://doi.org/10.3389/fchem.2021.704160Sponsors
This work was supported by the Spanish Agencia Estatal de Investigación (AEI) and the ERDF (PID2019-111126RB-100, RTI2018-098795-A-I00, and PID2019-111327GB-I00) and by the Xunta de Galicia and the ERDF (ED431F 2020/05, ED431C 2017/25, and Centro singular de investigación de Galicia accreditation 2016-2019, ED431G/09). MC thanks Xunta de Galicia for a predoctoral fellowship (ED481A-2017/ 068). RG-F thanks Ministerio de Ciencia, Innovación y Universidades, for a “Ramón y Cajal” contract (RYC-2016-20335). PFG thanks the Spanish Ministry of Economy and Competitiveness and the European Social Fund for his predoctoral research grant, reference BES-2016-076761. All calculations were carried out at the Centro de Supercomputación de Galicia.
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