A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

Tufféry, Pierre; Derreumaux, Philippe

doi:10.3389/fbinf.2023.1113928

Cited by 5 publications

(7 citation statements)

References 45 publications

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“…Note that since charges are associated with particles of heterogeneous sizes, we have considered shifting the energy curve to have energy values compatible with those of the Mie formulation. More details about practical implementation and parameter setup can be found in ( 38 ).…”

Section: Methodsmentioning

confidence: 99%

“…The default pKa values of 3.6, 4.2, 6, 10.5 and 12.5 for ASP, GLU, HIS, LYS and ARG residues can be superseded in a specific manner for each residue in a sequence. This provides a mean to take into account p K a variations due to amino acid local environment and conformations, if necessary using on-line servers (see ( 38 ).)…”

Section: Web Server Implementationmentioning

confidence: 99%

“…Next, we compared the predictions of PEP-FOLD4, TrRosetta and AlphaFold2 on (i) 15 peptides between 8 and 35 amino acids and pH from 4.3 to 7 for which a PDB structure is present and (ii) 4 peptides between 11 and 38 amino acids without nuclear magnetic resonance (NMR) structures, but with a topological description in literature. The three methods performed similarly on a total 17 peptides, but TrRosetta and AlphaFold2 failed on two peptides of 10 and 17 amino acids which are described as β-hairpins experimentally( 38 ).…”

Section: Use-casesmentioning

confidence: 99%

“…We recently compared the performances of PEP-FOLD4, TrRosetta and AlphaFold2 on six poly-charged peptides, (EK)15, (EK)5, (H)30, (K)15, (E)15 and (R)25 at pH varying between 3 and 13 ( 38 ), and at a salt concentration of 1 mM. The presence of α-helix in these peptides is pH-dependent, which cannot be captured by pH-independent methods such as TrRosetta and AlphaFold2.…”

Section: Use-casesmentioning

confidence: 99%

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PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution

Rey

Murail

Vries

et al. 2023

Nucleic Acids Research

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Accurate and fast structure prediction of peptides of less 40 amino acids in aqueous solution has many biological applications, but their conformations are pH- and salt concentration-dependent. In this work, we present PEP-FOLD4 which goes one step beyond many machine-learning approaches, such as AlphaFold2, TrRosetta and RaptorX. Adding the Debye-Hueckel formalism for charged-charged side chain interactions to a Mie formalism for all intramolecular (backbone and side chain) interactions, PEP-FOLD4, based on a coarse-grained representation of the peptides, performs as well as machine-learning methods on well-structured peptides, but displays significant improvements for poly-charged peptides. PEP-FOLD4 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4. This server is free and there is no login requirement.

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Section: Methodsmentioning

confidence: 99%

Section: Web Server Implementationmentioning

confidence: 99%

Section: Use-casesmentioning

confidence: 99%

Section: Use-casesmentioning

confidence: 99%

See 2 more Smart Citations

PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution

Rey

Murail

Vries

et al. 2023

Nucleic Acids Research

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show abstract

“…The structure of the peptide was built using the PEP-FOLD4 server (Rey et al, 2023;Tufféry and Derreumaux, 2023), which has the advantage over other available servers that it embeds a Debye-Hückel formalism to treat pH conditions and salt concentration variations. Peptide conformations are pH-and salt concentrationdependent, and thus, two systems were considered to mimic neutral and acidic conditions.…”

Section: Peptide Modelingmentioning

confidence: 99%

pH-dependent interactions of coacervate-forming histidine-rich peptide with model lipid membranes

Gudlur,

Ferreira,

Ting

et al. 2024

Front. Soft Matter

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Peptide-based liquid droplets (coacervates) produced by spontaneous liquid-liquid phase separation (LLPS), have emerged as a promising class of drug delivery systems due to their high entrapping efficiency and the simplicity of their formulation. However, the detailed mechanisms governing their interaction with cell membranes and cellular uptake remain poorly understood. In this study, we investigated the interactions of peptide coacervates composed of HBpep—peptide derived from the histidine-rich beak proteins (HBPs) of the Humboldt squid—with model cellular membranes in the form of supported lipid bilayers (SLBs). We employed quartz crystal microbalance with dissipation monitoring (QCM-D), neutron reflectometry (NR) and atomistic molecular dynamics (MD) simulations to reveal the nature of these interactions in the absence of fluorescent labels or tags. HBpep forms small oligomers at pH 6 whereas it forms µm-sized coacervates at physiological pH. Our findings reveal that both HBpep oligomers and HBpep-coacervates adsorb onto SLBs at pH 6 and 7.4, respectively. At pH 6, when the peptide carries a net positive charge, HBpep oligomers insert into the SLB, facilitated by the peptide’s interactions with the charged lipids and cholesterol. Importantly, however, HBpep coacervate adsorption at physiological pH, when it is largely uncharged, is fully reversible, suggesting no significant lipid bilayer rearrangement. HBpep coacervates, previously identified as efficient drug delivery vehicles, do not interact with the lipid membrane in the same manner as traditional cationic drug delivery systems or cell-penetrating peptides. Based on our findings, HBpep coacervates at physiological pH cannot cross the cell membrane by a simple passive mechanism and are thus likely to adopt a non-canonical cell entry pathway.

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Total Synthesis of Cyclosenegalin A

Aloanis,

Herlina,

Hardianto

et al. 2024

ChemistryOpen

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A Pro‐Gly‐typed cyclopeptide, cyclosenegalin A, was firstly isolated from Annona senegalensis and Annona scleroderma. In this study, we reported synthesis the cycloheptapeptide with a combination of solid‐ and solution‐phase synthetic methods. This study also compared the effectiveness of β‐turn inducer type I and II in cyclosenegalin A to facilitate the cyclization process. The synthesis of cyclosenegalin A were prepared using two different sequences of linear peptides for cyclization. First sequence employed β‐turn type I inducer (Ser‐Ala‐Val‐Thr) as turning point and second sequence employed β‐turn type II inducer (Thr‐Pro‐Gly‐Leu). The successful cyclization was obtained using the linear sequence of NH2‐Ala‐Val‐Thr‐Pro‐Gly‐Leu‐Ser‐OH with β‐turn type II. The final product was obtained in 8.2 % yield with PyBOP/DIEA act as coupling agent. The synthetic cyclosenegalin A were characterized with HR‐ToFMS, 1H NMR, 13C NMR, HSQC, HMBC, TOCSY, and ROESY. The synthetic product was also evaluated for its antimicrobial activity.

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A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

Cited by 5 publications

References 45 publications

PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution

PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution

pH-dependent interactions of coacervate-forming histidine-rich peptide with model lipid membranes

Total Synthesis of Cyclosenegalin A

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