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

Julien ReySamuel MurailSjoerd de VriesPhilippe DerreumauxPierre Tuffery
Published in: Nucleic acids research (2023)
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.
Keyphrases
  • aqueous solution
  • amino acid
  • machine learning
  • molecular dynamics
  • healthcare
  • high resolution
  • artificial intelligence
  • big data
  • mass spectrometry
  • health insurance
  • small molecule
  • protein protein