Amino acid modifications for conformationally constraining naturally occurring and engineered peptide backbones: Insights from the Protein Data Bank

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

Extensive efforts invested in understanding the rules of protein folding are now being applied, with good effect, in de novo design of proteins/peptides. For proteins containing standard α-amino acids alone, knowledge derived from experimentally determined three-dimensional (3D) structures of proteins and biologically active peptides are available from the Protein Data Bank (PDB), and the Cambridge Structural Database (CSD). These help predict and design protein structures, with reasonable confidence. However, our knowledge of 3D structures of biomolecules containing backbone modified amino acids is still evolving. A major challenge in de novo protein/peptide design concerns the engineering of conformationally constrained molecules with specific structural elements and chemical groups appropriately positioned for biological activity. This review explores four classes of amino acid modifications that constrain protein/peptide backbone structure. Systematic analysis of peptidic molecule structures (eg, bioactive peptides, inhibitors, antibiotics, and designed molecules), containing these backbone-modified amino acids, found in the PDB and CSD are discussed. The review aims to provide structure–function insights that will guide future design of proteins/peptides.

Original languageEnglish (US)
Article numbere23230
JournalBiopolymers
Volume109
Issue number10
DOIs
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biochemistry
  • Biomaterials
  • Organic Chemistry

Keywords

  • backbone modified amino acids
  • molecular tools
  • peptide conformation
  • structural data archives

Fingerprint

Dive into the research topics of 'Amino acid modifications for conformationally constraining naturally occurring and engineered peptide backbones: Insights from the Protein Data Bank'. Together they form a unique fingerprint.

Cite this