Login / Signup

Structural coordinates: A novel approach to predict protein backbone conformation.

Vladislava MilchevskayaAlexei M NikitinSergey A LukshinIvan V FilatovYuri V KravatskyVladimir G TumanyanNatalia G EsipovaYury V Milchevskiy
Published in: PloS one (2021)
To alleviate the above challenges, we propose a method that constructs a peptide's structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids' physicochemical properties and the resolved structures' statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints.
Keyphrases
  • amino acid
  • high resolution
  • systematic review
  • machine learning
  • cross sectional
  • protein protein
  • deep learning
  • healthcare