bioRxiv2020
MotivationLocal protein structure is usually described via classifying each peptide to a unique element from a set of pre-defined structures. These so-called structural alphabets may differ in the number of structures or the length of peptides. Most methods that predict the local structure of a protein from its sequence rely on this kind of classification. However, since all peptides assigned to the same class are indistinguishable, such an approach may not be sufficient to model protein folding with high accuracy. ResultsWe developed a method that predicts the structural representation of a peptide from its sequence. For 5-mer peptides, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Importantly, our prediction method does not utilize information about protein homologues but only physicochemical properties of the amino acids and the statistics of the structures, but relies on a comprehensive feature-generation procedure based only on the protein sequence and the statistics of resolved structures. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions for that under various geometric constraints. AvailabilityThe online implementation of the method is provided freely at http://pbpred.eimb.ru Contactmilch@eimb.ru or vmilchev@uni-koeln.de Supplementary informationSupplementary data are available online at http://pbpred.eimb.ru/S/index.html