Structural coordinates: A novel approach to predict protein backbone conformation
| Autor: | Vladimir G. Tumanyan, Sergey A. Lukshin, Yuri V. Kravatsky, A. M. Nikitin, Yury V. Milchevskiy, Vladislava Milchevskaya, Natalia G. Esipova, Ivan V. Filatov |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Protein Structure Comparison Class (set theory) Computer science Protein Conformation Bond Angles Peptide Protein Structure Prediction Biochemistry Physical Chemistry Protein sequencing Protein structure Mathematical and Statistical Techniques Stereochemistry Sequence Analysis Protein Macromolecular Structure Analysis Peptide bond chemistry.chemical_classification Sequence Multidisciplinary Physics Statistics A protein Protein structure prediction Amino acid Folding (chemistry) Chemistry Physicochemical Properties Physical Sciences Medicine Structural Proteins Biological system Algorithms Research Article Protein Structure Similarity (geometry) Science Geometry Computational biology Research and Analysis Methods Set (abstract data type) 03 medical and health sciences Humans Statistical Methods Molecular Biology 030102 biochemistry & molecular biology Biology and Life Sciences Proteins Protein superfamily Physical Properties 030104 developmental biology chemistry Chemical Properties Dihedral Angles Mathematics Forecasting |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 5, p e0239793 (2021) |
| ISSN: | 1932-6203 |
| Popis: | 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.ruContactmilch@eimb.ru or vmilchev@uni-koeln.deSupplementary informationSupplementary data are available online at http://pbpred.eimb.ru/S/index.html |
| Databáze: | OpenAIRE |
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