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A simple and efficient statistical potential for scoring ensembles of protein structures

Cossio, P
•
Granata, D
•
Seno, F
altro
Trovato, Antonio
2012
  • journal article

Periodico
SCIENTIFIC REPORTS
Abstract
In protein structure prediction it is essential to score quickly and reliably large sets of models by selecting the ones that are closest to the native state. We here present a novel statistical potential constructed by Bayesian analysis measuring a few structural observables on a set of 500 experimental protein structures. Even though employing much less parameters than current state-of-the-art methods, our potential is capable of discriminating with an unprecedented reliability the native state in large sets of misfolded models of the same protein. We also introduce the new idea that thermal fluctuations cannot be neglected for scoring models that are very similar to each other. In these cases, the best structure can be recognized only by comparing the probability distributions of our potential over short finite temperature molecular dynamics simulations starting from the competing models.
DOI
10.1038/srep00351
WOS
WOS:000302460800001
Archivio
http://hdl.handle.net/20.500.11767/14093
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84859738455
http://preprints.sissa.it/xmlui/handle/1963/5920
http://cdsads.u-strasbg.fr/abs/2012NatSR...2E.351C
Diritti
open access
Soggetti
  • QUASI-CHEMICAL APPROX...

  • STRUCTURE PREDICTION

  • ENERGY FUNCTIONS

  • FOLD RECOGNITION

  • MEAN FORCE

  • STRUCTURE SELECTION

  • CONTACT POTENTIALS

  • AMINO-ACIDS

  • DISCRIMINATION

  • QUALITY

  • Settore FIS/03 - Fisi...

Scopus© citazioni
45
Data di acquisizione
Jun 14, 2022
Vedi dettagli
Web of Science© citazioni
42
Data di acquisizione
Mar 12, 2024
Visualizzazioni
4
Data di acquisizione
Apr 19, 2024
Vedi dettagli
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