Modeling proteins using a super-secondary structure library and NMR chemical shift information

Vilas Menon, Brinda K. Vallat, Joseph M. Dybas, Andras Fiser

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

Original languageEnglish (US)
Pages (from-to)891-899
Number of pages9
JournalStructure
Volume21
Issue number6
DOIs
StatePublished - Jun 4 2013

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Small Molecule Libraries
Proteins
Amino Acid Motifs
Libraries
Magnetic Resonance Spectroscopy

ASJC Scopus subject areas

  • Molecular Biology
  • Structural Biology

Cite this

Modeling proteins using a super-secondary structure library and NMR chemical shift information. / Menon, Vilas; Vallat, Brinda K.; Dybas, Joseph M.; Fiser, Andras.

In: Structure, Vol. 21, No. 6, 04.06.2013, p. 891-899.

Research output: Contribution to journalArticle

Menon, Vilas ; Vallat, Brinda K. ; Dybas, Joseph M. ; Fiser, Andras. / Modeling proteins using a super-secondary structure library and NMR chemical shift information. In: Structure. 2013 ; Vol. 21, No. 6. pp. 891-899.
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