On the use of low-resolution data to improve structure prediction of proteins and protein complexes

Marco D'Abramo, Tim Meyer, Pau Bernadó, Carles Pons, Juan Fernández Recio, Modesto Orozco

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

We present a systematic study of the ability of low-resolution experimental data, when combined with physical/statistical scoring functions, to improve the quality of theoretical structural models of proteins and protein complexes. Particularly, we have analyzed in detail the "extra value" added to the theoretical models by: electrospray mass spectrometry (ESIMS), small-angle X-ray scattering (SAXS), and hydrodynamic measurements. We found that any low-resolution structural data, even when (as in the case of mass spectrometry) obtained in conditions far from the physiological ones, help to improve the quality of theoretical models, but not all the coarse-grained experimental results are equally rich in information. The best results are always obtained when using SAXS data as experimental constraints, but either hydrodynamics or gas phase CCS data contribute to improving model prediction. The combination of suitable scoring functions and broadly available low-resolution structural data (technically easier to obtain) yields structural models that are notably close to the real structures.

Original languageEnglish (US)
Pages (from-to)3129-3137
Number of pages9
JournalJournal of Chemical Theory and Computation
Volume5
Issue number11
DOIs
StatePublished - Nov 2009

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