Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

Abstract

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein (1)H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for (1)Hα, (1)HN, (13)Cα, (13)Cβ, (13)CO and backbone (15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.

Protein dynamics and function from solution state NMR spectroscopy

“…Together with the further development of spin relaxation techniques and isotope labeling strategies it is likely that protein dynamics will also be possible to study from chemical shifts. There has been development in this direction (Lehtivarjo et al 2012;Markwick et al 2010;Robustelli et al 2012) and the time averaging of chemical shifts in these methods is based on molecular dynamics simulations, which means that fast time scale motions can be addressed. As an example, Vendruscolo and co-workers developed a methodology that enabled characterization of a conformational equilibrium in Rnase A by including chemical shifts as restraints in molecular dynamics simulations (Camilloni et al 2012(Camilloni et al , 2013.…”

Towards understanding the molecular recognition process in prokaryotic zinc-finger domain

“…So far, to explore the dynamic behavior of Ros87 in solution we used chemical shift data ( 1 H, 15 N and 13 C) and the determined NMR ensemble [13]. At first, in order to check the quality of the chemical shifts prediction tool we fitted all experimental data to each conformer of the NMR ensemble using 4DSPOT software [33,34]. All backbone chemical shifts ( 13 Ca, 15 N, 1 H N and 1 Ha) provided good quality fits as reflected by Q factor values in the range of 0.032e0.111 for 15 N, 0.051e0.109 for 1 H N , 0.024e0.027 for 13 Ca, 0.062e0.069 for 1 Ha (Table SI1).…”

PPM_One: a static protein structure based chemical shift predictor

“…With the rapid expansions of both the BMRB (Ulrich et al 2008) database for protein chemical shifts and the protein data bank (PDB) (Berman et al 2000), empirical knowledgebased approaches have been developed in recent years that parameterize chemical shift hyper-surfaces as a function of atomic coordinates (Kohlhoff et al 2009;Lehtivarjo et al 2009Lehtivarjo et al , 2012Li and Brüschweiler 2012;Neal et al 2003;Sahakyan et al 2011a, b;Shen andBax 2007, 2010;Case 2001, 2002). At room temperature, the experimental chemical shift of a given nucleus reflects the Boltzmannweighted average of the 'instantaneous' chemical shifts of a large number of conformational substates.…”

“…At room temperature, the experimental chemical shift of a given nucleus reflects the Boltzmannweighted average of the 'instantaneous' chemical shifts of a large number of conformational substates. Therefore, chemical shift predictors that are parametrized based on experimental chemical shifts of proteins in solution against static crystal structures implicitly include some degree of dynamics averaging (Lehtivarjo et al 2009(Lehtivarjo et al , 2012Li and Brüschweiler 2012). In addition, some predictors, such as SPARTA?, include S 2 order parameters that are predicted from local interatomic contacts to account for some dynamics averaging effects (Shen and Bax 2010;Zhang and Brüschweiler 2002).…”

“…By fitting the ensemble averaged, back-calculated chemical shifts against experimental values, a set of 'static' model parameters can be obtained. Both 4DSPOT (Lehtivarjo et al 2009(Lehtivarjo et al , 2012 and PPM (Li and Brüsch-weiler 2012) follow this strategy by parametrizing chemical shifts using conformational ensembles generated by MD instead of static protein structures. In other cases, structurebased prediction has been augmented with sequence-based chemical shift information (Han et al 2011;Wishart et al 1997).…”