4D prediction of protein 1H chemical shifts

Lehtivarjo, Juuso; Hassinen, Tommi; Korhonen, Samuli-Petrus; Peräkylä, Mikael; Laatikainen, Reino

doi:10.1007/s10858-009-9384-1

Cited by 32 publications

(45 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…or Cl -ions were added. The equilibration protocol of the MD simulations is similar as before (Lehtivarjo et al 2009). In the production simulations of 100 ps the electrostatics were treated using the particle mesh Ewald method.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

“…The prediction protocol, for the most part, is the same as that presented in Lehtivarjo et al (2009) …”

Section: Prediction Protocolmentioning

confidence: 99%

“…In these methods, the teaching data usually consists of X-ray structures or single conformers of NMR structure ensembles, although chemical shifts are measured in solvent, where protein structures are dynamic. Recently, the importance of dynamics in chemical shift prediction has been recognized in the literature (Baskaran et al 2010;Lehtivarjo et al 2009;Li and Brueschweiler 2010;Markwick et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Baskaran et al (2010) showed that by averaging the prediction results of SHIFTX (Neal et al 2003) or SHIFTS (Xu and Case 2001) over the conformations of the NMR ensembles, about a 9% improvement in prediction accuracy was gained compared with the prediction using only the lowest energy conformations. On the other hand, short MD simulations, mapping local fluctuations, gave a 6-7% benefit for 1 H shifts (Lehtivarjo et al 2009). In order to gain more extensively mapped protein dynamics for the chemical shift prediction, we decided to expand the 4th dimension (conformational space) of our prediction model by combining both approaches.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2012

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Molecular Dynamicsmentioning

confidence: 99%

“…The prediction protocol, for the most part, is the same as that presented in Lehtivarjo et al (2009) …”

Section: Prediction Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…In case of glucopyranosyl and glucuronyl fragments, the structures are generated with the correct stereochemistry and chair conformation with as many as possible hydroxyl groups at equatorial position using the CORINA software ( http://www.molecular-networks.com ). Then the 3D structure is used for 1 H-NMR spectra predictions in a specifi ed deuterated solvent using the PERCH NMR Software ( www.perchsolutions.com ) following a similar procedure as described by [ 31 ]. The structures are optimised in three-dimensional space; a statistical set of conformers is generated using Monte-Carlo/molecular dynamic analysis.…”

Section: Examplesmentioning

confidence: 99%

Automated Annotation of Microbial and Human Flavonoid-Derived Metabolites

Mihaleva

Ünlü

Vervoort

et al. 2014

Molecular and Integrative Toxicology

View full text Add to dashboard Cite

Flavonoids are a class of natural compounds essentially produced by plants that are part of animal and human diets and have assumed health-promoting benefi ts. Upon human consumption, these fl avonoids are to a modest extent absorbed in the small intestines. The major part arrives in the colon where the microfl ora utilises and converts the fl avonoids to a wide range of products. Many of these products are absorbed in the major intestines and subsequently metabolised by the host. To understand the impact of the microfl ora on the metabolism and possible effects on human health, complete (and quantitative) identifi cation of the microbial as well as human metabolic conversion products of fl avonoids is required. This is a challenging task, as these bioconversion products are often present in relatively small amounts, making classical identifi cation strategies based on (accurate) mass information or nuclear magnetic resonance, not straightforward. In the absence of reference compounds, annotation of a component may be achieved by detailed expert evaluation, e.g. by searching for similar fragmentation patterns in spectral databases of known compounds. However, such manual analysis is a tedious task, and in advanced metabolite profi ling experiments, with large numbers of unknown metabolites, this is a major bottleneck. Therefore, new strategies are needed for quick and reliable identifi cation of the diverse range of molecules in complex matrices (faeces, blood, urine). Intelligent software for annotation and identifi cation of unknowns is crucial to fully exploit complex datasets. We developed a new software tool (MAGMA) for (sub)structure-based annotation of LC-MSn datasets which,

show abstract

Overview of Probing Protein‐Ligand Interactions Using NMR

2015

View full text Add to dashboard Cite

Nuclear magnetic resonance (NMR) is a powerful technique for the study and characterization of protein-ligand interactions. In this unit we review both experiments where the NMR spectrum of the protein is observed (protein-observed NMR experiments) and those where the NMR spectra of the ligand is observed (ligand-observed NMR experiments) for the identification of binding partners, the measurement of protein-ligand affinity, the design of molecules that are active against biological targets such as proteins, and the assessment of the binding modes of the ligands. Ligand-observed methods discussed in this unit are Nuclear Overhauser Effect (NOE)-based approaches, with well-known experiments such as the Saturation Transfer Difference, Water-Ligand Observed via Gradient Spectroscopy (WaterLOGSY), and transferred-Nuclear Overhauser Effect Spectroscopy (tr-NOESY) experiments, and also the INPHARMA experiment. Regarding protein-observed experiments, this unit focuses on the use of chemical shift perturbations observed in protein-NMR spectra upon ligand binding. Also discussed is how these chemical shift perturbations can be used for the analysis of protein-ligand complexes, including fast structure determination when combined with docking.

show abstract

4D prediction of protein 1H chemical shifts

Cited by 32 publications

References 40 publications

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

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

Automated Annotation of Microbial and Human Flavonoid-Derived Metabolites

Overview of Probing Protein‐Ligand Interactions Using NMR

Contact Info

Product

Resources

About