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Open Access Highly Accessed Research article

NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction

Anna Katharina Dehof1, Simon Loew2, Hans-Peter Lenhof2 and Andreas Hildebrandt2*

Author Affiliations

1 Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany

2 Johannes-Gutenberg-University Mainz, 55128 Mainz, Germany

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BMC Bioinformatics 2013, 14:98  doi:10.1186/1471-2105-14-98

Published: 16 March 2013

Abstract

Background

NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.

A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.

Results

In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.

In addition to this main result – the NightShift framework itself – we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.

Conclusion

By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.

The framework can be downloaded from https://bitbucket.org/akdehof/nightshift webcite. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/ webcite.