Abstract
Various methods for understanding the structural and dynamic properties of proteins rely on the analysis of their NMR chemical shifts. These methods require the initial assignment of NMR signals to particular atoms in the sequence of the protein, a step that can be very time-consuming. The probabilistic interaction network of evidence (PINE) algorithm for automated assignment of backbone and side chain chemical shifts utilizes a Bayesian probabilistic network model that analyzes sequence data and peak lists from multiple NMR experiments. PINE, which is one of the most popular and reliable automated chemical shift assignment algorithms, has been available to the protein NMR community for longer than a decade. We announce here a new web server version of PINE, called Integrative PINE (I-PINE), which supports more types of NMR experiments than PINE (including three-dimensional nuclear Overhauser enhancement and four-dimensional J-coupling experiments) along with more comprehensive visualization of chemical shift based analysis of protein structure and dynamics. The I-PINE server is freely accessible at http://i-pine.nmrfam.wisc.edu. Help pages and tutorial including browser capability are available at: http://i-pine.nmrfam.wisc.edu/instruction.html. Sample data that can be used for testing the web server are available at: http://i-pine.nmrfam.wisc.edu/examples.html.
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Web server availability
I-PINE web server is freely available from http://i-pine.nmrfam.wisc.edu for academic users.
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Acknowledgements
This work was supported by a Grant (P41GM103399) from the Biomedical Technology Research Resources (BTRR) Program of the National Institute of General Medical Sciences (NIGMS), National Institutes of Health (NIH). H.R.E. and H.T.D were supported in part by the National Center for Biomolecular NMR Data Processing and Analysis, which is supported by NIH Grant P41GM111135.
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Lee, W., Bahrami, A., Dashti, H.T. et al. I-PINE web server: an integrative probabilistic NMR assignment system for proteins. J Biomol NMR 73, 213–222 (2019). https://doi.org/10.1007/s10858-019-00255-3
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DOI: https://doi.org/10.1007/s10858-019-00255-3