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ROTDIF-web and ALTENS: GenApp-based Science Gateways for Biomolecular Nuclear Magnetic Resonance (NMR) Data Analysis and Structure Modeling

Published

Author(s)

Yuexi Chen, Cheol Jeong, Alexey Savelyev, Susan T. Krueger, Joseph E. Curtis, Emre H. Brookes, David Fushman

Abstract

Proteins and nucleic acids participate in essentially every biochemical process in living organisms, and the elucidation of their structure and motions is essential for our understanding how these molecular machines perform their function. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful versatile technique that provides critical information on the molecular structure and dynamics. Spin-relaxation data are used to determine the overall rotational diffusion and local motions of biological macromolecules, while residual dipolar couplings (RDCs) reveal local and long-range structural architecture of these molecules and their complexes. This information allows researchers to refine structures of proteins and nucleic acids and provides restraints for molecular docking. Several software packages have been developed by NMR researchers in order to tackle the complicated experimental data analysis and structure modeling. However, many of them are offline packages or command-line applications that require users to set up the run time environment and also to possess certain programming skills, which inevitably limits accessibility of this software to a broad scientific community. Here we present new science gateways designed for the NMR/structural biology community that address these current limitations in NMR data analysis. Using the GenApp framework for scientific gateways (https://genapp.rocks), we successfully transformed ROTDIF and ALTENS, two offline packages for bio-NMR data analysis, into science gateways that provide advanced computational functionalities, cloud-based data management, and interactive 2D and 3D plotting and visualizations. Furthermore, these gateways are integrated with molecular structure visualization tools (Jmol) and with gateways/engines (SASSIE-web) capable of generating huge computer-simulated structural ensembles of proteins and nucleic acids. This enables researchers to seamlessly incorporate conformational ensembles into the analysis in order to adequately take into account structural heterogeneity and dynamic nature of biological macromolecules. ROTDIF-web offers a versatile set of integrated modules/tools for determining and predicting molecular rotational diffusion tensors and model-free characterization of bond dynamics in biomacromolecules and for docking of molecular complexes driven by the information extracted from NMR relaxation data. ALTENS allows characterization of the molecular alignment under anisotropic conditions, which enables researchers to obtain accurate local and long-range bond-vector restraints for refining 3-D structures of macromolecules and their complexes. We will describe our experience bringing our programs into GenApp and illustrate the use of these gateways for specific examples of protein systems of high biological significance. We expect these gateways to be useful to structural biologists and biophysicists as well as NMR community and to stimulate other researchers to share their scientific software in a similar way.
Proceedings Title
Gateways 2019 Proceedings
Conference Dates
September 23-25, 2019
Conference Location
San Diego, CA, US
Conference Title
Gateways 2019

Keywords

GenApp, Generalized Application Framework, Biomolecular NMR, Structural Biology, Biophysics

Citation

Chen, Y. , Jeong, C. , Savelyev, A. , Krueger, S. , Curtis, J. , Brookes, E. and Fushman, D. (2019), ROTDIF-web and ALTENS: GenApp-based Science Gateways for Biomolecular Nuclear Magnetic Resonance (NMR) Data Analysis and Structure Modeling, Gateways 2019 Proceedings, San Diego, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928478 (Accessed June 18, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 22, 2019, Updated October 12, 2021