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SLDMOL: A Tool for the Structural Characterization of Thermally Disordered Membrane Proteins

Published

Author(s)

Joseph E. Curtis, Hailiang Zhang, Hirsh Nanda

Abstract

SLDMOL is a program for modeling the one-dimensional scattering length density (SLD) profile of proteins at the lipid membrane-solution interface or adsorbed to other surfaces. The program reads experimental SLD data from neutron or X-ray reflectivity measurements and compares the results to a trajectory of protein structures, finding the conformation and orientation that best fits the experimental data. SLDMOL is a freely distributed open source program written in python that can be run independently using command lines or with a GUI integrated in the larger SASSIE package. Sample environment conditions can be replicated including H2O/D2O solvent contrasts, specific amino acid deuteration and complex molecular assemblies. Ensembles of protein conformations can be generated independently (e.g. molecular dynamics simulations) or with SASSIE. For each individual structure a best-fit SLD profile is outputted along with a goodness of fit parameter, protein depth penetration and surface coverage. In addition to individual comparisons SLD profiles can be calculated over ensemble averages of protein structures. As a result SLDMOL provides a detailed molecular interpretation of reflectivity data or conversely can be used to predict experimental outcomes for different protein conformation and specific deuteration schemes prior to measurements.
Citation
Computer Physics Communications
Volume
185

Keywords

neutron reflectivity, membrane protein, molecular dynamics

Citation

Curtis, J. , Zhang, H. and , H. (2014), SLDMOL: A Tool for the Structural Characterization of Thermally Disordered Membrane Proteins, Computer Physics Communications, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915263 (Accessed December 11, 2024)

Issues

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

Created November 1, 2014, Updated February 19, 2017