English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

space spanned by a set of structural properties, such as radius of gyration and end-to-end distance. Comparison to experimental SAS data is carried out first by calculating the SAS intensities (Svergun, 1995) for each conformation produced by the MD simulation. This result in SAS intensities for each Leaf in the hierarchical tree. Intensities are then propagated up the hierarchical tree, yielding a SAS intensity for each Node. Because each Node takes the role of a conformational substate, we obtain SAS intensities for each substate. idpflex can compare the SAS intensity of each substate against the experimental SAS data. Also, it can average intensities from different substates and compare against experimental SAS data. The fitting functionality included in idpflex allows for selection of the set of substates that will yield maximal similarity between computed and experimental SAS intensities. Thus, arranging tens of thousands of conformations into (typically) less than ten substates provides the researcher with a manageable number of conformations from which to derive meaningful conclusions regarding the conformational variability of IDP’s. idpflex also provides a set of convenience functions to compute structural features of IDP’s for each of the conformations produced by the MD simulation. These properties can then be propagated up the hierarchical tree much in the same way as SAS intensities are propagated. Thus, one can compute for each substate properties such as radius of gyration, end-to-end distance, asphericity, solvent exposed surface area, contact maps, and secondary structure content. All these structural properties require atomistic detail, thus idpflex is more apt for the study of IDP’s than for the study of quaternary protein arrangements, where clustering of coarse-grain simulations becomes a better option (Rozycki, 2011). idpflex wraps other python packages (MDAnalysis (Michaud-Agrawal, 2011), (Gowers, 2016), mdtraj (McGibbon, 2015)) and third party applications (CRYSOL (Svergun, 1995), DSSP (Kabsch, 1983)) that actually carry out the calculation of said properties. Additional properties can be incorporated by inheriting from the base Property classes. To summarize, idpflex integrates MD simulations with SAS experiments in order to obtain a manageable representation of the rich conformational diversity of IDP’s, a pertinent problem in structural biology. The “notebooks” directory within the source contains two Jupyter notebooks that illustrate the use of idpflex when clustering an example MD trajectory.

idpflex: Analysis of Intrinsically Disordered Proteins by Comparing Simulations to Small Angle Scattering Experiments