installation usage idpflex contributing authors history
It is estimated that about 30% of the eucariotic proteome consists of intrinsically disordered proteins (IDP’s), yet their presence in public structural databases is severely underrepresented. IDP’s adopt heterogeneous inter-converting conformations with similar probabilities, preventing resolution of structures with X-Ray diffraction techniques. An alternative technique with wide application on IDP systems is small angle scattering (SAS). SAS can measure average structural features of IDP's when in vitro solution, or even at conditions mimicking protein concentrations found in the cell's cytoplasm.
Despite these advantages, the averaging nature of SAS measurements will prove unsatisfactory if one aims to differentiate among the different conformations that a particular IDP can adopt. Different distributions of conformations can yield the same average therefore it is not possible to retrace the true distribution if all that SAS provides is the average conformation.
To address this shortcoming, atomistic molecular dynamics (MD) simulations of IDP systems combined with enhanced sampling methods such as the Hamiltonian replica exchange method are specially suitable Affentranger06. These simulations can probe extensive regions of the IDP's conformational space and have the potential to offer a full-featured description of the conformational landscape of IDP's. The results of these simulations should not be taken at faith value, however. First, a proper comparison against available experimental SAS data is a must. This validation step is the requirement that prompted the development of idpflex.
The python package idpflex clusters the 3D conformations resulting from an MD simulation into a hierarchical tree by means of structural similarity among pairs of conformations. The conformations produced by the simulation take the role of Leafs in the hierarchichal tree. Nodes in the tree take the rol of IDP substates, with conformations under a particular Node making up one substate. Strictly speaking, idfplex does not require the IDP conformations to be produced by an MD simulation. Alternative conformation generators can be used, such as torsional sampling of the protein backbone Curtis12. In contrast to other methods Rozycki11, idpflex does not initially discard any conformation by labelling it as incompatible with the experimental data. This data is an average over all conformations, and using this average as the criterion by which to discard any specific conformation can lead to erroneous discarding decisions by the reasons stated above.
Default clustering is performed according to structural similarity between pairs of conformations, defined by the root mean square deviation algorithm Kabsch76. Alternatively, idpflex can cluster conformations according to an Euclidean distance in an abstract 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 Svergun95 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 Rozycki11. idpflex wraps other python packages (MDAnalysis Michaud11, Gowers2016, mdtraj McGibbon15) and third party applications (CRYSOL Svergun95, DSSP Kabsch83) 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.
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Source: https://github.com/jmborr/idpflex
This work is sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle LLC, for DOE. Part of this research is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, User Facilities under contract number DE-AC05-00OR22725.
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