IDPConformerGenerator is a flexible, modular platform for generating ensembles of disordered protein states that builds conformers by sampling backbone torsion angles of relevant sequence fragments extracted from protein structures in the RCSB Protein Data Bank.
IDPConformerGenerator can efficiently build large and diverse conformer pools of disordered proteins, with user defined options enabling variable fractional population of secondary structures, including matching those assigned based on NMR chemical shift data. These conformer pools are intended to be utilized as input for further approaches to match experimental data, such as re-weighting or sub-setting algorithms.
Note to users: IDR conformers generated with ldrs, specifically processed
using the align_coords() function in ldrs_helper.py prior to v0.7.17
may have the wrong stereochemistry. This bug has since been fixed. Thank you for
your understanding and we apologize for any inconvenience this has caused.
To get a first glance on IDPConformerGenerator, read through our first steps documentation page.
Installation instructions are described on the installation page.
Usage instructions are described in the usage page. See also
tutorial examples in the example/ folder or by following different sections on the usage page.
If you use IDPConformerGenerator, please always cite its original publication:
IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States João M. C. Teixeira, Zi Hao Liu, Ashley Namini, Jie Li, Robert M. Vernon, Mickaël Krzeminski, Alaa A. Shamandy, Oufan Zhang, Mojtaba Haghighatlari, Lei Yu, Teresa Head-Gordon, and Julie D. Forman-Kay The Journal of Physical Chemistry A 2022 126 (35), 5985-6003 DOI: 10.1021/acs.jpca.2c03726
If you use the Local Disordered Region Sampling (LDRS) module, please also cite:
Zi Hao Liu, João M C Teixeira, Oufan Zhang, Thomas E Tsangaris, Jie Li, Claudiu C Gradinaru, Teresa Head-Gordon, Julie D Forman-Kay, Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments, Bioinformatics, Volume 39, Issue 12, December 2023, btad739, https://doi.org/10.1093/bioinformatics/btad739
v0.7.25