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Article

DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences

1
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter Str. 50/A, 1083 Budapest, Hungary
2
3in Research Group, Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 2500 Esztergom, Hungary
*
Author to whom correspondence should be addressed.
Academic Editor: Daniele Di Marino
Biomolecules 2021, 11(10), 1505; https://doi.org/10.3390/biom11101505
Received: 23 August 2021 / Revised: 26 September 2021 / Accepted: 6 October 2021 / Published: 12 October 2021
Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server. View Full-Text
Keywords: protein ensemble model; intrinsically disordered proteins; structure prediction; principal component analysis; local interaction; dihedral angle protein ensemble model; intrinsically disordered proteins; structure prediction; principal component analysis; local interaction; dihedral angle
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MDPI and ACS Style

Harmat, Z.; Dudola, D.; Gáspári, Z. DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences. Biomolecules 2021, 11, 1505. https://doi.org/10.3390/biom11101505

AMA Style

Harmat Z, Dudola D, Gáspári Z. DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences. Biomolecules. 2021; 11(10):1505. https://doi.org/10.3390/biom11101505

Chicago/Turabian Style

Harmat, Zita, Dániel Dudola, and Zoltán Gáspári. 2021. "DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences" Biomolecules 11, no. 10: 1505. https://doi.org/10.3390/biom11101505

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