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Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Authors :
Romain Launay
Elin Teppa
Jérémy Esque
Isabelle André
Toulouse Biotechnology Institute (TBI)
Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)
Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Kumar Selvarajoo
John M. Walker
Source :
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology, Kumar Selvarajoo. Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology, 2553, Springer, pp.57-77, 2022, Methods in Molecular Biology, 978-1-0716-2616-0. ⟨10.1007/978-1-0716-2617-7_4⟩, Methods in Molecular Biology ISBN: 9781071626160
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

Details

Language :
English
ISBN :
978-1-07-162616-0
ISBNs :
9781071626160
Database :
OpenAIRE
Journal :
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology, Kumar Selvarajoo. Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology, 2553, Springer, pp.57-77, 2022, Methods in Molecular Biology, 978-1-0716-2616-0. ⟨10.1007/978-1-0716-2617-7_4⟩, Methods in Molecular Biology ISBN: 9781071626160
Accession number :
edsair.doi.dedup.....097bdc2e353deb2bf5e75a2c04d60ffb
Full Text :
https://doi.org/10.1007/978-1-0716-2617-7_4⟩