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Modeling the Dynamics of Protein-Protein Interfaces, How and Why?
Karaca, Ezgi; Prévost, Chantal; Sacquin-Mora, Sophie.
  • Karaca E; Izmir Biomedicine and Genome Center, Izmir 35340, Turkey.
  • Prévost C; Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir 35340, Turkey.
  • Sacquin-Mora S; CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 rue Pierre et Marie Curie, 75005 Paris, France.
Molecules ; 27(6)2022 Mar 11.
Article in English | MEDLINE | ID: covidwho-1765795
ABSTRACT
Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Intrinsically Disordered Proteins Type of study: Prognostic study Language: English Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: Molecules27061841

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Intrinsically Disordered Proteins Type of study: Prognostic study Language: English Journal subject: Biology Year: 2022 Document Type: Article Affiliation country: Molecules27061841