Dataset Information

Classifying the Binding Modes of Disordered Proteins.

ABSTRACT: Disordered proteins often act as interaction hubs in cellular pathways, via the specific recognition of a distinguished set of partners. While disordered regions can adopt a well-defined conformation upon binding, the coupled folding to binding model does not explain how interaction versatility is achieved. Here, I present a classification scheme for the binding modes of disordered protein regions, based on their conformational heterogeneity in the bound state. Binding modes are defined as (i) disorder-to-order transitions leading to a well-defined bound state, (ii) disordered binding leading to a disordered bound state and (iii) fuzzy binding when the degree of disorder in the bound state may vary with the partner or cellular conditions. Fuzzy binding includes polymorphic bound structures, conditional folding and dynamic binding. This classification scheme describes the structural continuum of complexes involving disordered regions as well as their context-dependent interaction behaviors.

SUBMITTER: Fuxreiter M

PROVIDER: S-EPMC7697186 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classifying the Binding Modes of Disordered Proteins.

Fuxreiter Monika M

International journal of molecular sciences 20201116 22

Disordered proteins often act as interaction hubs in cellular pathways, via the specific recognition of a distinguished set of partners. While disordered regions can adopt a well-defined conformation upon binding, the coupled folding to binding model does not explain how interaction versatility is achieved. Here, I present a classification scheme for the binding modes of disordered protein regions, based on their conformational heterogeneity in the bound state. Binding modes are defined as (i) < ...[more]

PMID: 33207556

Similar Datasets

Project description:Many disordered proteins function via binding to a structured partner and undergo a disorder-to-order transition. The coupled folding and binding can confer several functional advantages such as the precise control of binding specificity without increased affinity. Additionally, the inherent flexibility allows the binding site to adopt various conformations and to bind to multiple partners. These features explain the prevalence of such binding elements in signaling and regulatory processes. In this work, we report ANCHOR, a method for the prediction of disordered binding regions. ANCHOR relies on the pairwise energy estimation approach that is the basis of IUPred, a previous general disorder prediction method. In order to predict disordered binding regions, we seek to identify segments that are in disordered regions, cannot form enough favorable intrachain interactions to fold on their own, and are likely to gain stabilizing energy by interacting with a globular protein partner. The performance of ANCHOR was found to be largely independent from the amino acid composition and adopted secondary structure. Longer binding sites generally were predicted to be segmented, in agreement with available experimentally characterized examples. Scanning several hundred proteomes showed that the occurrence of disordered binding sites increased with the complexity of the organisms even compared to disordered regions in general. Furthermore, the length distribution of binding sites was different from disordered protein regions in general and was dominated by shorter segments. These results underline the importance of disordered proteins and protein segments in establishing new binding regions. Due to their specific biophysical properties, disordered binding sites generally carry a robust sequence signal, and this signal is efficiently captured by our method. Through its generality, ANCHOR opens new ways to study the essential functional sites of disordered proteins.

Dataset Information

Classifying the Binding Modes of Disordered Proteins.

Publications

Classifying the Binding Modes of Disordered Proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets