Characterizing conformational ensembles of multi-domain proteins using anisotropic paramagnetic NMR restraints

2022 ◽  
Author(s):  
Xue-Ni Hou ◽  
Hidehito Tochio
Keyword(s):  
Paramagnetic Nmr ◽  
Conformational Ensembles ◽  
Nmr Restraints

scholarly journals The MPS1 kinase NTE region has helical propensity and preferred conformations towards the TPR domain

2020 ◽  
Author(s):  
Yoshitaka Hiruma ◽  
Minos-Timotheos Matsoukas ◽  
Wouter G. Touw ◽  
Georgios Spyroulias ◽  
Geert J.P.L. Kops ◽  
...  
Keyword(s):  
Spindle Assembly Checkpoint ◽  
Convex Surface ◽  
Conformational Space ◽  
Paramagnetic Nmr ◽  
Helical Structures ◽  
Monopolar Spindle ◽  
Distance Restraints ◽  
Paramagnetic Relaxation Enhancements ◽  
Nmr Restraints ◽  
Tpr Domain

AbstractThe mitotic spindle assembly checkpoint (SAC) ensures accurate segregation of chromosomes by preventing onset of anaphase until all chromosomes are properly attached to spindle microtubules. The Monopolar spindle 1 (MPS1) kinase is one of the SAC components, localizing at unattached kinetochores by an N-terminal localization module. This module comprises a flexible NTE module and the TPR domain, which we previously characterized for their contribution to kinetochore binding. Here we discuss the conformations of the highly flexible NTE with respect to the TPR domain, using paramagnetic NMR. The distance restraints derived from paramagnetic relaxation enhancements (PREs) show that the mobile NTE can be found in proximity of a large but specific part of the surface area of the TPR domain. To sample the conformational space of the NTE in the context of the NTE-TPR module, we used the ab initio Rosetta approach supplemented by paramagnetic NMR restraints. We find that many NTE residues have a propensity to form helical structures and that the module localizes at the convex surface of the TPR domain. This work demonstrates the highly dynamic nature of the interactions between the NTE and TPR domains and it shows that the convex rather than the canonical concave TPR surface mediates interactions, leading to the auto-inhibition that the TPR exerts upon the NTE region in the context of SAC signaling.

Conformational ensembles

Science ◽  
2021 ◽  
Vol 372 (6538) ◽  
pp. 142.2-142
Author(s):  
Valda Vinson
Keyword(s):  
Conformational Ensembles

scholarly journals Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database

2021 ◽  
Vol 1 (7) ◽  
Author(s):  
Federica Quaglia ◽  
Tamas Lazar ◽  
András Hatos ◽  
Peter Tompa ◽  
Damiano Piovesan ◽  
...  
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Conformational Ensembles ◽  
Intrinsically Disordered

scholarly journals A graph-based algorithm for detecting rigid domains in protein structures

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Truong Khanh Linh Dang ◽  
Thach Nguyen ◽  
Michael Habeck ◽  
Mehmet Gültas ◽  
Stephan Waack
Keyword(s):  
Structural Changes ◽  
Viterbi Algorithm ◽  
Structural Information ◽  
Line Graph ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Alternative Methods ◽  
Structural Transitions ◽  
Tuning Parameters ◽  
Conformational Ensembles

Abstract Background Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. Results We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. Conclusions The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/.

Sign in / Sign up

Export Citation Format

Share Document