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Quality Evaluation Based Simulation Selection (QEBSS) for analysis of conformational ensembles and dynamics of multidomain proteins

  • Amanda E. Sandelin
  • , Ricky Nencini
  • , Ekrem Yasar
  • , Satoshi Fudo
  • , Vassilis Stratoulias
  • , Tommi Kajander
  • , O. H.Samuli Ollila*
  • *Corresponding author for this work
  • University of Helsinki
  • Erzincan University

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Multidomain proteins containing both folded and intrinsically disordered regions are crucial for biological processes, but characterizing their conformational ensembles and dynamics remains challenging. We introduce the Quality Evaluation Based Simulation Selection (QEBSS) protocol, which combines MD simulations with NMR-derived protein backbone 15N T1 and T2 spin relaxation times and hetNOE values to interpret conformational ensembles and dynamics of multidomain proteins. We demonstrate the practical advantage of QEBSS by characterizing four flexible multidomain proteins: calmodulin, EN2, MANF, and CDNF. These biologically important proteins have been difficult to study due to their flexible nature. Our findings reveal new insights into their conformational landscapes and dynamics, providing mechanistic understanding of their biological functions. QEBSS offers quantitative quality evaluation of simulations and a systematic approach for resolving conformational ensembles of multidomain proteins with heterogeneous dynamics. Given the importance of such proteins in biology, biotechnology, and materials science, QEBSS should benefit fields from drug design to novel materials development. (Figure presented.)

Original languageEnglish
Article number241
JournalCommunications Chemistry
Volume8
Issue number1
DOIs
Publication statusPublished - Dec 2025
MoE publication typeA1 Journal article-refereed

Funding

We acknowledge CSC—IT Center for Science for computational resources and the Research Council of Finland for funding (grant nos. 315596, 319902, 345631, 356568, and 350636). RN acknowledges funding from Emil Aaltonen Foundation and E.Y. from EC Research Innovation Action HPC-EUROPA3 (INFRAIA-2016-1-730897) under the H2020 Programme. T.K. acknowledges funding from Jane and Aatos Erkko Foundation.

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