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<p>Disordered proteins play essential roles in myriad cellular processes, yet their structural characterization remains a major challenge due to their dynamic and heterogeneous nature. Here we present a community-driven initiative to address this problem by advocating a unified framework for determining conformational ensembles of disordered proteins. Our aim is to integrate state-of-the-art experimental techniques with advanced computational methods, including knowledge-based sampling, enhanced molecular dynamics and machine learning models. The modular framework comprises three interconnected components: experimental data acquisition, computational ensemble generation and validation. The systematic development of this framework will ensure the accurate and reproducible determination of conformational ensembles of disordered proteins. We highlight the open challenges necessary to achieve this goal, including force-field accuracy, efficient sampling, and environmental dependence, advocating for collaborative benchmarking and standardized protocols.</p>

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Toward a unified framework for determining conformational ensembles of disordered proteins

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Hamidreza Ghafouri,
  • Pavel Kadeřávek,
  • Ana M. Melo,
  • Maria Cristina Aspromonte,
  • Pau Bernadó,
  • Juan Cortés,
  • Zsuzsanna Dosztányi,
  • Gábor Erdős,
  • Michael Feig,
  • Giacomo Janson,
  • Kresten Lindorff-Larsen,
  • Frans A. A. Mulder,
  • Peter Nagy,
  • Richard Pestell,
  • Damiano Piovesan,
  • Marco Schiavina,
  • Benjamin Schuler,
  • Nathalie Sibille,
  • Giulio Tesei,
  • Peter Tompa,
  • Michele Vendruscolo,
  • Jiri Vondrasek,
  • Wim Vranken,
  • Lukas Zidek,
  • Silvio C. E. Tosatto,
  • Alexander Miguel Monzon

摘要

Disordered proteins play essential roles in myriad cellular processes, yet their structural characterization remains a major challenge due to their dynamic and heterogeneous nature. Here we present a community-driven initiative to address this problem by advocating a unified framework for determining conformational ensembles of disordered proteins. Our aim is to integrate state-of-the-art experimental techniques with advanced computational methods, including knowledge-based sampling, enhanced molecular dynamics and machine learning models. The modular framework comprises three interconnected components: experimental data acquisition, computational ensemble generation and validation. The systematic development of this framework will ensure the accurate and reproducible determination of conformational ensembles of disordered proteins. We highlight the open challenges necessary to achieve this goal, including force-field accuracy, efficient sampling, and environmental dependence, advocating for collaborative benchmarking and standardized protocols.