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<p>Proteins and other macromolecules exist as dynamic ensembles of interconverting conformations essential for catalysis, allosteric regulation and molecular recognition. While AI tools like AlphaFold have revolutionized static structure prediction, they cannot yet capture conformational ensembles. Progress toward the next-generation ensemble predictors is limited by the lack of accurate, high-resolution ground-truth data at the scale required for training and validation—no single experimental technique fully resolves the atomistic complexity of conformational landscapes, and challenges remain in defining, representing, comparing and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and leveraging these to build benchmarks and ensemble-specific validation protocols. We also discuss how ensemble prediction will drive an interactive cycle of experimental and computational innovation, ultimately moving structural biology beyond static snapshots toward a dynamic understanding of the full complexity of molecular behavior.</p>

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From possibility to precision in macromolecular ensemble prediction

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Stephanie A. Wankowicz,
  • Massimiliano Bonomi

摘要

Proteins and other macromolecules exist as dynamic ensembles of interconverting conformations essential for catalysis, allosteric regulation and molecular recognition. While AI tools like AlphaFold have revolutionized static structure prediction, they cannot yet capture conformational ensembles. Progress toward the next-generation ensemble predictors is limited by the lack of accurate, high-resolution ground-truth data at the scale required for training and validation—no single experimental technique fully resolves the atomistic complexity of conformational landscapes, and challenges remain in defining, representing, comparing and validating structural ensembles. Here, we outline the infrastructure and methodological advances needed to overcome these barriers. We highlight emerging strategies for integrating heterogeneous experimental data into unified ensemble encoding representations and leveraging these to build benchmarks and ensemble-specific validation protocols. We also discuss how ensemble prediction will drive an interactive cycle of experimental and computational innovation, ultimately moving structural biology beyond static snapshots toward a dynamic understanding of the full complexity of molecular behavior.