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<p>Proteins are dynamic molecules whose conformational fluctuations drive biological function. Traditional structural techniques, including X-ray crystallography and cryo-electron microscopy, yield static representations that obscure dynamic complexity. Computational methods like molecular dynamics reveal conformational landscapes but face limits in cost and sampling. Machine learning now addresses these challenges by refining force fields, guiding adaptive sampling, and improving trajectory analysis; automating cryo-EM and NMR workflows; and enhancing structure prediction via deep learning and protein language models. By integrating physical and data-driven principles, machine learning advances dynamic structural modeling, bridging static representations with functional ensembles central to biology and drug discovery.</p>

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Advancements in characterization of protein dynamics with machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Mohammad Madani,
  • Anna Tarakanova

摘要

Proteins are dynamic molecules whose conformational fluctuations drive biological function. Traditional structural techniques, including X-ray crystallography and cryo-electron microscopy, yield static representations that obscure dynamic complexity. Computational methods like molecular dynamics reveal conformational landscapes but face limits in cost and sampling. Machine learning now addresses these challenges by refining force fields, guiding adaptive sampling, and improving trajectory analysis; automating cryo-EM and NMR workflows; and enhancing structure prediction via deep learning and protein language models. By integrating physical and data-driven principles, machine learning advances dynamic structural modeling, bridging static representations with functional ensembles central to biology and drug discovery.