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<p>Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries but also pose challenges in leveraging such massive datasets effectively. In this work, we explore the dynamic landscape of hundreds of thousands of AlphaFold-predicted structures using normal mode analysis. The resulting data serve to empirically define an alphabet summarizing relative protein flexibility, termed ProFlex. Leveraging ProFlex, we describe the flexibility information space occupied by this massive dataset. We believe leveraging the data compression offered by ProFlex-like approaches opens opportunities for understanding protein function, refining structural predictions, and rendering analyses computationally tractable.</p>

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ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Damian J. Magill,
  • Timofey A. Skvortsov

摘要

Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries but also pose challenges in leveraging such massive datasets effectively. In this work, we explore the dynamic landscape of hundreds of thousands of AlphaFold-predicted structures using normal mode analysis. The resulting data serve to empirically define an alphabet summarizing relative protein flexibility, termed ProFlex. Leveraging ProFlex, we describe the flexibility information space occupied by this massive dataset. We believe leveraging the data compression offered by ProFlex-like approaches opens opportunities for understanding protein function, refining structural predictions, and rendering analyses computationally tractable.