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<p>The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformational diversity essential for drug discovery. FiveFold is a novel ensemble method that combines predictions from five complementary algorithms (AlphaFold2, RoseTTAFold, OmegaFold, ESMFold, and EMBER3D) to improve our understanding of protein conformational landscapes, representing a significant advancement in structural biology. This review examines current applications of the methodology, analyzes its unique advantages in modeling IDPs, and explores its expanding potential in drug discovery. To demonstrate the utility of this method, we conducted computational modeling of alpha-synuclein as a model IDP system, proving it can better capture conformational diversity than traditional single-structure methods. We discuss future applications in structure-based drug design, allosteric drug discovery, protein–protein interaction inhibitors, and precision medicine. The framework’s ability to generate multiple plausible conformations through its Protein Folding Shape Code (PFSC) and Protein Folding Variation Matrix (PFVM) addresses critical limitations in current structure prediction methodologies, enabling novel therapeutic intervention strategies targeting previously “undruggable” proteins.</p>

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A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Sarfaraz K. Niazi,
  • Jiaan Yang

摘要

The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformational diversity essential for drug discovery. FiveFold is a novel ensemble method that combines predictions from five complementary algorithms (AlphaFold2, RoseTTAFold, OmegaFold, ESMFold, and EMBER3D) to improve our understanding of protein conformational landscapes, representing a significant advancement in structural biology. This review examines current applications of the methodology, analyzes its unique advantages in modeling IDPs, and explores its expanding potential in drug discovery. To demonstrate the utility of this method, we conducted computational modeling of alpha-synuclein as a model IDP system, proving it can better capture conformational diversity than traditional single-structure methods. We discuss future applications in structure-based drug design, allosteric drug discovery, protein–protein interaction inhibitors, and precision medicine. The framework’s ability to generate multiple plausible conformations through its Protein Folding Shape Code (PFSC) and Protein Folding Variation Matrix (PFVM) addresses critical limitations in current structure prediction methodologies, enabling novel therapeutic intervention strategies targeting previously “undruggable” proteins.