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<p>The ASH domain (ASPM-SPD-2-Hydin), recognized for its microtubule-binding feature, is found in several proteins associated with the Golgi apparatus, centrosome, cilia, and flagella. These ASH-domain-containing proteins play essential roles in maintaining cytoskeletal integrity, regulating ciliogenesis, enabling cellular motility, and facilitating intracellular transport. Mutations in these proteins are implicated in a spectrum of human disorders, including ciliopathies, infertility, neurological syndromes, and cancer. Despite its functional importance, the ASH domain remains difficult to detect via conventional domain search engines due to its low sequence conservation. This review provides an integrative overview of ASH-domain-containing proteins, their biological roles and disease associations, and proposes a modern identification pipeline using AlphaFold-based structural prediction coupled with PyMOL superposition analysis for an accurate recognition of ASH domains.</p>

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ASH: a new protein domain on the horizon

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Sneha Desai,
  • Jacinta S. D’Souza

摘要

The ASH domain (ASPM-SPD-2-Hydin), recognized for its microtubule-binding feature, is found in several proteins associated with the Golgi apparatus, centrosome, cilia, and flagella. These ASH-domain-containing proteins play essential roles in maintaining cytoskeletal integrity, regulating ciliogenesis, enabling cellular motility, and facilitating intracellular transport. Mutations in these proteins are implicated in a spectrum of human disorders, including ciliopathies, infertility, neurological syndromes, and cancer. Despite its functional importance, the ASH domain remains difficult to detect via conventional domain search engines due to its low sequence conservation. This review provides an integrative overview of ASH-domain-containing proteins, their biological roles and disease associations, and proposes a modern identification pipeline using AlphaFold-based structural prediction coupled with PyMOL superposition analysis for an accurate recognition of ASH domains.