Scholarlink

<p>Transthyretin amyloidosis (ATTR) is a genetically diverse disorder caused by destabilising mutations in the transthyretin (TTR) protein, leading to pathological aggregation. While stabilisers like tafamidis and acoramidis are approved, their efficacy across TTR variants remains unclear. This study presents an in silico pipeline combining AlphaFold3 for structure prediction, ESM2 for sequence embeddings, DiffDock-L and AutoDock Vina for molecular docking, and DiffSBDD for ligand generation. Simulations show that binding affinities of approved ligands vary significantly among TTR variants, with some mutations (e.g., W61L, Y98F) reducing binding despite being distant from the binding site. Embedding-based clustering highlights potential benign mutations and supports scalable variant classification. Additionally, customised ligand optimisation can recover binding affinity in specific cases, though effects are mutation-dependent. These findings emphasise the need for variant-aware therapeutic strategies. This integrative approach offers a foundation for precision drug design in ATTR, enabling the development of personalised stabilisers tailored to individual mutational profiles.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrative structural profiling and ligand optimisation across the transthyretin mutational landscape

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月25日

查看全文
  • Ugo Lomoio,
  • Valentina Carbonari,
  • Federico Manuel Giorgi,
  • Francesco Ortuso,
  • Pietro Lió,
  • Pierangelo Veltri,
  • Pietro Hiram Guzzi

摘要

Transthyretin amyloidosis (ATTR) is a genetically diverse disorder caused by destabilising mutations in the transthyretin (TTR) protein, leading to pathological aggregation. While stabilisers like tafamidis and acoramidis are approved, their efficacy across TTR variants remains unclear. This study presents an in silico pipeline combining AlphaFold3 for structure prediction, ESM2 for sequence embeddings, DiffDock-L and AutoDock Vina for molecular docking, and DiffSBDD for ligand generation. Simulations show that binding affinities of approved ligands vary significantly among TTR variants, with some mutations (e.g., W61L, Y98F) reducing binding despite being distant from the binding site. Embedding-based clustering highlights potential benign mutations and supports scalable variant classification. Additionally, customised ligand optimisation can recover binding affinity in specific cases, though effects are mutation-dependent. These findings emphasise the need for variant-aware therapeutic strategies. This integrative approach offers a foundation for precision drug design in ATTR, enabling the development of personalised stabilisers tailored to individual mutational profiles.