Scholarlink

<p>AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3’s unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3’s advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.</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

Analysing protein complexes in plant science: insights and limitation with AlphaFold 3

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月22日

查看全文
  • Pei-Yu Lin,
  • Shiang-Chin Huang,
  • Kuan-Lin Chen,
  • Yu-Chun Huang,
  • Chia-Yu Liao,
  • Guan-Jun Lin,
  • HueyTyng Lee,
  • Pao-Yang Chen

摘要

AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3’s unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3’s advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.