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

Abstract 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, i...

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Main Authors: Pei-Yu Lin, Shiang-Chin Huang, Kuan-Lin Chen, Yu-Chun Huang, Chia-Yu Liao, Guan-Jun Lin, HueyTyng Lee, Pao-Yang Chen
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Botanical Studies
Subjects:
Online Access:https://doi.org/10.1186/s40529-025-00462-2
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author Pei-Yu Lin
Shiang-Chin Huang
Kuan-Lin Chen
Yu-Chun Huang
Chia-Yu Liao
Guan-Jun Lin
HueyTyng Lee
Pao-Yang Chen
author_facet Pei-Yu Lin
Shiang-Chin Huang
Kuan-Lin Chen
Yu-Chun Huang
Chia-Yu Liao
Guan-Jun Lin
HueyTyng Lee
Pao-Yang Chen
author_sort Pei-Yu Lin
collection DOAJ
description Abstract 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.
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institution OA Journals
issn 1999-3110
language English
publishDate 2025-05-01
publisher SpringerOpen
record_format Article
series Botanical Studies
spelling doaj-art-3e036c25463348fda62d8097f923fa132025-08-20T01:52:29ZengSpringerOpenBotanical Studies1999-31102025-05-0166111210.1186/s40529-025-00462-2Analysing protein complexes in plant science: insights and limitation with AlphaFold 3Pei-Yu Lin0Shiang-Chin Huang1Kuan-Lin Chen2Yu-Chun Huang3Chia-Yu Liao4Guan-Jun Lin5HueyTyng Lee6Pao-Yang Chen7Institute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaAbstract 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.https://doi.org/10.1186/s40529-025-00462-2AlphaFold 3Crop resilienceProtein structureProtein–protein interactionProtein complexStructure biology
spellingShingle Pei-Yu Lin
Shiang-Chin Huang
Kuan-Lin Chen
Yu-Chun Huang
Chia-Yu Liao
Guan-Jun Lin
HueyTyng Lee
Pao-Yang Chen
Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
Botanical Studies
AlphaFold 3
Crop resilience
Protein structure
Protein–protein interaction
Protein complex
Structure biology
title Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
title_full Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
title_fullStr Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
title_full_unstemmed Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
title_short Analysing protein complexes in plant science: insights and limitation with AlphaFold 3
title_sort analysing protein complexes in plant science insights and limitation with alphafold 3
topic AlphaFold 3
Crop resilience
Protein structure
Protein–protein interaction
Protein complex
Structure biology
url https://doi.org/10.1186/s40529-025-00462-2
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