ArticleInternational journal of molecular sciences2026
Evaluation of AI-Predicted GH11 Xylanase Models Against a Previously Unreported Experimental Structure: Implications for Conformational Accuracy and Ligand Binding.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.International journal of molecular sciences · 2026Article
- Evaluation of AlphaFold3 for Predicting Human Heme-Binding Protein Structures.International journal of molecular sciences · 2026Article
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1 author.
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Abstract
Artificial intelligence (AI)-based structure prediction tools have emerged as powerful methods for understanding previously unsolved structures. AI-predicted models are widely used for protein function identification, drug development, and protein engineering. Although AI-predicted structures offer significant opportunities to advance research, their inaccuracies can lead to misinterpretations of molecular mechanisms. Thus, evaluating the structural differences between AI-predicted and experimental structures is crucial for accurately understanding molecular mechanisms and guiding the design of subsequent experiments. In this study, the previously unreported crystal structure of xylanase from
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