Rethinking what pLDDT really tells us about protein flexibility
Jakob R Riccabona1, Johannes R Loeffler2, Clara T Schoeder1
1Institute for Drug Discovery, Faculty of Medicine, Leipzig University, Leipzig, Germany; Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, Dresden/Leipzig, Germany.
Structure (London, England : 1993)
|December 5, 2025
Summary
Deep learning accurately predicts protein structures. However, the study questions if predicted local confidence scores reflect true protein flexibility, crucial for understanding dynamics.
Area of Science:
- Structural biology
- Computational biology
- Biophysics
Background:
- Deep learning models have revolutionized protein 3D structure prediction.
- Confidence metrics like predicted local distance difference test (pLDDT) estimate prediction uncertainty.
Purpose of the Study:
- To investigate whether pLDDT accurately reflects intrinsic protein flexibility.
- To advance the understanding of protein dynamics and conformational changes.
Main Methods:
- Analysis of deep learning model outputs (pLDDT).
- Comparison of pLDDT with experimental and computational measures of protein flexibility.
Main Results:
- The relationship between pLDDT and intrinsic protein flexibility requires further investigation.
- pLDDT's ability to capture true protein dynamics is not fully established.
Conclusions:
- Clarifying the link between pLDDT and protein flexibility is essential.
- Accurate quantification of protein dynamics is key for modeling conformational changes across timescales.
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