Nick Whyatt
Nick Whyatt has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Agnel Praveen Joseph
Scientific Computing Department, UKRI Science and Technology Facilities Council, Research Complex at Harwell, Didcot, UK
1 shared publication - Jeyan Thiyagalingam
Rutherford Appleton Laboratory
1 shared publication - Jyothish Thomas1 shared publication
Luc G. Elliot
1 shared publicationNiraj Bhujel
1 shared publication- Sony Malhotra
Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
1 shared publication
Latest Publications
- Open Access
- 14 November 2026
- Published by Springer Nature in Nature Communications
DPI-score: a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived assemblies
Advances in cryoEM have led to a surge in high-resolution structures; however, model building and refinement at resolutions ( > = 3 Å) and in regions with variable local resolution remain challenging, making validation essential. CryoEM derived assemblies often contain extensive protein-protein interfaces, however, most existing validation metrics focus on density fit or overall geometry without directly assessing interface quality. To address this, we present DPI-Score, a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived complexes. The method uses only raw structural coordinates of interface atoms, without requiring engineered features, and achieves 87.53% validation accuracy. DPI-Score was applied to 29,120 interfaces from 6,011 fitted entries with resolutions worse than 3 Å in the Electron Microscopy Data Bank. Here, we show that DPI-Score provides complementary information to existing validation metrics and can identify interface errors in modelled assemblies that are not detected by density-based scores alone.
- 14 November 2026
- Published by Springer Nature in Nature Communications
Open AccessDPI-score: a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived assemblies
Advances in cryoEM have led to a surge in high-resolution structures; however, model building and refinement at resolutions ( > = 3 Å) and in regions with variable local resolution remain challenging, making validation essential. CryoEM derived assemblies often contain extensive protein-protein interfaces, however, most existing validation metrics focus on density fit or overall geometry without directly assessing interface quality. To address this, we present DPI-Score, a deep learning-based metric for assessing protein-protein interfaces in cryoEM derived complexes. The method uses only raw structural coordinates of interface atoms, without requiring engineered features, and achieves 87.53% validation accuracy. DPI-Score was applied to 29,120 interfaces from 6,011 fitted entries with resolutions worse than 3 Å in the Electron Microscopy Data Bank. Here, we show that DPI-Score provides complementary information to existing validation metrics and can identify interface errors in modelled assemblies that are not detected by density-based scores alone.