Journal: bioRxiv
Article Title: Deep Learning Structural Ensembles as Proxies for Protein Flexibility
doi: 10.64898/2026.05.16.725658
Figure Lengend Snippet: Ranksorted mean squared fluctuations (MSF) from all normal modes of cryo-EM structures and three deep learning structure prediction methods (AlphaFold3, AlphaFold2 and RosettaFold). A) Projections of the ranksorted MSF onto protein structures for 9yin. Blue-White-Red color palette is used for the projections, where blue indicates low flexibility and red indicates high flexibility. B) 2D comparison of the experimental and the computed MSF for 9yin. Black line (with squares) is for the experimental data, blue line (with circles) is for AlphaFold3, orange line (with inverse triangles) is for AlphaFold2 and green line (with stars) is for RosettaFold. C) Cosine similarity of the experimental and the computed MSF for 82 proteins in the cryo-EM dataset. AlphaFold3 bars are blue, AlphaFold2 bars are orange and RosettaFold bars are green. Averages of the cosine similarities over the entire dataset are also provided as horizontal lines for AlphaFold3 (blue dot-dashed line), AlphaFold2 (orange dashed line) and RosettaFold (green dotted line).
Article Snippet: An interactive 2D plot of the MSF is generated with Plotly Python library .
Techniques: Cryo-EM Sample Prep, Comparison