Fig. 3: Machine learning aided classification of surface SAS-6 self-assembly. | Nature Communications

Fig. 3: Machine learning aided classification of surface SAS-6 self-assembly.

From: Kinetic and structural roles for the surface in guiding SAS-6 self-assembly to direct centriole architecture

Fig. 3

a Distinct oligomeric states recognized by machine learning aided classification in PORT-HS-AFM data. b Representative examples of existing classes. In total there are 10 open and 4 closed conformations, some of which are present in the image shown in panel a. c Number of assemblies of each oligomeric species over time (thin lines, colors as in panel B) in one PORT-HS-AFM experiment. d Average oligomerization degree (\(\bar{{{{{{\boldsymbol{N}}}}}}}\)) as a function of the total number of homodimers on the mica surface for 5 experiments (see Supplementary Fig. 3 for individual time traces). Note similar slopes, reflecting analogous oligomerization kinetics.

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