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Status: Done started: 2018-Mar-14 05:39:46 UTC
Project Nameuga10
SequenceDQGCAVNFGKRELKCGDGIFVFRDSDDWLTKYSYYPEDPVKLASIIKASYEEGKCGLNSVDSLEHEMWRSRADEINAIFE ENEVDISVVVQDPKNIYQRGTHPFSRIRDGLQYGWKTWGKNLIFSPGRRNGSFIIDGKSRKECPFSNRVWNSFQIEEFGM GVFTTRVFMDAVFDYSVDCDGAILGAAVNGKKSAHGSPTFWMGSHEVNGTWMVHTLETLDYKECEWPLTHTIGTSVEESD MFMPRSIGGPVSSHNHIPGYKVQTNGPWMQVPLEVRREPCPGTSVVLDTGCDGRGKSTRSTTDSGKIIPEWCCRSCTMPP VSFHGSDGCWYPMEIRPMKTHESHLVRSWVAA
Secondary structure

CCCCCCCCCCCEEEECCCEEEEECHHHHHHHCCCCCCCHHHHHHHHHHHHHCCCCCCCCCCHHHHHHHHHHHHHHHHHHH HCCCEEEEEEECCCCCCCCCCCCCCCCCCCCCCCHHCCCCCEEECCCCCCCCEEEECCCCCCCCCCCCCCCCEEEEEECC EEEEEEEEEEEEECCCCCCCCCEEEEEECCCEECCCCCCEEEEEEECCCEEEEEEEEEECCCCCCCCCCCCCCCCCCCCC EEEEEECCCCCCCCCCCCCCCCCCCCCCCCCCEEEEEECCCCCEEEECCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC CEEECCCCCEECCCCCCCCCCCCCEEEEECCC

Simulation (CABS) temperature2.0 - 1.0
Estimated finish time2018-Mar-14 18:34 UTC

Provided templates.

Time evolution of structural features: CABS energy, End-to-end distance and Radius of gyration.

Cluster #123456789
Cluster density161.9107.593.780.838.432.129.622.420.6
Cluster size936557512522191513
Average cluster RMSD0.60.60.60.60.70.70.60.70.6

Clustering of protein models is the task of separating a set of protein models (here a protein dynamics trajectory) into groups (called clusters). The clustering is done in such a way that models are more similar in the same group to each other (here in the sense of RMSD measure), than those in other groups (clusters). CABSfold utilizes classical K-means clustering method.
After clustering is done, each cluster representative is chosen (always the model which average dissimilarity to all models in a cluster is minimal). Predicted protein models, presented in the Predicted structures tab, are each cluster representatives (the clusters and the corresponding models are marked by the same numbers, e.g. Model 1 represents Cluster 1).
The clusters are numbered/ranked according to cluster density values, from the most dense (numbered as a first) to the least dense one.

#123456789
1 0.00 0.88 1.06 1.08 1.18 2.49 1.34 1.50 1.72
2 0.88 0.00 1.01 1.01 1.22 2.67 1.56 1.43 1.97
3 1.06 1.01 0.00 1.16 1.39 2.66 1.65 1.50 1.60
4 1.08 1.01 1.16 0.00 1.21 2.51 1.33 1.53 1.93
5 1.18 1.22 1.39 1.21 0.00 2.62 1.53 1.80 1.71
6 2.49 2.67 2.66 2.51 2.62 0.00 2.53 2.70 2.94
7 1.34 1.56 1.65 1.33 1.53 2.53 0.00 1.87 1.96
8 1.50 1.43 1.50 1.53 1.80 2.70 1.87 0.00 2.34
9 1.72 1.97 1.60 1.93 1.71 2.94 1.96 2.34 0.00

The table contains RMSD values (calculated on the Cα atoms) between the predicted models.
Read more about the root-mean-square deviation (RMSD) measure.

#123456789
1 1.00 0.96 0.94 0.93 0.92 0.93 0.89 0.89 0.92
2 0.96 1.00 0.95 0.95 0.92 0.91 0.90 0.88 0.90
3 0.94 0.95 1.00 0.94 0.88 0.92 0.89 0.91 0.91
4 0.93 0.95 0.94 1.00 0.92 0.92 0.91 0.91 0.90
5 0.92 0.92 0.88 0.92 1.00 0.89 0.88 0.83 0.88
6 0.93 0.91 0.92 0.92 0.89 1.00 0.87 0.90 0.91
7 0.89 0.90 0.89 0.91 0.88 0.87 1.00 0.86 0.86
8 0.89 0.88 0.91 0.91 0.83 0.90 0.86 1.00 0.86
9 0.92 0.90 0.91 0.90 0.88 0.91 0.86 0.86 1.00

The table contains GDT_TS values (calculated on the Cα atoms) between the predicted models.
Read more about the global distance test (GDT, also written as GDT_TS to represent "total score") measure.


 

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