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Status: Done started: 2017-Jan-13 12:46:27 UTC
Project Namen298
SequenceDSGCVVSWKNKELKCGSGIFITDNVHTWTEQYKFQPESPSKLASAIQKAHEEGICGIRSVTRLENLMWKQITPELNHILS ENEVKLTIMTGDIKGIMQAGKRSLRPQPTELKYSWKTWGKAKMLSTESHNQTFLIDGPETAECPNTNRAWNSLEVEDYGF GVFTTNIWLKLREKQDVFCDSKLMSAAIKDNRAVHADMGYWIESALNGTWKMEKASFIEVKSCHWPKSHTLWSNGVLESE MIIPKNFAGPVSQHNYRPGYHTQTAGPWHLGKLEMDFDFCEGTTVVVTEDCGNRGPSLRTTTASGKLITEWCCRSCTLPP LRYRGEDGCWYGMEIRPLKEKEENLVNSLVTA
Secondary structure

CCCCEEEEECCCEECCCCEEECCCCCCCCCCCCCCCCCHHHHHHHHHHHHHCCCCCCEEHHHHHHHHHHHHCHHHHCCCC CCEEEEEEEECCHHHHHHHCCCCCCCCCCCCEEEHHCCCCCCCCCCCCCCCEEEEECCCCCCCCCCCCCCCCEEEEEEEE EEEEEEEEEEECCCCCCCCCHHHHHHHHHCCCCCCCCCHHHHHHHHCCCCCCCEEEEEEEECCCCCCCCCCCCCCCCEEC CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCEEEEEECCCCCCCCCCEEEECCCCCHHHHHCCCCCCCC CEECCCCCEEECCCCCCCCHHHHHHHHHHCCC

Simulation (CABS) temperature2.0 - 1.0
Estimated finish time2017-Jan-14 01:41 UTC

Provided templates.

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

Cluster #123456789
Cluster density43.433.732.318.58.17.14.02.3Inf
Cluster size94807134282319101
Average cluster RMSD2.22.42.21.83.53.24.84.40.0

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 4.32 5.06 10.40 5.03 9.62 8.74 11.50 12.70
2 4.32 0.00 4.05 9.47 2.98 10.10 6.53 9.72 11.70
3 5.06 4.05 0.00 10.50 4.57 10.60 7.93 9.67 12.10
4 10.40 9.47 10.50 0.00 8.58 5.31 4.30 5.15 8.83
5 5.03 2.98 4.57 8.58 0.00 9.54 5.93 8.85 11.80
6 9.62 10.10 10.60 5.31 9.54 0.00 6.82 7.50 7.97
7 8.74 6.53 7.93 4.30 5.93 6.82 0.00 4.97 8.97
8 11.50 9.72 9.67 5.15 8.85 7.50 4.97 0.00 9.78
9 12.70 11.70 12.10 8.83 11.80 7.97 8.97 9.78 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 0.81 0.78 0.77 0.79 0.78 0.78 0.76 0.79
2 0.81 1.00 0.79 0.78 0.79 0.78 0.80 0.77 0.78
3 0.78 0.79 1.00 0.77 0.80 0.78 0.78 0.78 0.82
4 0.77 0.78 0.77 1.00 0.81 0.76 0.77 0.79 0.81
5 0.79 0.79 0.80 0.81 1.00 0.79 0.79 0.78 0.80
6 0.78 0.78 0.78 0.76 0.79 1.00 0.79 0.77 0.78
7 0.78 0.80 0.78 0.77 0.79 0.79 1.00 0.79 0.80
8 0.76 0.77 0.78 0.79 0.78 0.77 0.79 1.00 0.80
9 0.79 0.78 0.82 0.81 0.80 0.78 0.80 0.80 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.


 

© Laboratory of Theory of Biopolymers 2013