Download models Download trajectory View trajectory
Status: Done started: 2015-Jul-23 05:37:56 UTC
Project Name3rzeA1
SequenceMPLVVVLSTICLVTVGLNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLLMSKWSLGRPLCLFWLSMD YVASTASIFSVFILCIDRYRSVQQPLRYLKYRTKTRASATILGAWFLSFLWVIPILGWNHRREDKCETDFYDVTWFKVMT AIINFYLPTLLMLWFYAKIYKAVRQHYLHMNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKNCCNEHLHMFTIWLG YINSTLNPLIYPLCNENFKKTFKRILHI
Secondary structure

CCHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCHHHHHHHHHHHHHHHHCCCCCCHHHHHHHCCCCCCCCCCCHHHHHHH HHHHHHHHHHHHHHHHCCEEEEECCCCCCCCCCHHHHHHHHHHHCHHHHHHHHHHHCCCCCCCCCCCCCCCCCCHHHHHH HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCHHHHHHHHHHHHHEEECCCCCEEEHHHCCCCCCCCCHHHHHHHHHHH HHHCCCCHHHHHCCCHHHHHHHHHHHCC

User provided constraintsYes
Simulation (CABS) temperature3.5 - 1.0
Estimated finish time2015-Jul-23 19:32 UTC

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

Cluster #123456789
Cluster density44.919.018.918.614.410.49.85.13.9
Cluster size623839793138371917
Average cluster RMSD1.42.02.14.22.23.63.83.74.3

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 3.56 4.16 4.51 2.80 4.57 4.63 5.10 6.26
2 3.56 0.00 3.92 4.39 4.23 3.84 3.81 5.18 5.70
3 4.16 3.92 0.00 4.77 3.92 5.46 4.76 4.19 5.91
4 4.51 4.39 4.77 0.00 4.57 4.87 3.35 4.91 6.00
5 2.80 4.23 3.92 4.57 0.00 5.10 5.16 4.68 6.57
6 4.57 3.84 5.46 4.87 5.10 0.00 4.43 5.87 7.02
7 4.63 3.81 4.76 3.35 5.16 4.43 0.00 5.14 5.79
8 5.10 5.18 4.19 4.91 4.68 5.87 5.14 0.00 6.32
9 6.26 5.70 5.91 6.00 6.57 7.02 5.79 6.32 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 0.61 0.60 0.54 0.70 0.52 0.52 0.47 0.43
2 0.61 1.00 0.57 0.49 0.54 0.56 0.56 0.43 0.44
3 0.60 0.57 1.00 0.53 0.56 0.50 0.55 0.50 0.44
4 0.54 0.49 0.53 1.00 0.54 0.48 0.59 0.50 0.45
5 0.70 0.54 0.56 0.54 1.00 0.49 0.47 0.49 0.40
6 0.52 0.56 0.50 0.48 0.49 1.00 0.54 0.45 0.41
7 0.52 0.56 0.55 0.59 0.47 0.54 1.00 0.49 0.46
8 0.47 0.43 0.50 0.50 0.49 0.45 0.49 1.00 0.39
9 0.43 0.44 0.44 0.45 0.40 0.41 0.46 0.39 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