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Status: Done started: 2015-Jul-22 18:18:09 UTC
Project Name4ej4A1
SequenceRSASSLALAIAITALYSAVCAVGLLGNVLVMFGIVRYTKLKTATNIYIFNLALADALATSTLPFQSAKYLMETWPFGELL CKAVLSIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPAKAKLINICIWVLASGVGVPIMVMAVTQPRDGAVVCML QFPSPSWYWDTVTKICVFLFAFVVPILIITVCYGLMLLRLRSVYEKDRSLRRITRMVLVVVGAFVVCWAPIHIFVIVWTL VDINRRDPLVVAALHLCIALGYANSSLNPVLYAFLDENFKRC
Secondary structure

CCCCCCCHHHHHHHHHHHHHHHHHCCHHHHHHHHHCCCCCCCHHHHHHHHHHHHHHHHHHCCCHHHHHHHHCCCCCCCHH HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCCCCCCCCCCHHHHHHHHHHHHHHHHHHCCCCEEEEEEECCCCEEEECC CCCCCCHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHCHHHHCCCCCCCHHHHHHHHHHHHHCCCCHHHHHHHHHH HCCCCCCHHHHHHHHHHHHHHHHCCCCCCHHHHCCCCCCCCC

User provided constraintsYes
Simulation (CABS) temperature3.5 - 1.0
Estimated finish time2015-Jul-23 08:13 UTC

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

Cluster #123456789
Cluster density45.429.517.715.18.05.03.41.81.3
Cluster size98484959452124115
Average cluster RMSD2.21.62.83.95.64.27.06.13.7

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 5.69 5.26 5.32 6.00 6.34 9.61 13.40 14.10
2 5.69 0.00 3.19 4.43 6.02 5.30 9.16 13.80 13.10
3 5.26 3.19 0.00 4.12 5.96 5.14 9.17 13.90 13.00
4 5.32 4.43 4.12 0.00 5.51 5.90 9.42 13.40 13.80
5 6.00 6.02 5.96 5.51 0.00 6.64 7.11 13.80 13.80
6 6.34 5.30 5.14 5.90 6.64 0.00 9.81 13.80 14.00
7 9.61 9.16 9.17 9.42 7.11 9.81 0.00 12.50 14.40
8 13.40 13.80 13.90 13.40 13.80 13.80 12.50 0.00 20.10
9 14.10 13.10 13.00 13.80 13.80 14.00 14.40 20.10 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 0.47 0.50 0.54 0.51 0.43 0.35 0.33 0.30
2 0.47 1.00 0.64 0.50 0.45 0.44 0.36 0.29 0.36
3 0.50 0.64 1.00 0.55 0.49 0.45 0.37 0.29 0.34
4 0.54 0.50 0.55 1.00 0.52 0.39 0.35 0.34 0.31
5 0.51 0.45 0.49 0.52 1.00 0.36 0.39 0.29 0.28
6 0.43 0.44 0.45 0.39 0.36 1.00 0.30 0.27 0.31
7 0.35 0.36 0.37 0.35 0.39 0.30 1.00 0.29 0.30
8 0.33 0.29 0.29 0.34 0.29 0.27 0.29 1.00 0.22
9 0.30 0.36 0.34 0.31 0.28 0.31 0.30 0.22 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