Status: Done started: 2017-Dec-02 12:43:07 UTC
| Project Name | 1 |
| Sequence | MAPQSITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGATFQVEVPGSQHIDSQKKAIERMKDTLRITYLTE TKIDKLCVWNNKTPNSIAAISMENYAPQDPYAPQDPIVVTGDKAVNIYTSSQTGSIIVKLLPNLPKDKEACAKAPLDAYN RTLTTLLTPLGDSIRRIQESVTTSGGGRQGRLIGAIIGGVALGVATPAQITAAAALIQAKQNAANILRLKESIAATNEAV HEVTDGLSQLAVAVGKMYAPQDPYAPQDPVANYPGVGGGSFIDSRVWFSVYGGLKPNSPSDTVQEGKYVIYKRYNDTCPD EQDYQIRMAKSSYKPGRFGGSEKDELHHHHHH |
| Secondary structure | CCCHHHHHHHHHHCCCEEEEECCCCCHHCCCCCCCEEEEEEEEECCCEEEEECCCCCCCCHHHHHHHHHHCCEEEEEEEC CCCCCCCHHHHHHHHHHHCEECCCCCCCCCCCCCCCEEEECCEEEEEEECCCCCCEEEECCCCCCCCHHHHHCCCHHHHH HHHHHHCCCCHHHHHHHCCCEECCCCCCCCCEEEEEEEHHHHCCCCHHHHHHHHHHHHHHCCHHHHHHHHHHHHHHHHHH HHHHHHHHHHHHHHHCCCCCCCCCCCCCCCCCCCCCCCCCCCCCEEEEEEECCCCCCCCCCCCCCCEEEEEEECCCCCCC CCHHHHHHHHCCCCCCCCCCCCCCCCCCCCCC |
| Simulation (CABS) temperature | 2.0 - 1.0 |
| Estimated finish time | 2017-Dec-03 01:38 UTC |
Time evolution of structural features: CABS energy, End-to-end distance and Radius of gyration.
| Cluster # | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Cluster density | 33.0 | 26.0 | 10.5 | 5.9 | 4.8 | 4.1 | 3.4 | 1.5 |
| Cluster size | 104 | 104 | 62 | 22 | 25 | 21 | 15 | 7 |
| Average cluster RMSD | 3.2 | 4.0 | 5.9 | 3.8 | 5.3 | 5.1 | 4.5 | 4.8 |
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.
| # | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| 1 | 0.00 | 26.10 | 25.30 | 26.80 | 34.00 | 23.40 | 37.40 | 33.70 |
| 2 | 26.10 | 0.00 | 10.90 | 24.90 | 30.10 | 23.80 | 33.20 | 30.60 |
| 3 | 25.30 | 10.90 | 0.00 | 25.50 | 29.80 | 23.20 | 32.10 | 31.60 |
| 4 | 26.80 | 24.90 | 25.50 | 0.00 | 32.70 | 28.70 | 35.90 | 32.70 |
| 5 | 34.00 | 30.10 | 29.80 | 32.70 | 0.00 | 28.80 | 12.30 | 11.20 |
| 6 | 23.40 | 23.80 | 23.20 | 28.70 | 28.80 | 0.00 | 31.20 | 30.50 |
| 7 | 37.40 | 33.20 | 32.10 | 35.90 | 12.30 | 31.20 | 0.00 | 14.70 |
| 8 | 33.70 | 30.60 | 31.60 | 32.70 | 11.20 | 30.50 | 14.70 | 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.
| # | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| 1 | 1.00 | 0.13 | 0.12 | 0.12 | 0.12 | 0.11 | 0.12 | 0.10 |
| 2 | 0.13 | 1.00 | 0.39 | 0.12 | 0.13 | 0.11 | 0.12 | 0.11 |
| 3 | 0.12 | 0.39 | 1.00 | 0.10 | 0.10 | 0.09 | 0.09 | 0.09 |
| 4 | 0.12 | 0.12 | 0.10 | 1.00 | 0.10 | 0.10 | 0.11 | 0.10 |
| 5 | 0.12 | 0.13 | 0.10 | 0.10 | 1.00 | 0.11 | 0.32 | 0.35 |
| 6 | 0.11 | 0.11 | 0.09 | 0.10 | 0.11 | 1.00 | 0.10 | 0.11 |
| 7 | 0.12 | 0.12 | 0.09 | 0.11 | 0.32 | 0.10 | 1.00 | 0.29 |
| 8 | 0.10 | 0.11 | 0.09 | 0.10 | 0.35 | 0.11 | 0.29 | 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.